Data Mining in Life Insurance Business

Data Mining proposal
for xxxxx Life Insurance
Lucknow
Submitted by
Accommodator Consultancy Services, Lucknow
Sep 28, 2013
Accommodator Consultancy Services Lucknow

Data Mining
Part of Business Intelligence
 Data Warehousing: is a central repository of
meaningful and accurate data created by integrating
data from disparate sources within a company, with
past and current data for both operational and
strategic decision making and senior management
reporting such as annual comparisons of budget per
scientist etc.
 Data Mining: According to the Gartner Group “it is
the process of discovering meaningful new
correlations, patterns and trends by sifting through
large amounts of data stored in repositories, using
pattern recognition technologies as well as
statistical and mathematical techniques”.

Effective Uses of Data Mining
 Establishing rates.
 Acquiring new customers.
 Retaining customers
 Developing new product lines
 Creating geographic exposure reports
 Detecting fraudulent claims
 Performing sophisticated campaign management
 Estimating outstanding claim provision
 Coordinating actuarial and marketing department

Establishing Rates
 Rate setting of each policy is an important problem for the
actuary. Likelihood and size of claim determine the rate.
 Attributes of existing customers are automatically analyzed to
establish relation with claims made (or not made), size of
claim and amount disbursed.
 Individual attributes are analyzed in iterative combinations until
meaningful and practical relationship is established.
 Variety of simple modeling techniques are available along with
visual display of results to arrive at meaningful relations.
 The goal is to categorize customers on basis of patterns of
risk, profitability and behavior. Each category is easily
assigned a rate for well known risk, profit and behavior.

Acquiring New Customers
 Data Mining is used to maximize marketing campaign’s
ROI by targeting customers with attributes indicative of
greater loyalty and hence better profits over the lifetime
of customer’s stay with the company.
 Data mining is also used to identify best time, best
season and best media to reach out to potential
customers.

Retaining Customers
 Offering bundled packages has long been traditionally used to
retain customers. The size of the bundle determines if
customer is likely to renew and also if he is likely to switch.
 Data Mining can easily lead to this magical figure of bundle
size.
 Additionally data mining can determine attributes indicative of
customer switching through predictive modeling.

Developing New Product Lines
 Products sought by customers keep changing with time.
Companies need to be on a constant lookout for change.
 To counter change, companies need to identify upfront
profitable customer profiles. New product offerings
should be tested against such profitable customers
profiles.
 Once the usefulness of new product is established, it
should be prioritized for introduction to the market based
on profit, number of potential customers or speed of
acceptance.

Creating Geographic Exposure
Report
 Insurance business and demographic database can be
augmented with socio geographic data aka spatial
attribute data.
 Purpose of doing this is to facilitate easy and informed
decision making for decision makers when setting rates
and identifying risks. Primarily used for determining
exposure and accordingly rate adjustments and
reinsurance needs.
 We have included sample reports in the demo which are
part of our offerings.

Detecting Fraudulent Claims
 Data Mining is ideally suited for detecting fraudulent
claims. Possible saving from detecting fraud fully justifies
the investment required.
 One of the techniques employes is to compare expected
and standard against actuals to see if abnormal data
exists.
 Blue Ccross Blue Shield saved an estimated $4 million in
1997 alone on account of saving from fraud detection.
 Demo includes fraud detection.

Estimating Outstanding Claims
Provision
 In the event of huge exposure spread out among large
number of individual policies as opposed same exposure
to limited firms, traditional methods penalize the latter
behavior thus forcing reinsurance which may be counter
productive.
 Data mining saves us from unreasonable fears by
understanding the claims and payouts for similar groups
in the past data and then predicting the real exposure.
 The aim of the modeler is to find the most granular
section of segment that results in a claim and use this
knowledge to reinsure such high risk cases.

Performing Sophisticated
Campaign Management
 As firms grow, customer centricity tends to lose focus
and instead product development takes center stage with
mass appeal for maximum profits.
 Data mining can help in identifying customer’s real needs
and desires and serves as foundation of future campaign
development.
 Data mining can also be applied to past campaign data to
understand how campaigns have done in the past to try
and improve campaigns.
 Demo includes fraud detection.

Coordinating Actuarial and
Marketing Departments
 Coordination of efforts can be achieved by strategic use
of data mining.
 Marketing departments findings can feed into actuarial
department and vice versa.

