Consumer Engagement through Digital Transformation

Transcription

HEALTHCARE INSIGHTS
Consumer Engagement through Digital Transformation
Nov 2014
In This Edition
Creating a Consistent and Connected
Experience Across the Consumer Lifecycle..........................................................................................05
Look Before You Leap: What Health Plans
Must Do Before Diving into Digital ...........................................................................................................13
Clear as Crystal: Refocusing Healthcare Consumer
Transparency from Information Availability to Usability ..............................................................21
Big Data Analytics in Healthcare –
Taming the Elephant in the Room.............................................................................................................33
Using Analytics for Insurance Fraud Detection3 Innovative
Methods and a 10-Step Approach to Kick Start Your Initiative.................................................41
Creating a Consistent and Connected Experience
Across the Consumer Lifecycle
Summary
Infosys Public Services conducted a panel discussion on creating a consistent and connected experience for
consumers at a flagship healthcare industry forum in June 2014 with leaders from Aetna and HealthSparq.
The session produced practical perspectives and insights on understanding consumers and their needs,
motivation to engage with health insurance plans, and what it takes for the plans to create personalized
and connected experiences that consumers want AND expect.
The US health insurance business model is transforming in fundamental ways and is driven by
consumerization. Healthcare consumer expectations are now set by their experience with retail and other
consumer industries. Multiple studies have shown that consumers are rapidly embracing digital channels
for healthcare – 39% of consumers will visit a website to research a new health insurance plan, 76% will sign up
for a mobile app to track health goals, one-third use social media and online forums to address health concerns,
and two-thirds who have good experiences stay with the insurer. The onus is thus on health insurance plans to
close the gap on engaging with consumers.
Three essential principles drive consumer engagement that leads to accountability and sustainable behavior change, and thus improves
health outcomes and lowers costs:
Consumer centric
Outcome oriented
Omni channel
Engage consumers with a 360o view
Connect consumer touch-points to
Go from engaging with a consumer
of all their interactions with a health
fulfill their immediate need and also
via multiple channels to connecting
insurance plan
move them along towards ultimate
those interactions to provide a better
health improvement goals
experience as “one brand”
Digital Transformation is key to consumer engagement:
The shift from group (employer) to individual (consumer) market is an opportunity for health plans to gain new members to drive growth.
But health insurance plans need to compete to acquire and retain consumers, inform and service them, empower and engage with
them to improve health outcomes and lower costs. Digital transformation is key to the successful transition from an employer-centric to a
consumer-centric model.
There has been a paradigm shift from one-to-many to one-to-one communication and engagement, delivering a consistent message and
connected experience to consumers across channels – digital and traditional, direct and indirect. Health insurance plans that deliver better
digital consumer experience will gain a competitive edge.
Web
Personalized Communications
Brokers
Unified Insights
Call Center
Acq
u
Mobile
In
vo
e
r
Fast Fullfillment
Retain
Mail
Inform + S
er
3600 Service
+
ire
ce
vi
lve + E m p o w
Self Service
Targeted Offers
Community
Groups
Decision Support / Transparency
Social Media
Providers
On-demand Communities
External Document © 2015 Infosys Limited
Micro Segmentation
Exchanges
Panelists
Aneesh Kumar
Tamara Khan
Eric Paternoster
Head-Consumer Engagement Strategy,
VP of Products,
President & Chief Executive Officer,
Aetna
HealthSparq
Infosys Public Services
In his role as the head of consumer
Tamara leads product, user experience,
As chief executive of the Infosys
products group, Aneesh’s team focuses
and informatics at HealthSparq. She has
subsidiary, Eric oversees strategy
on developing solutions for individuals
over a decade of product management
and execution for profitable growth.
who have choice in their healthcare
experience in consumer web, health,
He advises CxOs in healthcare and
and healthcare financing. Rather than
gaming, electronics, and AI. Tamara
government on technology and
tweaking their group-oriented solutions
specializes in creating intuitive user
operations. Eric has over 30 years of
to become prettier and consumer
experiences that have driven large
experience with firms in healthcare,
friendly, the team develops solutions
scale growth at brands such as Practice
consulting, and business technology.
that holds the consumer as the center
Fusion, Kiva, and Nickelodeon. She
Eric’s team combines healthcare
and then builds the business back.
plays many roles – that of a designer,
expertise with insights and practices
behavioral economist, and technologist.
from industries such as retail and
She drives positive behavior change
banking to bring innovative solutions for
online and brings a data driven
consumer engagement and other big
approach to creating products that
challenges in healthcare.
users love.
Moderated By: Eric Paternoster
Panel Discussion
Eric: What are the organization, processes,
and technology implications of delivering
a consistent, connected, personalized
consumer experience?
What health plans need mostly doesn’t
have to be invented but can be adapted
from retail, telecom, banking, and other
B2C industries. There is opportunity for
technology to support transforming every
aspect of understanding and engaging with
consumers so that the focus can shift to
health and costs.
Aneesh: Organizational challenges are
really the main challenges that we face and
anybody at a big company can probably
empathize. Benefit managers, CFOs, and
government program administrators, are
our current stakeholders. They have not
only tolerated complexity but in fact have
asked for it in terms of organizationalspecific customization. They have a
terms of formulating some of our business
data onto it, so that an end-user can read it,
models. I’m also heartened by the fact that
understand the terms we are using, ensure
the people who are working in today’s
it’s appropriate for the location and so on.
group-specific model, are even more
keenly aware of some of the shackles that
the organization has put on itself. So I find
that the relationship is very collaborative.
The key message for health plans is that
some separation of this new group focused
on consumers from a group that is focused
on employers and government program
administrators is necessary if you want to
address this new market in a simple way.
Aneesh: Let me add a little bit to the
consistency point that Tamara brought
up. Product design skills in our industry
are lacking. You need somebody like an
Amazon of healthcare to define products
as human constructs. Managed care
companies and employers have not done
a good job defining healthcare products.
This is a unique industry where MCOs
have the opportunity to connect with
Tamara: The need of the hour is to
consumers from the time they can make a
rationalize information. Information is
purchase--let’s say when they become an
in dispersed locations and customized
adult--to the time they die, and we haven’t
to employer groups - to turn this data
really taken advantage of that. But we can,
into a solution is a cumbersome process.
once we think of the product differently.
One of the tenets of a great personalized
Audience Question: I think health plans
experience is consistency. So, when I use a
health plan site this year to buy Plan A and
next year to buy Plan B, I shouldn’t have a
wildly different experience. I shouldn’t have
to learn again from start to finish.
are missing a huge opportunity with
separating focus for individuals and groups. I
understand, 10 years from now it will probably
be all consumers, but if you look at the next
2-3 years, I think it’s an opportunity to provide
As we innovate with new types of plan
consumer-like experiences through the group
attitude toward consumers who, of course,
designs and preference-based pricing,
engagement -- may be through private
“cannot manage their own health.” It is
the complexity adds more layers to the
exchanges that help get that brand-stickiness
difficult to move away from the capabilities
consumer experience to a point where
vs. wait for this evolution to occur.
and payment models that have made us
it becomes incredibly challenging.
Aneesh: Consumer Engagement
successful towards a new model where
This becomes the real operational,
is a journey and I have frequently
the consumer is empowered to make
organizational impact: re-thinking how we
characterized private exchanges as
choices that affect their health. Today, we
design products and continue to achieve
graceful disengagement, in the sense
don’t know consumer segmentation, we
that personalization without necessarily
that an employer can pick multiple points
don’t know how to make money selling
sacrificing member experience, and
along their entire journey from today’s
to consumers, because we don’t have
thinking about new ways to gather and
paternalistic model to the future, where
the track record. This goes a little bit into
record information so that it continues to
choice, responsibility, and spend, move
Christensen’s innovator dilemma where
serve consumers in more ways beyond
completely to the consumer. The employer
new business opportunities frequently are
what is intended now. Now we have a new
will then be a facilitator. But this journey
less profitable than today’s business, and
goal, which is the purpose of empowering
is not going to happen overnight or over
almost always require new skills, but will
consumers with enough information to
one year.
eventually be profitable.
make decisions, just as in a retail store.
The approach that we have taken is
The big practical impact as we work with
exchanges because we can, for the first time,
identifying a different set of people to
organizations trying to enable better
provide an experience to the consumer where
focus on the new businesses and with that
consumer experience is figuring out how
they have greater choice, where they have
we’ve seen quite a bit of success, at least in
to take information and layer the meta-
the notion of trade-off between premiums
conservative approach, and a paternalistic
So an employer can say, “Let’s get to private
and co-pays and network sizes and they
experience for consumers with their wellness
that healthcare organizations can take
get more used to taking care of their own
products through rewards and other
advantage of. There is a really interesting
health and their own healthcare spending.”
game mechanisms. This is driving a more
opportunity to partner with EHR vendors,
Over time, I do believe that benefit design
sustainable behavior change, which is key
because those that are mining data, know
will move away from employers, because
to drive down healthcare costs. A health
exactly what interventions and actions
they do not really belong in the healthcare
solutions company we are working with is
lead to real measurable outcomes (EHR
business. But that is over time, we cannot
integrating public social networks onto their
deals with biometric data from all labs).
tie our success only to that event. It has
web portals to bring more comprehensive
to be tied to the entire transformation of
health data or condition information and
increasing consumer choice.
resources to consumers. We see this as a
Eric: Tamara, you have great experience in
growing trend.
the EHR world before your current role. I’d like
Tamara: EHRs have a lot of valuable
For example, women who take prenatal
information. However, the information
vitamin of a certain type have C-sections –
could be across countries, in different
the kind of things that might inspire new
formats, and not necessarily be in usable
plan designs that would be cost-effective
For example, Avivia Health from Kaiser
form. There is a lot of interest at the federal
and offer pro-healthy choices. These
Permanente has built a gamification
level and from EHR vendors to turn this
discoveries could then be fed into health
platform so they can provide an interactive
information into actionable insights
plan designs.
to ask you how health plans can leverage
digital capabilities through tools like that to
start providing this connected experience?
