Still trying to stop insurance fraud? With PNA's Data Analytics, you can find insurance fraud before it happens. With advanced pattern recognition, you can stay ahead of the fraudsters.

1. www.positivenaick.com
Pinpointing Churn and
Detecting Fraud in
Insurance
Finding the secret sauce to predict
customer trends in finance.

2. Overview of Analytics
The insurance industry is about helping people’s problems and getting them back on their feet.
But insurance companies need to keep their own internal issues in control to ensure all of their
customers stay with them, instead of taking their business to a competitor. Two of the biggest
issues for insurance providers lies with understanding and reducing customer churn and detecting
fraud before it happens. Both problems are vast and muddled by mountains of data that aren’t
easily deciphered.
Customer churn is notoriously tricky to nail down. With thousands of customers each having a
reason of their own to be dissatisfied, and not enough staff on hand to address each customer
individually, nailing down the actual reasons for churn in an insurance company can seem like an
insurmountable task.
And then there’s fraud. Insurance is a high-stakes game, and there are always going to be people
that try and get a claim done even when there’s no valid reason. But the problem that plagues
customer churn also plagues fraud. There’s simply way too many claims at any given time to sift
through to find which are real claims and which are frauds. So, most insurance providers rely on
human intuition and thorough investigations to uncover whether it’s an actual claim or attempted
fraud.
This is where data analytics can ease the burden. Insurance providers are never in deficit of useful
data to use for analysis. The only problem they face is figuring out how to use the data efficiently.
1
Marketing Analytics
Marketing
Claims
Legal
Provider
Mgmt
Finance
Investment
Sales
Customer
Service
UnderWriting
Actuarial
Sales AnalyticsInvetment Analysis
Customer Service
Analytics
Financial
Analytics
UW Analytics
Provider and
Supplier Analytics
Actuarial Analysis
Claims Analytics
Legal Analytics
• Lead Generation
• Market Research
• Business
Development
• Campaign mgmt
• Advertising and
promotions
• Channels
• Sales Mgmt
• Field Development
• Productivity
• Compensation
• Training
• New Business
Processing
• Renewals
• Payments
• Complaints
• Inquireies
• Assessment
• Classification
• Pricing
• Profitability
• Rate Dev, and Filings
• Prod, Development
• Reserving
• Reporting
• Registration
• Adjustment
• Fraud Mgmt
• Medical Mgmt
• Litigation Mgmt
• Compliance
• Ins.Dept
Complaint Mgmt
• Litigation Support
• Contracting
• Resource Mgmt
• Performance Mgmt
• Expense Mgmt
• Planning and
Budgeting
• Profitability Mgmt
• Performance Mgmt
• Financial Rpting
• Compliance
• Reinsurance
• Asset and
Capital Mgmt
Insurance analytics

3. 2
Churn Analytics
Customer churn is a major
performance indicator that
companies seek to reduce as
much as possible, since
customer retention is less
expensive than finding new
customers. Additionally,
reducing churn can directly
correlate to better customer
satisfaction, since the
customers are not looking for
better alternatives.
Business Need
Using this churn identification
and prediction, we can find
what is currently causing
churn, and what can be
expected to cause churn in the
future. By identifying and
rectifying these problems with
intelligent business decisions,
we can reduce customer churn
to a large extent.
OutcomeApproach
First, churn rate has to be
categorized and measured.
There are two types of churn,
customer churn and revenue
churn. Using logistical
regression and SVM machine
algorithms, we can identify
the various reasons causing
either customer or revenue
churn. This gives us a baseline
to predict future reasons.
Churn Analytics helps understand the individual issues customers face that’s making them turn
away from a company’s insurance policies, and provide direction to help mitigate the churn.
It’s much less expensive to retain existing customers than to acquire new customers. To acquire
new customers, insurance providers have to start from the very beginning of the marketing and
sales funnel to find and convince new customers to buy insurance policies.

4. 3
With these methods, we expect
to find a variety of types of
fraud, including identified fraud,
unidentified fraud, and predict
future instances of fraud.
Keeping track of current and
anticipating future methods of
fraud can help insurance
companies stay vigilant against
malicious parties.
Outcome
Fraud Detection
The need here is to identify
and flag fraudulent insurance
claims to human agents, who
can then follow up these
cases to deal with
appropriately. Additionally,
new and novel methods of
fraud need to be identified
before they cause the
business any losses.
Business Need
Insurance fraud is no small issue for insurance providers. But the issue with detecting and
preventing insurance fraud is no small problem. Insurance providers have hundreds of thousands
of customers. Insurance claims are also complicated and lengthy processes, which can hide fake
insurance claims amongst legitimate claims. Dealing with insurance fraud using only human agents
is time-consuming, laborious, inefficient, and often, inaccurate. But that’s where Data Analytics can
help.
Approach
We look at past and present
patterns of recording fraud
using algorithms like the
Naive Bayes Classifier to
classify and use data. Using
these methods, we can
prepare to deploy measures
to handle current incidents, as
well as stay alert for future
incidents where fraud may
happen.

5. 4
Smooth and Easy
Adoption of Analytics
When it comes to adopting analytics in the insurance sector, the technical requirements aren’t as
vast as some other industries. Data is already collected and stored. Adopting analytics is done
through the following steps.
• Identifying internal use cases (what are the goals for incorporating analytics into a firm?)
• Understanding the role and impact of analytics (Finding out what analytics can do for you and
your needs)
• Finding Required Talent (Third party consultant, or adding an in-house analytics wing?)
• Technical requirements (How are we collecting data? What new methods can be used?)
When it comes to data analytics, one size
does not fit all. It can be wise for some
companies to try and reach out to a dedicated
Data Analytics solution provider to avoid the hassle of
setting up an internal team. With thorough internal
deliberation, an outcome can be reached as to
whether the company should hire third party
consultants, or to begin assembling a team internally.
There is also an option of hiring a team occasionally to
check current performance and suggest changes in the
future.
Finding Required Talent
Once the format of the team is finalized,
the technical requirements are viewed. For
insurance, there is already a wealth of
customer data through insurance policies and their
background checks. Here, there should be a check to
see if the data collected is sufficient, or if there is more
data needed. If there is more data or resources
required, then appropriate methods to collect and
obtain those resources should be implemented.
Technical Requirements
Every department in a company has data waiting to be
mined and processed into actionable insights.
Although every department does have data useful for
data analytics, the problem is that not every company
has the time or resources necessary to make use of all
the generated data. By identifying these internal use
cases, we can create clear and actionable goals for
data analytics to work on. With this, we can create
metrics and milestones to measure the progress.
Identifying Internal Use Cases
Once the use cases have been identified, the next step
is to set up how future changes will be measured. This
means having a conversation before deployment and
asking a few key questions.
-What are the performance goals after deploying the
solution?
-How are these goals going to be measured?
-Does our organization have the tools necessary to
measure them?
Questions like these will provide valuable insight into
finding out whether or not the deployed
solution is actually performing as intended,
or if it has any unintended consequences.
measuring Analytics

6. We hope this gave you better insight into how Data Analytics can help your company reach new
business goals. If you have any questions, please contact us using the details below.
Thank You
PositiveNaick Analytics Ltd. No177,1st floor,
LM Tech Park, 1st Main Rd, Nehru Nagar, Kottivakkam,
Chennai, Tamil Nadu 600041.
Email: customercare@positivenaick.com
Website: www.positivenaick.com
Phone: +91-44 4857 6162