We can start to think of the EHR strategy
and interweaving and doing research
around how claims data correlates to labs
data from a specific population set, which
would help us find interesting insights.
plans either do that or find new ways of
getting people to refer via some other
mechanism, but I can’t imagine displacing
the provider in that funnel. So, the strategic
value is in figuring out how health plans
make providers “like” them – but not
necessarily by acquiescing to whatever
prices they want, because health plans
still need to deliver value to the end-user. I
would propose that there is a way to make
providers happier by getting into their cost
structure in a different way.
Aneesh: The Provider is the central hub
of the healthcare system - they are the
sellers and the consumers are the buyers.
One of the systemic problems in the
industry is that there are too many entities
in the middle. Part of where Health plans
Eric: To build a great consumer brand you
Therefore, the relationship between
need to have omni-channel capabilities that
the consumer and the health plan and
all fit together. I’d be interested in each of
consumer and the physician, both of
you talking about the implications to the
which have been highly asymmetrical
role of “indirect channels” such as providers,
relationships for two or three generations,
brokers and community groups in ensuring a
are becoming equal conversations.
consistent experience to consumers?
risk transfer at say a product level, and
Being an honest, plain-speaking party
let a consumer shop, the consumer
Aneesh: I have two thoughts. The first is
in the conversation within all these
would become knowledgeable on where
that a direct connection with consumers
indirect channels is the underlying
the money is going. That is absolutely
is critical. The panelists before us showed
premise for success.
necessary for changing the dynamics of
what is possible once you develop that
Tamara: I will answer it a bit differently,
direct connection. The previous examples
based on which ones I see as more
did not even begin in the healthcare
strategic. Engaging consumers is only one
space but now we have an offering that
step of the converging funnel (prospecting
is truly valuable. We need to develop that
to converting into members). It is right
Audience Question: Consumers, especially
capability as an industry and we are really
there in the middle. Acquisition of our
those with poly-chronic conditions who
at the beginning of the maturity curve. At
new potential customers through viable
drive most of the healthcare expenses, are
the same time, Eric, to your point, there
mechanisms like brokers, providers, etc.,
they really empowered to make choices that
will be social media groups, patients-
is all the more interesting to mention.
benefit them the most by taking care of
like-me type of groups that will not be
If you look at the system as is today,
completely controlled by us, and neither
providers are a terrible advocate for
Aneesh: My belief and observation are,
should we try to control them. Then the
insurance plans – “they don’t pay me; they
yes. It is a cultural shift, it is not going
thing that a managed care organization
don’t reimburse my claims”. Changing that
to happen in the next two weeks, and
should hope for, in my opinion, is to be a
dynamic and relationship is really key to
it is going to be a rocky road. But I
part of the conversation.
ensuring that the funnel is healthy. Health
fundamentally believe that consumers are
could have done a better job is to pay the
Providers in a way that made economic
sense and then transfer some appropriate
risk to Providers. The moment you set
the system such that there is meaningful
the relationship. And when that does, I
absolutely believe that consumers will be
more satisfied, and the cost of the entire
healthcare system will go down.
their health?
capable of taking care of their health. Do
likely. If you look at search trends today
opportunity pass.” I would challenge
they need a support system? Yes, all of us
and see what people look for online, it is
and say it is a huge opportunity for
do, in everything we do. And companies
by and large information about healthcare.
everyone. If we combine our knowledge
such as ours are well-positioned to create
Curiosity is enormous. Companies like
of the system, the payers, other players,
the right support system.
WeightWatchers are catering to that
and how to navigate various stakeholders
interest; BestBuy decided to open floor
and partner with those who are engaging
space in their stores to support this desire
the consumer, then we would have a
for health tracking and applications. There’s
strong offering that changes the health
a huge appetite in America to manage
dynamic quite dramatically.
If you look at the United Kingdom, their
macro-health outcomes are better than
ours, their total healthcare cost is 40% of
ours, there’s no employer in the middle,
and they have the same mix of millennials
and boomers, pretty much as we do. So I
do believe that that is possible, because
other countries have shown that to be true.
conditions like obesity and diabetes and
just general health concerns. So, the
question is “Are we going to be
Eric: Thank you for all your questions,
insights, and engaging discussion.
participants in that? Are we going
to partner with the people who are
Tamara: I would take a step further to not
innovating? Or, are we going to accept
only say that it is possible but also very
that it’s not our strong suit and let that
In closing, building a consumer-centric business model and becoming a consumer brand is a multi-year journey. So, bringing
in proven practices from other industries and pre-built solutions can make a big difference in accelerating the journey. As we
discussed, there are numerous capabilities – particularly on the digital front – but health plans have limited resources and
competing priorities. Value realization frameworks should be used upfront to prioritize areas to focus, investments needed, and
roadmap to execute. In healthcare, as in other industries, strong business ownership from the health plan to drive change is crucial
to build momentum and see these initiatives through.
Look Before You Leap: What Health Plans Must Do
Before Diving into Digital
Abstract
Healthcare organizations are looking to leverage technology to improve consumer engagement and
experience. Modeling these initiatives on the retail industry, health plans and providers are hoping to
replicate their success at providing a seamless experience across channels – websites, social media, mobile
apps, call centers, email, and paper-based promotions.
And they’re putting their money where their mouth is. In their “Healthcare IT Payer Predictions For 2013”, a leading analyst
firm said that approximately 40 percent of U.S. healthcare payers planned to invest in establishing or renewing consumer
engagement initiatives, including web portals. This in fact topped the list of priorities for planned investment.
So, sometime last year, when Mayo Clinic announced that barely five percent of the several hundred thousand patients
registered on their web portal actually used it, it created quite a stir. This was after all the Patient Portal they were talking
about, a poster boy of consumer experience in healthcare, and an inspirational model for the industry!
Don’t digitize without direction
Mayo Clinic’s troubles are only
symptomatic of an ailment that is plaguing
most consumer engagement programs
in healthcare. The root cause? A lack of
directedness in the digitization drive
behind such programs.
Over the years, we have seen a number
of health plans rush headlong into digital
media in pursuit of consumer engagement,
or simply, a nimbler rival. In the process,
they fail to do the necessary due diligence
of questioning objective, mapping
consumer need, enumerating constraints,
quantifying end goals and identifying
efficient methodologies. (See Haste
Makes Waste) So it comes as a breath
of fresh air – even a jolt perhaps – when
the chief customer experience officer of
a pharmacy benefit manager challenges
even the engagement premise by candidly
admitting that she is yet to find someone
who wants to engage with a health plan.
This is not as outrageous as it seems – data
indicates that health apps are accessed
far less frequently than social media or
gaming apps. The Ruder Finn U.S. mHealth
apps citing reasons such as lack of need or
health plans that have acted in haste thus
preference for seeing a doctor.
far must now introspect carefully before
survey corroborates this statement with
The message is clear: Consumers are
this finding: Three in four respondents
yet to engage with healthcare in the
are reluctant to engage with healthcare
way they do with say, retail. Therefore,
investing further resources in digitization
to make sure all efforts are directed at
improving engagement.
Haste Makes Waste
A vast majority of commercial healthcare plans are present in digital channels like website, mobile, and social. Their success stories spur
much of the frenzy among health plans to replicate or outdo the competition. Unfortunately, this has led to a number of rash moves.
Without adequate thought going into them, these plays for digitization have yielded disappointing results.
Take for instance, the mobile apps launched by several BCBS health plans. Because the apps were developed without taking the existing
features of the portal into consideration, they ended up duplicating both effort and investment. BCBS health plans would have been
better off simply making the features on their portal mobile browser friendly.
Here’s another example. Until six months ago, many large health plans had different teams in charge of social, mobile, and website
initiatives, each spending time and money on doing the same things. Worse, the teams often pursued conflicting strategies. Health
plans realized the futility of this approach and have now switched to a holistic, unified strategy. This situation could have been avoided
altogether with a little foresight and planning.
Seek and ye shall find
health plans’ decision of how much and
where to invest in digital media.
generation of consumers, they would do
networks that could then perform the
elderly or the need to identify the best
An illustration might be useful here. Let’s
say a health plan is trying to decide which
services to digitize. They have several types
of services, categorized by complexity,
volume, touch-intensity, requirement and
so on. If the health plan’s objective is to
reduce the cost of delivering low touch
services – such as appointments, claim
status check, profile modification, etc. – the
logical step would be to divert requests
from the high cost call center to a low cost
channel like kiosk or website. However
before making any move, it is absolutely
critical that the health plan assess its likely
impact on consumers. For instance, if the
primary users are seniors with a likely
preference for an assisted channel, it would
be foolhardy to migrate the interactions to
self-service mode.
entry level plan among first time members,
On the other hand, if the health plan’s end
Mobile Web and Marketing Choice -
and so on. This should eventually drive
goal is to improve experience for a new
Email or SMS?)
But first, they must know what questions to
ask. It is not enough to do this intuitively.
What health plans need is a formal process,
backed by a framework, to arrive at a list of
critical questions that will have a bearing
on the investment decision. The framework
should be rigorous so as to provoke the
organization to think about the big picture
as well as see the small detail. It should
lead the decision makers to ask all the right
questions: What is our objective in investing?
Which digital channel should we use? Which
technology enjoys the highest adoption?
Where do we deploy resources first? How do
we measure results?
The answer to these questions must be
mapped against the needs of different
consumer segments, such as the need to
manage chronic conditions among the
well to invest in mobile apps and social
same functions as a traditional channel.
Besides core objective and consumer
need, health plans must also factor rate of
adoption into their decision. However, this
is not as easy as it sounds. For instance,
although healthcare portals are yet to hit
their stride, they are ideal for disseminating
information, and hence cannot be
dismissed. Social media is great for
engagement, but its performance metrics
are still unclear. And while mobile is an
obvious choice, the availability of different
technologies complicates the investment
decision. Clearly, there is no one size fits
all approach and each health plan must
decide based on what works best for them.
(See Technology Choice - App Versus
Technology Choice - App Versus Mobile Web
Health plans were late entrants to the mobility channel. But given that 104 million
people in the U.S. own smartphones and about 50 percent of smartphone users
download apps, a lot of health plans are giving serious thought to their mobility agenda.
They have two distinct options before them – mobility app or mobile browser. The choice
depends on a combination of investable resources, marketing strategy, RoI expectations,
and required functionality, such as shopping, searching, navigation, etc.
Both options have their advantages. A Mobile analytics firm’s study of heavy smartphone
user behavior indicates explosive growth in the ‘mobile addict’ segment – those who
launch at least six times the number of apps that an average user does everyday. The
number of mobile addicts has grown 123 percent between 2013 and 2014, whereas Super
Users have grown at less than half that pace, at 55 percent, and Regular Users (16 or fewer app launches daily1) a mere 23 percent. These
numbers make a strong argument in favor of the mobile app.
However, some mobility technology pundits have sounded the death knell for apps for a number of reasons, ranging from economic to
functional. For instance, they claim that it is not possible to sustain separate app programs for iPhones, BlackBerrys, Android phones
and other assorted devices on a limited budget in the long term.
Our experience with several BCBS health plans indeed shows that budget issues can derail a digital transformation program. In the face of
such constraints, it would be prudent for health plans to go the mobile browser route to ensure continuity in consumer experience.
Marketing Choice - Email or SMS?
A study by the Pew Research Center’s Internet and American Life Project says texting is still the reigning mobile phone activity. Email is
ranked 3rd. 81 percent of mobile users text, especially the younger adults, the college educated, and those with higher income. On the
other hand, only 50 percent of mobile users send or receive email. The profile of email users is similar to those who text.
Studies show that response rates – or more specifically read and respond rates – are higher for text messages. Yet most BCBS health
plans seem to prefer email. This reveals a need for optimizing communication based on channel preference to make it cost effective.
In other words, health plans should switch to text for soliciting business from their younger customers.
Compete wider and deeper
A health plan’s digital foray must not only encourage consumer engagement in healthcare, but also contend with competition from a
variety of healthcare organizations, all vying to engage with the same consumers on the same channels. Business and channel partners
like providers, physician groups, pharmacy benefit managers, minute clinics, and specialists intersect with healthcare consumers at various
touchpoints – digital and otherwise – throughtout the consumer life cycle. The graphic below depicts the different member touch points
currently in use across different channels.
Prospects /
Conversion
Enrollment/
Onboarding
Health
Management
Social
Collaboration via
Health Forums /
Health Boards
Social
Media
Member
Services
Post
Care
Provider Ratings &
Reviews
Engage members
through health-tips,
events, quizzes etc.
Patient Experiences
Low
High
Low
High
Low
High
High
Low
High
Low
ID Card
Manage Premium
Payment
Mobile
App
Wellness Applications Cost Transparency
Tools
Fitness Applications
Text based
solicitation
Claims Status
OOP Payments
Locate Provider
Schedule
Appointments
ID Card
Portal
Rx Reminders
Welcome Kit
Wellness Tips &
Trackers
Enrollment
Clarifications
Call
Center
Mail
Electronic Enrollment
via Insurance
Exchange
Fitness Tips &
Trackers
Mail campaigns
Cost to Implement & Maintain
Cost Transparency
Tools
Locate Provider
Claims Status
Bill Notification
OOP Payments
Schedule
Appointments
Prescription refill
Locate Provider
Appointments
Benefits
Care Coordination
Manage Premium
Payment
Wellness Tips
Paper based
Enrollment
Manage Premium
Payment
Wellness visit
alerts & reminders,
Prescription re-fill
reminders etc.
Impact on Consumer Engagement
Notice the number of white spaces of untapped opportunity. Health plans can stand out among the healthcare crowd by leveraging digital
channels like web, mobile, and social media to enter these spaces and garner first mover advantage.
The following graphic depicts a host of additional possibilities for engagement at each touchpoint.
Prospects /
Conversion
Enrollment/
Onboarding
Health
Management
Member
Services
Post
Care
Provider Ratings &
Reviews
Insurance Education
Social
Media
Social Collaboration via
Health Forums / Health
Boards
Payer Assisted
Enrollment Through
online chats, posts
Engage members
through health-tips,
events, quizzes etc.
Social
Collaboration via
Health Forums /
Health Boards
Anonymous Chats with
Members on forums /
boards
Patient Experiences
Provider / Specialist
Engagement during
Rehabilitation
Low
High
Low
High
Low
High
High
Low
High
Low
ID Card
Text based
solicitation
Mobile
App
Plan recommendation
Personalized content
On Mobile applications
to assist Enrollment
Insta-Chat capability
with Call Centre, other
members
Manage Premium
Payment
Wellness Applications
Fitness Applications
Cost Transparency
Tools
Locate Provider
Schedule
Appointments
Claims Status
OOP Payments
Telemedicine and
Remote Patient
Monitoring
Telemedicine
Health and wellness
trackers
ID Card
Plan recommendation
Portal
Call
Center
Mail
Dedicated member
portal
Health forums
Electronic Enrollment
via Insurance
Exchange
Rx Reminders
Welcome Kit
Wellness Tips &
Trackers
Decision support Systems Enrollment
for purchasing plan
Clarifications
Manage Member
Enrollment to Wellness,
Attrition
Care Management
Programs
Mail campaigns
Paper based
Enrollment
Cost to Implement & Maintain
Fitness Tips &
Trackers
Manage Premium
Payment
Cost Transparency
Tools
Locate Provider
Bill Notification
OOP Payments
Schedule
Appointments
Prescription refill
Locate Provider
Appointments
Benefits
Care Coordination
Manage Premium
Payment
Wellness Tips
Claims Status
Medication therapy
management
Care coordination b/w
different entities
Wellness visit
alerts & reminders,
Prescription re-fill
reminders etc.
Impact on Consumer Engagement
However, a big challenge is that most health plans do not have the technological capability to transform the above possibilities into reality.
It is here that they must seek the services of a specialist.
Choose wisely
It is critical that health plans choose the
right technology partner to help with their
digitization strategy. While most system
integrators have basic system integration
capability, only a few have consulting
acumen or transformation expertise, skills
that are vital to the success of the program.
Health plans must ensure that they take
on a partner who can contribute at every
stage, from conceptualization of strategy
to implementation of technology.
The partners approach, including overall
strategy, tools, methodologies, and
frameworks, must figure among the top
selection criteria. Ideally, they should bring
the following to the table:
• A roadmap to the right path, created using
prior domain experience and a framework
for assessing and prioritizing areas of focus.
The system integrator should be able to
envision the impact of current and future
digital capabilities on the health plan’s
core processes and systems, and factor
this into their recommended strategy.
• Ready solutions, frameworks, and
accelerators in the form of mobile use
cases or service dashboards to fast-track
implementation. Where they lack
in-house capability, the system
integrator should be able to fill the gaps
with offerings from alliance partners.
They must have a proven approach for
program and change management.
• Predictable, low risk implementation,
which assures value by leveraging
best practices in digital in the areas
of user experience, mobility, social
media, analytics and so on. The
partner must assure integration with
business processes and internal as
well as external systems, and set up
sufficient business rules and decision
management controls to enable the
health plan’s consumers to interact with
them seamlessly on all channels.
• Last but not least, demonstrated ability
to measure and monitor the performance
of the digital channels with the help of
sophisticated analytic tools and metrics of
consumer engagement.
Go well
Healthcare organizations are making rapid investments in digital media with a view to attracting consumers. However, mobile apps, and
indeed all other digitized offerings from health plans lack foresight and planning. In their eagerness to stay on top of the digital trend or
keep up with their competitors, health plans have committed vast resources without stopping to ask important questions – such as what
they hope to achieve, what their consumers need, and whether the twain will meet. It is high time that health plans took a more considered
approach to digital, starting with introspection, then finding new ways of exploiting different channels, and finally, identifying the right
technology partner to see the strategy through.
About the authors
Anand Madhavan
Senior Practice Lead, Infosys Public Services
Anand Madhavan is a Senior Practice Lead with Infosys Public Services responsible for analytics in healthcare.
He has a decadelong experience in helping organizations navigate strategic business problems using analytics and
driving tangible business impact.
He can be reached at Anand_Madhavan@infosys.com
Muthuselvan Nallamuthu
Associate Manager, Client Services Group, Infosys Public Services
Muthuselvan is an Associate Manager in the Client Services Group responsible for managing key healthcare client
relationships in the US. He has wide experience in the healthcare industry and has helped in solving customers’
business problems by bringing in best of solutions and capabilities from healthcare and other industry verticals.
He has special interest in customer acquisition and retention, leveraging consumers’ social behavior.
He can be reached at Muthuselvan_N@infosys.com
References
1
U.S. Healthcare Payer IT 2013 Top 10 Predictions - Janice W. Young, Lynne Dunbrack, Scott Lundstrom
2
Ruder Finn US mHealth Report
3http://www.comscore.com/Insights/Press-Releases/2012/4/comScore-Reports-February-2012-U.S.-Mobile-Subscriber-Market-Share
4
The Rise of the Mobile Addict - April 22, 2014; Simon Khalaf – Flurry Analytics
5
Pew Research Center’s Internet & American Life Project, April 17- May 19, 2013 Tracking Survey
6http://www.fiercehealthpayer.com/story/consumer-engagement-do-insurers-have-it-wrong/2014-06-16
7http://www.fiercemobilehealthcare.com/story/more-43000-mhealth-apps-have-limited-use-functionality-and-evidence/2013-10-30
Clear as Crystal: Refocusing Healthcare Consumer Transparency from
Information Availability to Usability
Research shows that consumers are better
Transparency Laws reveals the startling
A small Initiative – Price
Transparency Program for
MRIs launched between 2010
and 2012 by several Blue
Cross and Blue Shield health
plans has yielded benefits:
fact that 90% of states fail in providing
Among Consumers: It has led to
Healthcare Price Transparency. Against this
more members using lower-priced
backdrop, it is encouraging to see signs
providers
advocates of a brand if the association
is built on transparency and trust. Yet,
the healthcare industry has traditionally
lagged in sharing information, both
clinical and financial, with consumers.
The 2014 Report Card on State Price
of positive change both in the provision
of information by the industry and
acceptance by consumers.
A few of the several initiatives launched by
government agencies along with the payer
and provider community to strengthen
the state of healthcare transparency, are
the Health Benefit Marketplace (HBM) and
the All-Payer Claims Databases (APCDs).
The fundamental premise of both is the
provision of open, easily comparable,
Among Providers: It has resulted in
modest charge reductions by highpriced providers
Overall Price-Based Selective
Usage and Cost Reduction
observed in the intervention
of consumers purchasing High Deductible
Health Plans and accommodating greater
out-of-pocket expenses. Consequently,
•
transparency, choices, and control.
per test
•
expectations and consequently, the
dimensions of healthcare transparency?
• Plan – Where should health plans
invest to meet consumers’ expectations
of transparent information, namely, the
timeliness, usability, and convenience of
data shared?
• Implement – How should health plans
evaluate and prioritize the conflicting
opportunity areas?
•
“Currently, consumers most often do
is charging them or their insurance
Price variation between hospital and
non-hospital facilities narrowed by
30% after prices were posted
company for a given procedure, like a
knee replacement, or how much price
difference there is, at different hospitals
within the same city.”
– Former Health & Human Services
But most of these initiatives are aimed
secretary, Kathleen Sibelius
at achieving “information availability”.
to “repeated information usability”.
towards making information on price,
Despite more than 95% of health plans
quality of service & outcomes, and process
offering cost estimator tools, a paltry 2%
data available to consumers. The Towers
of consumers are actually putting them
Watson survey reveals that currently
to use. The usage of information and
60% of employers offer price and quality
tools needs to become a consumer habit.
transparency tools to employees through
This can only happen when health plans
health plans and specialty vendors. An
provide easy access to the right data,
additional 29% plan to do so in 2015.
launch awareness initiatives, and provide
Further, payers have made a strong entry
incentives, which motivate consumers to
in this space, such as the partnership
make sustained usage of the information.
the public for free.
• Understand – What are the consumers’
usage fell by 15%
Not enough thought has been applied
payment database that will be available to
three key questions:
not come to know what a hospital
plans have started taking small steps
Health Care Cost Institute, to create a
based on the answers to the following
More expensive hospital-based MRI
Cognizant of these trends, even health
between Aetna, UHG, Humana, and the
to use. The roadmap must be designed
Cost reduced by $220 or 18.7%
they have more skin in the game and
thereby they demand more information,
accessing information to truly putting it
market:
and universally available information.
Further, there is a consistent rising trend
to engaged, as they move from merely
In this context, health plans should chart
a well-planned roadmap that gradually
transforms consumers from attracted
Understand
How clear is clear? The Dimensions of Healthcare Consumer Transparency
Consumers have myriad expectations
industry. Mapping the future transparent
can help health plans address the most
about the quality and clarity of
state to the current situation would help
common needs. Healthcare consumers’
information. Some of these stem from
them realize consumers’ expectations and
transparency needs can be classified as:
their experience with other industries, like
develop the transparency roadmap. Some
retail or banking. Therefore, healthcare
of these expectations would be specific to
organizations must contextualize these
an individual or situation. However, there
expectations within the realities of their
are common threads, which if identified,
•Price
• Quality of service and outcomes
•Process
Price transparency
Consumer expectations
Current state
Limited information on provider charges
on a local basis
Cost of
medical service
Future state
Clear data on average provider charges
for a particular service, starting from
admission to discharge
• Comprehensive listing of retail
Retail price of drugs offered at nearby
pharmacies available disparately
Cost of
medicines
• Focus on high impact areas, such as
specialty pharma
Out-of-pocket estimates unknown prior
to provider visit*
Health insurance
obligation clarification
*72% of consumers, who visited a provider in 2013, were unaware of their payment responsibility during a provider visit
drug costs including generic drug
equivalents of brands
• Personalized out-of-pocket estimate
prior to provider visit
• Comparative benchmark prices listed
service-wise as well as region-wise
Quality of service and outcomes transparency
Current state
Future state
• Comparative listing of providers based
• Limited knowledge through
personal experience
Patient’s experience of the
provider & care provided*
• Limited unverified reviews
• No listing of patients’ experience in
drug usage
Drug effectiveness &
reactions
Provider performance
assessment
• Difficulty in identification of generic
drug equivalents of branded drugs
on expert-referral, prior consumerexperience
• Awareness on consumers’ definition
of good quality care increases among
both the consumer and provider
community
• Open database to share drug
effectiveness and possible reactions
from both patients and providers
• Listing of possible generic drug
equivalents
Transparency at hospital level or physician
level available disparately, but not on
service level
Comprehensive listing of provider-service
combination and success rates
(Number of operations and treatments
undertaken, etc.,)
• Real-time feedback from consumers on
Listing of HEDIS, CAHPS, NCQA, 5-star, and
other standard quality rankings
Payer performance
assessment
rankings
• Open information self-released by
payers on varied parameters (network
sufficiency, member health statistics)
*97% of consumers would appreciate cost saving information from their doctor, but are not getting it.
Process transparency
Current state
Future state
• Automated tools to share/guide on
Administrative
procedural knowledge
Consumers unclear of process resulting in
high call center traffic. Example: Unclear
Explanation of Benefits (EOB), long wait
times for claim status update*
established procedures
• Real-time prompts for possible savings
• Clear & as-expected EOB
• Real-time claim status update and
alerts to validate identity
• Customized awareness sessions,
personalized trackers and alerts.
Very small population segments with only
limited generic information**
• Common symptoms database,
Healthcare literacy remains poor.
Understanding of basic coverage terms is
below average***
Increased persistent consumer-payer,
consumer-consumer virtual interactions
Medical procedural
knowledge
regularly updated by members
(verified prior) & providers.
Healthcare insurance
knowledge
*51% of patients do not check health records & EOBs for inaccuracies, either because they don’t know how or it’s too confusing
**50% of patients with a chronic condition do not get diagnosis and treatment information when needed
***More than 60% of Health Insurance Exchange target population unaware of fundamental concepts, including premiums, out-of-pocket spending limits
Plan
How and When to Fulfill Transparency Expectations
Healthcare organizations are yet to offer
a wholesome engagement experience to
the consumers as compared to Retail or
Banking industries. This is primarily due
to privacy concerns, regulations, legacy
B2B business models designed to serve
large groups and focused on improving
administrative efficiency, inability to
simplify the complex medical and
financial information for easy consumer
comprehension, and the presence of
multiple internal & external information
sources limiting health plans’ agility in
sharing timely data.
This expectation can be met by evolving
from basic and discrete information
tools to a portfolio of solutions in order
to provide consumers with a full “retail
experience”. This includes informing,
educating, clarifying, assisting with
shopping or navigating the healthcare
system, and engaging consumers on a
continuous basis. These solutions can be
deployed at specific consumer touchpoints
and each has a specific informationsharing role to play. Some solutions, such
as the comprehensive listing of providers
or drugs, can reach their full potential
if multiple payers work together with
regulatory agencies. This will enable the
design of more practical solutions that
the consumers can put to real use. The
following is a graphical representation
of specific areas of opportunity for
improving consumer transparency, where
unique nimble solutions can be designed
to offer information that is readily usable
by consumers.
Addressing the consumer’s transparency needs – opportunity areas across consumer touchpoints
A
• Quality Rating display
• Comparative picture of
Know your health plan:
• Member health statistics Scorecard
industry standing
(Prevention & Care Management)
B1
Basic Admission to Discharge Cost
Estimator
B3
Care Efficiency Scorecard, including
Provider Network Sufficiency
B2
Provider-dynamic directory with
Ease of Navigation Enablers
B4
Automated Service-Alerts based on
member profile
C1
Comprehensive Provider-Service
Index across:
C6:
A
Advanced Pre-Visit Shopping
Companion (Care: C, Admin: A)
•Quality
•Cost
•Performance
•Credentials
• Max Peer Referral
• Consumer Experience Rating
A – Attention
B – Enrollment
C – Delivery
of care
D – Claim
management
E – Feedback
F – Member
engagement
C2
Online Symptom Search
C3
Online Health Terminology Directory
C4
Pre-emptive Alerts based on Medical
Need of Service
C5
Customized Wellness tracking tools
C6:
C
Advanced Pre-Visit Shopping
Companion (Care: C, Admin: A)
• Both Price and Quality
• Process transparency
C7
Drug Choice Help
• Prescription Drug Price Index
• Drug Alternatives Listing
• Personalized Drug Reaction
Checker
C: Accurate OOP Estimator
D1
Provider-Specific Performance
Metrics
D2
Personalized savings alerts
D3
Ease of claim settlement
• Online Claim Status Checker
• Live Meeting Walk-through of
claim documentation
• Real-time Threat Alert and
Denial Management
Member-Provider Reviews of:
• Provider Services
E2
Suggest As I Know You
Payers/ Members can refer new
plans/services based on known
history of other consumers
F4
Constant Interaction and
Summary Sharing
• Payer Transparency
• Real-Time Feedback on
Legend - Mapping to
Dimensions of transparency
outcomes transparency
A: Automated Incentive Provision:
Choice of Provider
C: Benchmark-based pricing tool
E1
• Price transparency
• Quality of service and
A: Appointment Scheduling post
service-based comparison
F1
F2
F3
published ratings
Virtual Recognition of constant users
Virtual Interactive session between
Top Rated/Popular Providers and
Members
Real Engagement Scorecard
• Constant Prompter Service
– Small Prompts at every
interaction point (screen, etc.,)
giving highlights of next step
• Customizable dashboard based
on member profiling
• Constant Surveys
Implement
Adopting a Transparency Solution and Prioritizing Investment
A health plan’s decision of which areas of
of-pocket related process information only
• Involvement of third parties such as
consumer transparency opportunity to
at the delivery of care stage. Mapping the
providers, other health plans, and
pursue will depend on its advancement in
solution’s true purpose to the touchpoint
consumers
the consumer engagement journey, digital
will ensure that the consumer receives just
transformation progress, prioritization
the right information at the right time.
of short and long term objectives, and
resource constraints. Generally, health
plans would tap those opportunity
areas that provide optimal solutions
Depending on the investment cycle,
some health plans may prefer short term,
low-hanging fruit, whereas others may
very complex. To elaborate, very complex
as follows:
implementation entails highly customized
• Providing basic “good to know”
solutions, with high implementation
information to consumers
• Providing operational information for
peace of mind
• Providing critical information to
consumers for making decisions on
choose broad-based initiatives appealing
to a larger section of consumers. However,
common ground for evaluation and
their healthcare
• Enhancing knowledge to facilitate
prioritization of investments in consumer
transparency should be based on the
solution’s “transparency objective” and
informed shopping
• Personalizing information to
build loyalty
“complexity of implementation”.
A.
Transparency objective
Usually enterprises share business
information with the consumer with a
specific objective which is best served
when the information is provided to the
consumer at the time he needs it the
most and through the most appropriate
touchpoint. For example, a consumer with
health insurance cover would need out-
be graded along five levels, from simple to
A solution’s objective may be categorized
and wholesome experience, while
complementing their business model.
The complexity of implementation may
B.
Complexity of implementation
The complexity of implementing a
transparency solution largely depends on
the following three parameters:
• Build versus buy decision based on the
level of customization needed
• Complexity of data gathering, sharing of
information and maintenance
and maintenance costs, and needs realtime dynamic data updates, requiring
stakeholders to commit time, resources,
and ideas in order to succeed. On the
other hand, simple implementation
involves solutions with industry-standard
functionality and minimal customization,
supported primarily by static data that is
updated periodically. For these reasons, a
simple implementation makes it easier to
get the stakeholders on-board and costs
less than a complex one.
Mapping complexity of implementation to the solution’s transparency objective
The combination of the considerations of “transparency objective” and “implementation complexity” will help health plans analyze and
prioritize the transparency focus areas as depicted in the graphic below, and chart out a roadmap for making relevant information available
and usable to a large number of consumers.
Mapping of “Complexity of Implementation” to “Transparency Objective”
Transparency objective
Personalized information –
to build loyalty
F4
F1
E2
Enhanced knowledge –
to facilitate informed shopping
Critical information –
for healthcare decision making
C4
C6:
A
C7
B4
D2
F2
B2
C6:
C
E1
Operational information –
for peace of mind
Basic information –
good to know
D3
C5
A
C1
B3
F3
D1
B1
C3
C2
Simple
Medium
Complex
Very Complex
Complexity of implementation efforts
Touchpoints:
Attention
Enrollment
Delivery of Care
Claim Management
Feedback
Member Engagement
The alpha-numerical names in the circles represent the opportunity areas identified in the previous section of this article
It is evident from the graphic that the path
Looking ahead
to transparency is not simple. Most of
To succeed, a health plan’s consumer
the focus areas, which make information
transparency initiative should be based on
usable to consumers, are complex to
three fundamental strategies:
implement. This can be observed from the
1. Consumer-centric
mapping in the graphic wherein 60% of the
opportunity areas fall under complex or
very complex implementation categories.
Similarly, most of the unaddressed
transparency needs are in the top three
brackets of transparency objectives, which
are supposed to provide critical care
information, enhance consumer experience
and ultimately build loyalty. Additionally,
delivery of care (the yellow circles) and
enrollment and claim (the blue circles) are
the health functions with the maximum
white spaces in consumer transparency
implementation. By targeting these gaps
• The transparency decisions should be
aligned to consumer expectations
and experience but not heavily
influenced by “complexity of
implementation”.
• Transparency cannot be achieved
through a big-bang approach, rather,
should be aligned to consumer clusters
and in some cases, to individual
consumers. This will help in creating
personal appeal.
• Consumers’ expectations are drawn
from what they experience with
early, health plans can succeed at providing
other industries. Cross-industry learning
a wholesome consumer experience.
can keep the health plans abreast of
The bottom line in the near future:
evolving needs.
the healthcare industry would rapidly
accept transparency as a state of being.
• The true value of transparency
initiatives can only be realized if the
This state will call for bringing together
consumers use the applications. To
multiple stakeholders, pooling in data,
achieve this, specific awareness sessions
time, and effort, and ultimately engage
should be undertaken targeted at
the consumers. To ensure health plans play
particular clusters,
an important role, they should start now,
tweaked based on population-specific
prioritize their investments, and address the
content and mode of access.
existing information gaps, to evolve from
2. Organization-wide effort
• Transparency needs more strategic
focus and regular investment
commitment, and should be part
of quarterly plans and boardroom
conversations. It requires the strong
backing of external stakeholders to
create a holistic plan
• Transparency cannot be left to
functional teams. Special task force
teams including the heads of IT,
marketing, and consumer experience,
should run the show in conjunction
with the functional SMEs.
• Transparency opens the floodgates of
information, bringing in the need for
tighter and sharper privacy policies.
3. Agile:
• Consumer transparency needs to keep
evolving. Facilitating a continuous
feedback mechanism and innovation
environment can ensure that health
plans constantly take the right effort
and stay ahead of their competitors.
• Using a combination of in-house
resources, third party vendor solutions,
and strategic consulting partnerships,
will help health plans create a
scalable and flexible transparency
solution portfolio.
the current state of information availability
to a target state of information usability.
References
1
Per Gartner’s four key attributes of Consumer Engagement
2
2014 Report Card on State Price Transparency Law
3
From 2006 to 2013 there has been a fivefold increase in employee enrollment in High Deductible Health Plans as per Kaiser/
HRET Survey of Employer-Sponsored Health Benefits
4
19th Annual Towers Watson/National Business Group on Health Employer Survey on Purchasing Value in Health Care
5
Catalyst for Payment Reform Survey
6
InstaMed Survey of Consumers who visited a Provider in 2013
7
2013 Accenture Consumer Transparency Survey
8
Ponemon Institute Survey on Medical Identity Theft, 2013
9
Institute of Medicine
10 Health Affairs Study published in December 2013
About the authors
Deepak Agarwal is a Senior Consultant with the Healthcare Consulting Practice at Infosys Public Services.
He has 6+ years of experience working for different Provider & Payer clients (U.S.A. & India) – advising them
on IT modernization, Process Re-engineering, Digital transformation for Consumer engagement, new Care
delivery models in tune with ACO and PCMH, and Enterprise Risk analytics & management. He brings niche
expertise on compliance strategies for healthcare reforms especially for ACA and ICD-10.
He can be reached at deepak_agarwal111@infosys.com
Madhuri Murthy is a Senior Associate Consultant with the Healthcare Consulting Practice at Infosys
Public Services. Her key areas of expertise lie in analysing the trends and market potential of upcoming
technologies, delivering research & consulting support and Points of View, contributing to business
development. She has also gained experience in QNXT and ICD-10 compliance related work.
She can be reached at Madhuri_Murthy@infosys.com
Big Data Analytics in Healthcare – Taming the Elephant in the Room
Analytics – The Next Big Thing in Healthcare
Analytics will have a large role to play
as member segmentation using factors like
a different approach to analytics to close
in helping healthcare payers redefine
Medical Loss Ratio, age, gender, length of
the gaps in consumer experience that are
themselves and engage consumers
policy, etc.) and the like. However, survey
highlighted by the surveys. Two aspects
by helping them manage their
feedback as mentioned below, portrays
need to change going forward:
healthcare experience from beginning
a stark picture in the payer industry. A
to end. According to a leading analyst
leading analyst firm states that up to three-
firm, analytics is the 3rd leading
quarters of consumers say that they are not
investment driver for payers in the year
satisfied with the documents and materials
2013-2014, with 50 percent of the
they use for making healthcare decisions.
health plans reporting investment in
Another report says that consumers rank
consumer analytics.
health plans last among 14 industries
This is not to say that payers didn’t use
analytics in the past, or aren’t doing
enough at present. Payers have used
on consumer experience, trailing even
television and Internet service providers,
and well behind other insurance providers.
analytics before for triggering simple mail
Since analytics is the key to consumer
for policy renewal, in internal analysis (such
experience, it is clear that payers must take
1) Consumers expect health plans to
provide the same consumer experience
as mature industries like retail, telecom,
and banking. Hence it would be useful
to adopt and apply the analytics
concepts used in those industries,
in healthcare.
2) Healthcare analytics must learn to
leverage Big Data to achieve the
outcomes of better patient care,
consumer satisfaction, etc.
Let’s talk about the issue of consumer
Not many examples of big data analytics
preference, and other factors uncovered
retention in a competitive environment.
in healthcare are quoted publicly since
in the data
Most Blues don’t have a CRM-based
many firms are still experimenting with
discovery phase.
approach to understand which members
it. Carolinas HealthCare System recently
are likely to attrite. Commercial payers
mentioned using data, which includes
are more advanced in that they do have
purchases a patient has made (using a
statistical models which use different
credit card or store loyalty card), into
factors to develop attrition lists.
predictive models that assign a risk score
Let us see how cross-industry experience
and big data may be leveraged in this
context.
to patients. The score would be regularly
passed on to doctors and nurses who can
3) Prescriptive analytics – Based
on data discovery and predictive
analytics, provide a range of marketing
intervention strategies for policies
required through the member
life cycle.
suggest timely interventions to high-risk
Or let’s take another area of consumer
patients before they actually fall ill. To
experience from a care coordination and
Cross-Industry Experience: Following the
quote an example of the analytics that’s
management perspective. Typically, this is
lead of industries such as retail, health
possible – “For a patient with asthma, the
an area where different entities responsible
plans could employ member profiling,
hospital would be able to assess how likely
for care don’t have appropriate hand-
product recommendation algorithms, and
he is to arrive at the emergency room by
offs, which adds to readmission cost and
extensive factor A/B testing for products to
looking at whether he’s refilled his asthma
patient discomfort. According to research
tailor their product and message outreach
medication at the pharmacy, has been
from 2012, the top reason for readmission
strategy to different users.
buying cigarettes at the grocery store, and
among the Medicare fee-for-service patient
Big Data: They can modify the outreach
lives in an area with a high pollen count”.
population is heart failure; more than 25
strategy further using big data by factoring
This example is quite futuristic and should
contact center and member portal data
be within the realms of achievement in
fields, if statistically significant, in the model.
some years. However, there are other
In our experience, the adoption of analytics
opportunities that can be tapped in the
concepts from other industries has started
present itself. In our experience, given
in earnest. However, big data analytics is
the huge volumes of varied data, there is
still a new idea in the industry, and will take
an opportunity to find insights to answer
some time to gain traction. This topic will
questions that were previously considered
be discussed in some detail in this paper.
beyond reach by the payer industry. On the
A leading consulting firm estimates that big
data analytics can enable more than $300
billion in savings per year in U.S. healthcare
and first mover advantage will ensure
significant gains over a longer period.
According to another survey by a leading
consulting firm, 95 percent of healthcare
CEOs are exploring better ways of using
and managing big data; however, only
36 percent have made any headway in
coming to grips with it. All agree that big
data analytics has the potential to
improve the quality and cost of care, but
many are still struggling with finding the
percent of patients hospitalized for heart
failure will be readmitted to the hospital
within 30 days of discharge. It is here
that big data analytics can be effectively
leveraged for reducing heart failure patient
readmission by:
1. Understanding current readmission
rates.
2. Establishing 30 and 90 day
consumer experience front, let’s consider
readmission measures to prevent
the role of analytics in member renewal or
looking at old data.
prospect solicitation. Integrated data based
on member demographics, medical claims,
and social media activity can throw up
immense possibilities for analytics of the
following type:
1) Data discovery – Initiatives focused
3. Identifying and then stratifying patients
with a primary diagnosis of heart failure,
so that multidisciplinary teams may
examine the root cause of readmissions
to implement evidence-based, bestpractice intervention plans for patients.
on identifying which policies work
The teams can implement these
for which specific segments of the
interventions and track their impact on
population from multiple perspectives
readmission rates.
– cost benefit, value-based benefit care,
administrative effectiveness, etc.
2) Predictive analytics – Initiatives around
right ways to infuse analytics into
recommending policies to prospects
everyday operations.
based on the analytics of behaviour,
Context
Member /Patient Centricity
Traditional Care Models
New Care Model
Lack of provider integration, member
Data interchange, exchange, and
data, channel data, etc., leads to
coordination allow for higher degree of
operational silos.
customizable care for members / patients.
Integrated, coordinated care across entire
Care Delivery
Fragmented and disjointed –
care continuum via proactive disease
redundancies and gaps in care.
identification, mapping care program to
patient and care management.
Accountability
No accountability for care delivered.
Ability to incorporate pay-forperformance for care delivered.
Nearly one-third of Americans have two or more chronic conditions, and individuals with chronic diseases drive more than 75 percent of
healthcare costs. Payer health plans and insurance companies can significantly reduce the cost of care by addressing some of the following
areas with the aid of big data solutions:
• Time sequencing – longitudinal analysis of care across patients and diagnoses
• Cluster analysis on influencers of treatment for chronic conditions
• Analysis of clinical notes (multi-structured data)
Technical Challenges
The benefit of using big data is well
trend investigation into data going
size of stored data and provide a cleaner
understood. But performing analytics on
back several decades. However, if the
set of data for analytics.
big data presents its own set of unique
purpose is to improve understanding
challenges. The Carolinas Healthcare
of members’ portal usage behaviour, it
System story has had a huge impact on the
may not be relevant to store data that is
possibilities of using big data for analytics.
more than a few months old.
However, on the flip side, many payers are
concerned that the amount of amassed
data is so large that it is difficult to find
•
Should we analyze it all?
This question, in the context of big
the most valuable pieces of information.
data, is parallel to that of the correct
Here are some of the questions that IT /
sample size in predictive analytics.
Business personnel frequently grapple with
When we are talking about data of
when analyzing big data.
the order of Petabytes and Zettabytes,
•
understanding it becomes a huge
Should we store all our data for doing analytics?
challenge. The guideline here is to
understand that since computation
Setting aside the need to maintain
is cheaper than storage, an inherent
certain healthcare data by law, the
differentiation of data which will be
decision of what and how much
stored / data lakes from the real time
data to store depends on the final
incoming data / data streams should be
purpose. For e.g., any initiative to study
made. An upfront data evaluation of the
effectiveness of care needs a long term
incoming streams can help reduce the
Acquire & Retain
Indicative data to be used: Consumer
demographic information along with
products selected information
Use: Offer suggestions to new prospects
based on their demographic information
Outcome: Increase in prospect to
member conversion
Involve & Empower
Indicative data to be used: RX and
medical data
Use: Identifying consumer need and
providing relevant / allied information
Outcome: Ability to pre-authorize for
medical and pharmacy and display copay information associated with specific
pharmaceuticals
•
Which areas should we focus analytics on while managing the data deluge?
The key to obtaining effective insights
from analytics lies in identifying the
appropriate areas where this insight
would be used. Segregating the
sheer volume and variety of data to
identify those areas that are vital from
an analytics point of view is of grave
importance. An indicative diagrammatic
representation of the data which can be
used across the consumer lifecycle and
the benefits this entails is given below:
Inform & Service
Indicative data to be used: Collecting
and analyzing trends from mobile
app interactions
Use: Monitor and gather cardio /
diabetes related data
Outcome: Allows medical professionals
to provide care options using remotely
gathered data
We had already indicated an example of
payers using big data analytics for
soliciting prospects. Here’s another
example of how integrating Rx and
• How can we find data points
analytics should answer in order to be
both strategically and operationally
of drugs with clinical diagnoses to
regardless of the time frame.
both technology and business to rapidly
develop industry-specific insights.
nothing to business intelligence. So,
spending perspective.
An ideal solution is one that enables
understanding business strategy – what
technology and business users to
is trying to be accomplished at the
2) Predictive analytics – Initiatives
around analysis of cost of care over
highest levels, and how this strategy
identified treatment paths to identify
plays out in operations /outcomes is
the most effective one.
important.
work together to integrate, aggregate,
manage, analyze, disseminate, and act
upon large volumes of multi-structured
data. With a repository of over 250
algorithms, 50+ visualization options,
• How fast can I capitalize on
and industry-specific applications,
big data?
analytics, provide a range of
There is a need for a platform like
Infosys BigDataEdge for empowering
is “noise” that contributes little or
appropriate from a safety and
drug utilization.
analytics, which can be a huge ask,
meaningful. 95 percent of big data
understand if utilization is
diagnosis codes as a way of managing
scientists, modelers, etc.) to do the
performance indicators (KPIs), which
on identifying utilization patterns
perquisite trials to restricting
hiring, and training resources (data
be able to define questions on key
1) Data discovery – Initiatives focused
provider education to requiring
available products as well as identifying,
data scientists / BI personnel need to
“service” area of the consumer lifecycle:
intervention strategies, from enabling
This requires deep evaluation of
Once the hurdle of which area(s) to
focus on in big data analytics is crossed,
possibilities for analytics under the
data discovery and predictive
needs agility of insights and actions.
which are really significant?
medical data can throw up immense
3) Prescriptive analytics – Based on
data applications, whereas business
Infosys BigDataEdge can help
In our experience, enterprises are
businesses generate insights up to eight
looking for the ability to quickly
times faster and action decisions in real
discover, analyze, and act on
time.
information to drive business decisions
as a way of capitalizing on the
Each payer might focus on a different part
opportunities of big data analytics and
of the consumer life cycle based on their
addressing its technical challenges.
internal set of objectives, priorities, budget
Technology teams need the flexibility
constraints, et al.
to rapidly develop industry-specific big
The final decision of how analytics will be
used will depend on the individual payer’s
requirements and constraints of budget,
time, and personnel, and therefore must
be based on a thorough understanding of
these elements.
References
[1] Burghard, Cynthia; Young, Janice W., U.S. Healthcare Payer Top 10 2014 Predictions: Focus on Analytics, Dec 2013, Doc # HI244841, IDC Health Insights
Presentation
[2] Manning, Harley. “Hot off the press: Forrester’s Customer Experience Index, 2011.” January 11, 2011.
[3] Groves, Peter; Kayyali, Basel; Knott, David; Kuiken, Steve Van; The “Big Data” revolution in healthcare, McKinsey&Company, January 2013
[4] Fit for the future 17th Annual Global CEO Survey, Key findings in the Healthcare industry, PWC, February 2014
[5] http://www.carolinashealthcare.org/body.cfm?id=14&action=detail&ref=805
[6] http://www.cdc.gov/chronicdisease/resources/publications/aag/chronic.htm
About the authors
Dr. Deepti Mehtani
Healthcare Consultant, Infosys Public Services
Deepti Mehtani is a healthcare consultant working with Infosys Public Services. She has wide experience of working on both
Payer and Provider domains and is an SME on Provider Revenue Cycle Management.
She can be reached at Deepti_Mehtani@infosys.com
Madhumitha Swaminathan
Senior Associate Consultant, Infosys Public Services
Madhumitha Swaminathan is a Senior Associate Consultant, working with the healthcare vertical in the Infosys Public
Services. She has worked extensively in the healthcare payer domain, for both Blues and Government clients. She has mainly
worked on Health Insurance Exchanges and Payer Portals.
She can be reached at Madhumitha_S03@infosys.com
Anand Madhavan
Senior Practice Lead, Infosys Public Services
Anand Madhavan is a Senior Practice Lead with Infosys Public Services responsible for analytics in healthcare.
He has a decade-long experience in helping organizations navigate strategic business problems using analytics and
driving tangible business impact.
He can be reached at Anand_Madhavan@infosys.com
Using Analytics for Insurance Fraud Detection
3 Innovative Methods and a 10-Step Approach to Kick Start Your Initiative
Summary
If you’ve been used to thinking about analytics in terms of sales or marketing, think again. Today, analytics
can reinvent your enterprise technologies — social networking, big data, CRM — to crack down on
financial offenders. Giving you more than an insight a day, to keep the fraud away.
Digitization
aids branding, customer acquisition, and
incidences of high-value fraud went
a new opportunity for fraud
detection?
retention. Insurance firms also receive a
undetected. In addition to this, the big
plethora of inputs from digital information
data trend, (the growth in unstructured
in the form of feedback, which also can be
data) always leaves lot of room for a
used to come up with customized products
fraud going undetected if data is not
of mobile devices and social media is
and competitive pricing.
analyzed thoroughly.
changing the business landscape for
In addition to these opportunities,
all sectors — including insurance. The
insurance companies are harnessing
The big data trend, (the growth in
opportunities offered by this landscape
digitization — using data analytics for
unstructured data) always leaves lots
for insurers are vast. Social networks and
fraud detection. Handling fraud manually
of room for a fraud going undetected
communities help insurers connect with
has always been costly for insurance
if data is not analyzed thoroughly
their customers better, which in turn
companies, even if one or two low
Digitization marked by a growing number
Traditionally, insurance companies use statistical models to identify fraudulent claims
Fraud detection by insurance
companies
These models have their own
companies have to bear the consequences
Analytics addresses these challenges and
disadvantages. First, they use sampling
of the first time. Finally, the traditional
plays a very crucial role in fraud detection
methods to analyze data, which leads to
method works in silos and is not quite
for insurance companies. Some of the
one or more frauds going undetected.
capable of handling the ever-growing
key benefits of using analytics in fraud
There is a penalty for not analyzing all
sources of information from different
detection are discussed below.
the data. Second, this method relies on
channels and different functions in an
the previously existing fraud cases, so
integrated way.
every time a new fraud occurs, insurance
Using sampling techniques comes with its own set of accepted errors. By using analytics,
insurance companies can build systems that run through all critical data. This in turn
helps detect low-incidence (0.001%) events. Techniques such as predictive modeling can
be used to thoroughly analyze instances of fraud, filter obvious cases, and refer lowincidence fraud cases for further analysis.
Analytics help in building a truly global perspective of the anti-fraud efforts throughout
the enterprise. Such a perspective often leads to effective fraud detection by linking
associated information within the organization. Fraud can occur at a number of source
points: claims or surrender, premium, application, employee-related or third-party fraud.
At the same time, insurance channel diversification is adding to the fragmentation of
traceable data. Insurance-related activities can be done via mobile devices apart from
the traditional online and face-to-face insurance. This can be viewed as an addition to
information silos in the insurance industry. Given greater channel diversification and the
increase in areas where fraud can occur, it is important for insurers to have accessible
enterprise-level information about their business and customers.
Analytics plays an important role in integrating data. Effective fraud detection capabilities
can be built by combining data from various sources. Analytics also help in integrating
internal data with third-party data that may have predictive value, such as public records.
Data sources with derogatory attributes are all public records that can be integrated into
a model. Examples include bankruptcies, liens, judgements, criminal records, foreclosures,
or even address change velocity to indicate transient behavior. Other types of third-party
data can be beneficial in enhancing efficiencies such as review of appraisal information
to determine if damages match description or loss or injuries being claimed. One of
the most under-utilized data sources is medical bill review data. This data, if used in a
model properly, is a gold mine for companies investigating medical fraud. Uncovering
anomalies, in billing and adding these to the other scoring engines or social network
analysis will decrease the amount of time an investigator or analyst spends trying to pull
all of the pieces together to identify fraudulent activity.
Analytics helps in deriving the best value from unstructured data. Fraud can be soft fraud
or hard fraud. This is based on whether it consists of a policyholder’s exaggerated claims, or
if it consists of a policy holder planning or inventing a loss. At a high level, fraud can occur
during commission rebating, due to false documentation, collusion between parties or
from mis-selling. Although lots of structured information is stored in a data warehouse as
part of many applications, most of the crucial information about a fraud is in unstructured
data, such as third party reports, which are hardly analyzed. In most insurance firms,
information available in social media is not appropriately stored. A special-investigativeunit investigator will agree that unstructured data is very important for fraud analysis.
Since textual data is not directly used for reporting, it does not find a place in most data
warehouses. This is where text analytics can play a key role in reviewing this unstructured
data and providing some valuable insights in fraud detection.
Three innovative fraud
detection methods
saves time and gives the insurer an
link analysis, one looks for clusters and
insight into the parameters involved in
how those clusters link to other clusters.
1. Social Network Analysis (SNA)
the fraud case. SNA allows the company
Public records such as judgments,
to proactively look through large
foreclosures, criminal records, address
amounts of data to show relationships
change frequency, and bankruptcies are
use of social network analysis (SNA). In
via links and nodes.
all data sources that can be integrated
a car accident, all people in the vehicle
The SNA tool combines a hybrid
into a model.
have exchanged addresses and phone
approach of analytical methods. The
numbers and provided them to the
Using the hybrid approach, the insurer can
hybrid approach includes organizational
insurer. However, the address given by
rate these claims. If the rating is high, it
business rules, statistical methods,
one of the accident victims may have
pattern analysis, and network linkage
indicates that the claim is fraudulent. This
many claims or the driven vehicle may
analysis to really uncover the large
have been involved in other claims.
amounts of data to show relationships
Having the ability to cull this information
via links. When one looks for fraud in a
Let’s take an example to explain the
SNA follows this path:
• The data (structured and unstructured) from
various sources is fed into the extract transform
Operational
data store
may be because of a known bad address
or suspicious provider or vehicle in many
accidents with multiple carriers.
Extract
transform
load
Fraud
repostitory
and load tool. It is then transformed and loaded
into a data warehouse.
• The analytics team uses information across a wide
variety of sources and scores the risk of fraud and
prioritizes the likelihood based on multiple factors.
The information used can range anywhere from
a prior conviction, a relationship in some manner
to another individual with a prior case, multiple
rejected claims, odd combinations of data, or even
odd modifications to personal information.
• Technologies such as text mining, sentiment
analysis, content categorization and social network
analysis are integrated into the fraud identification
and predictive modeling process.
• Depending on the score of the particular network,
an alert is generated.
• The investigators can then leverage this
information and begin researching more on the
fraudulent claim.
• Finally, issues or frauds that are identified are
added into the business use case system, which is a
part of the hybrid framework.
Insurance fraud detection using social network analysis
Before implementing SNA, insurers should consider:
• How fast data arrives
• How clean the data is when it arrives
• How deep the analysis must go to get the results
• What type of user interface components need to be
included in the SNA dashboard
Case study: GE Consumer & Industrial Home Services Division
Scenario
providers committing fraud. This situation
calculated for each claim. There are
made for an ideal pilot scenario. SAS was
some indicators like flags that are
given the responsibility of analyzing the
calculated based on various metrics
available data and identifying patterns in
and sent for auditing when they
the data to find out who was committing
indicate that multiple elements in the
the fraud.
claim fall out of the normal curve. Once
these claims are flagged, the auditors at
identify patterns. With the amount of
Functioning of the fraud
detection system
data available to them, no one could see
Typically, there are some metrics and
unusual behavior emerging. Sometime
indicators on every claim that assist in
The GE Consumer & Industrial Home
back, GE got the perfect scenario to test
identifying suspicious or fraudulent claims.
Services Division estimated that it
an SNA solution from SAS, a developer
GE’s claims data is fed into the fraud
saved about $5.1 million in the first year
of business analytics software. The
detection software. There are 26 claim-
of using SAS, to detect suspect claims.
company was tipped off to some service
level analyses, which are automatically
In GE Consumer & Industrial Home
Services Division, claims typically came
from technicians who repair consumer
products that are under warranty. One
of the biggest problems with their
old process was that they could not
2. Predictive analytics for
big data
Consider a scenario when a person raises
a claim saying that his car caught fire,
but the story that was narrated by him
indicates that he took most of the valuable
items out prior to the incident. That might
indicate the car was torched on purpose.
Here’s how the text analytics
technology works:
• Claim adjusters write long reports
when they investigate the claims
• Clues are normally hidden in the
reports, which the claims adjuster
would not have noticed
GE investigate these suspicious claims.
Outcome
• However, the computing system,
which is based on business rules, can
spot evidence of possible fraud
• The most important point to observe
is that people who usually commit
fraud alter their story over time. The
fraud detection system can spot
these discrepancies
Predictive analytics include the use of text
analytics and sentiment analysis to look at
big data for fraud detection. Claim reports
span across multiple pages, leaving very
little room for text analytics to detect
the scam easily. Big data analytics helps
in sifting through unstructured data,
which wasn’t possible earlier and helps
in proactively detecting frauds. There has
been an increase in the use of predictive
analytics technology, which is a part of big
data analytics concept, to spot potentially
fraudulent claims and speed the payment
of legitimate ones. In the past, predictive
analytics were used to analyze statistical
information stored in the structured
databases, but now it is branching out
into the big data realm. The potential
fraud present in the written report
above is spotted using text analytics and
sentiment analysis.
Case study: Infinity
Insurance Co.
to others. With the kind of exposure
After using predictive analysis,
Infinity has, spotting insurance fraud,
the claims fraud system increased
Infinity, a property and casualty
either while raising the claim or while
the success rate in pursuing
calculating the premium to be paid, is
fraudulent claims from 50–88 % and
even more important than it is to other
reduced the time required to refer
insurance companies. Infinity uses a
questionable claims for investigation
predictive analytics technology to spot
by as much at 95%.
company, came up with the idea
of ‘scoring’ insurance claims from
customers to look for signs of fraud.
Its target market is mainly drivers
who have higher than normal
risks and pay high rates compared
potentially fraudulent claims and speed
the payment of legitimate ones.
3. Social customer
relationship management
(CRM)
Social CRM uses a company’s existing
Social CRM is neither a platform nor a
social chatter, which acts as reference
technology, but rather, a process. It is
data for the existing data in the current
important that insurance companies link
CRM. The reference data along with
social media to their CRM. When social
information stored in the CRM is fed into
media is integrated within multiple layers
a case management system. The case
of the organization, it enables greater
management system then analyzes the
transparency with customers. Mutually
information based on the organization’s
beneficial transparency indicates that the
business rules and sends a response. The
company trusts its customers and vice versa.
response from the claim management
This customer-centric ecosystem reinforces
system as to whether the claim is
the fact that increasingly the customer is in
fraudulent or not, is then confirmed
control. This customer-centric ecosystem
by investigators independently, since
can be beneficial to the business as well,
the output of social analytics is just an
if the business is able to leverage the
indicator and should not be taken as the
collective intelligence of its customer base.
final reason to reject a claim.
CRM and gathers data from various
social media platforms. It uses a
‘listening’ tool to extract data from
Regulators
Customer
Business
Case study: AXA OYAK,
Turkey
AXA OYAK is a Turkish insurance
Using its social CRM, AXA was able to
efficiently. Using SAS, AXA OYAK was
company that has been using the SAS
clean up their customer portfolio data.
quickly able to find the relationships
Social CRM solution to manage risk
This helped them find and correct
between customer behavior and
and prevent fraud. AXA OYAK built an
inconsistencies in this data, which enables
fraudulent claims. With the SAS data
intelligent enterprise around social
AXA to link two slightly different records to
warehouse, AXA is able to segment
CRM in such a way that it integrates all
the same customer. With cleaner data, AXA
their customer data based on flags that
customer-related information into a
can run more accurate customer analysis
are generated while analyzing certain
single and coordinated corporate vision.
and investigate fraudulent claims more
relationships between data sets.
A 10-step approach to implement analytics for fraud detection
Many insurance fraud detection tools target only a specific insurance vertical, such as claim management, and build the entire framework
around it. For making the insurance fraud framework more robust, a more holistic framework is needed. One which examines all potential
areas for fraud – claims, premiums, applications, employee and vendor details in an integrated fashion. Here we outline 10 steps for
implementing analytics for fraud detection.
1
Insurance companies are realizing the importance of analytics in the fraud detection
Perform SWOT
space and hurriedly opting for expensive fraud solutions that are not aligned to the
company’s weakness and strengths. In order to leverage analytics solutions to the
fullest, insurance companies should first do a SWOT analysis of existing fraud detection
frameworks and processes to identify gaps.
2 Build a dedicated
fraud management
team
Usually, in a traditional insurance company, no specific team or person is proactively
accountable for fraud detection. When fraud is detected internally, people point fingers,
raise alarms and take measures to fight it. It is important that a dedicated team is identified
and made accountable for fraud detection. The team should report to senior management
for necessary buy in.
3
Whether to build
or buy
Once the SWOT is complete and a team of dedicated people for fraud detection have been
identified, insurance companies should review how they want to implement analytics and what
data sources they want to analyze. Insurance firms need to be honest in answering whether the
skill set for building analytics solutions are available in-house or whether there is a need to buy an
analytical fraud detection solution from an external vendor. If there is a need to buy the analytics
solution, insurance firms should evaluate different analytics vendors in the market to find a
solution that best fits the company’s requirements. Key parameters to judge an external vendor
are cost, user interface, scalability, ease of integration and ability to add new data sources.
4
Clean data
Integrate siloed databases and remove inefficiencies from processes and
redundancies from data sources.
5 Come up
with relevant
business rules
Insurance companies should leverage existing domain expertise and experienced resources to come
up with business rules. Certain types of fraud are very specific to the industry and, in some cases,
certain companies. Without inputs from in-house capabilities, it will be difficult for any internal or
external team to build a robust fraud detection solution.
6
Come up with
pre-determined
anomaly detection
thresholds
7 Use predictive
modeling
Whether the analytics framework is built in-house or by using a third-party vendor, insurance
companies should provide inputs for threshold values for different anomalies. The number
of claims received for life insurance is different from the number of claims received in nonlife insurance. Key performance indicators associated with tasks or events are baselined and
thresholds are set using anomaly detection. Setting the threshold is a major decision in anomaly
detection. If thresholds are set too high, too many fraudulent claims could slip through the system.
When thresholds are set too low, there can be risks of wasting time, alienating members and
providers, and can result in late-payment penalties. Certain statistical analyses take an empirical
value by determining ‘normal’ ranges for predetermined metrics.
An important fraud detection method is one that utilizes data mining tools to build
models that produce fraud propensity scores linked to unidentified metrics. Claims are
automatically scored to look for any indication of a discrepancy or fraud. After this, the
results are made available for review and further analysis.
8
Use of SNA
SNA has proven effective in identifying organized fraud activities by modeling relationships
between various entities involved in the claim. Entities can range anywhere between locations
to telephone numbers. The number of linkages between certain types of entities may be found
to be much greater than the average number of connections expected based on statistical
analyses of other ‘networks’ of entities.
an integrated
9 Build
case management
system leveraging
social media
Integrated case management capabilities allow investigators to capture all key findings that
are relevant to an investigation, including claims data, network diagrams, adjuster notes, and
social media, which can contain structured or unstructured data. Metrics are the key indicators
of fraud or abuse and can be automatically tabulated for comparison at the individual entity or
network level (using the anomaly threshold or SNA). Case workflow enables a full and complete
assessment of investigative workload, efficiency, and return on investment.
10
Forward-looking
analytics solutions
Insurance companies should keep looking for additional sources of data and integrate those
with existing fraud detection solutions, for building the most efficient fraud detection system
possible to address a variety of new frauds that may emerge in the future.
The proposed system can
•
•
•
•
•
Rapidly organize and analyze the unstructured data present in the claims submitted by the claimant, notes of the claim
adjuster and third-party reports
Examine the sentiments of the claimant to help drill down to the specific concerns that bother at-risk customers
Synthesize complex fraudulent patterns that contain the presence of multiple red flag indicators
Detect and provide early warning of potential issues before they become problems
Uncover early patterns in fraudulent activity
The way forward
Insurance firms always hesitate in implementing analytics because of the initial time investment needed for analytics solutions. However, it
has been seen that analytics goes a long way in detecting fraud proactively and earlier in the insurance lifecycle. It culminates in reducing the
overall cost of fraud detection and improving the overall ROI of insurance fraud solutions.
Insurers must now exploit the existing data in any form (structured or unstructured) by using analytics to effectively detect, manage, and
report frauds. The earlier the fraud is detected in the insurance lifecycle, the lesser it costs to manage it. Analytics can play a very important
role in identifying fraud early in the insurance lifecycle, and failing to act on this opportunity could quite literally equate to a gargantuan loss.
About the Authors
Ruchi Verma
Senior Consultant, Financial Services and Insurance Unit
She has around eight and half years of experience in Infosys in varied roles across multiple
accounts. Her areas of interest includes emerging trends and regulations in the financial
services and insurance domain.
She can be reached at ruchi_verma@infosys.com
Sathyan Ramakrishna Mani
Senior Associate Consultant, Financial Services and Insurance Unit
He has close to three years of experience in varied roles across multiple accounts. His interests
are in the area of capital markets. He is also a keen follower of macroeconomic events that take
place around the world.
He can be reach at sathyan_mani@infosys.com
Notes
Notes
Notes
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About Infosys
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Consumer Engagement through Digital Transformation

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