Few examples of companies
using Data Mining
 Fidelity – for real time Cross Selling, Customer
Retention and Weeding out Unprofitable Customers.
 Capital One – for lowering loan loss rate.
 Wakhovia Bank – for providing alternate branches to
customers moving cities and arranging for essential
services at their new location.
 Vodafone – for timely educating first time customers
about plans that would save them from running
exorbitant first bill amount by observing their usage.
 Swiss Life – Project DAWAMI implemented to enable
non technical end users to convert data into
information independently.

Data Mining Process
1. Identify Business Problem
2. Transform Data into Information
3. Take Action on Information
4. Measure the Outcome

Two pitfalls to avoid
 Learning things that aren’t true – Consider two
people A and B, both respond to charity calls, A
pays check of $500 but B pays $100 to five
responders. Is B more responsive to charity calls?
 Learning things that are true but not useful – In US
presidential election history, taller candidate always
wins. Is this finding any good? Similarly customers
credit history is indicative of whether there will be
insurance claim but regulators prohibit insurers from
making underwriting decision based on it.

Types of Data Mining Tasks
Type Definition Un/Directed Example Supported Algorithms
Classification Involves examining features of newly
presented object & assigning to a predefined
class
Directed -Spotting fraudulent
insurance claims
-Decision Tree
-Nearest neighbor
-Neural Network
-Link analysis
Estimation Continuous valued outcome however there
is no relation between input and target
variable. No past data necessary.
Directed -Estimating lifetime value of
customer --Estimating
prospect will buy insurance
-Regression
-Neural Network
Prediction Same as above. Records are classified
according to predicted future value.
Directed -Predicting which customers
will leave in next six months.
-Predicting which customers
will buy bundle of products
-Regression
-Neural Network
Affinity Grouping or
Association
To determine which things go together. Ex:
market basket analysis. Used for generating
rules from data.
Undirected
Clustering It’s the task of segmenting heterogeneous
population into a number of homogeneous
subgroups. No predefined groups exist.
Undirected
Profiling The purpose is to simply understand what’s
going on in a complicated database
Both Undirected and
Directed
-Decision Tree

Data Mining Process

Process: what it really means
 Translate business problem into one of six DM tasks.
 Locate appropriate data that can be transformed into
actionable information.
 Explore the data.
 Prepare the data by cleaning and modifying as necessary
applying rules.
 Build model, verify validity, deploy and measure results.

How US Life Insurers Use DM
1. Ideal underwriting is expensive with insistence on blood and urine reports for
setting price. DM can eliminate applicants who are low risk and hence can be
spared of tests.
2. Determine attributes of competitor’s customers.
3. Speed up, streamline and standardize underwriting process.
4. Use third party data in conjunction with traditional underwriting for accurate
predictions. They buy data from pharmacies about prescriptions.
5. Weed out bad/unprofitable customers from good ones and find out when is a
customer about to leave.
6. They also use data mining to recruit better underwriters.
7. No legal issues as such as DM is used mainly for triage.
8. Modeling mortality rate is not practical, hence they model underwriting
decisions.
9. Fraud detection
10. Asset Liability Management
11. Solvency Analysis
12. Screening of underwriters application for recruitment

Demo
1. Targeted Mailing – We demonstrate how to determine, from a list of potential
customers, ones most likely to buy our products, from their given attributes and
past purchasing behavior of similar customers for focused marketing.
2. Forecasting – We demonstrates how to predict sales and other business
indicators based on past data for better planning.
3. Market Basket analysis – We demonstrate how to determine products that are
being purchased in bundles by customers for cross selling.
4. Sequence analysis – We demonstrate s.
.

Targeted Mailing
1. Attributes of existing customers are analyzed and model is trained.
2. A user specified % of records is set aside for testing at later stage.
3. Multiple algorithms are applied to same data ex: Decision Tree, Naïve Bayes,
Cluster etc.
4. Prospects likely to buy insurance along with the probability is compared across
algorithms for models validity and usefulness.
5. Lift offered by each algorithm is analyzed by comparing the models with actual
production data set aside in testing phase.
6. Ascribe a consistent holdout seed value for consistent results (due to keeping
aside records for testing at later stages).
7. A number of parameters are available for customized prediction.
8. Input columns can be continuous or discrete, though few models do not support
all ex. Naïve does not support continuous columns.
9. Prediction value based on existing customers can be easily applied to an
external table with prospective customers with similar attributes.
.

Forecast
1. Time period has to be decided upfront on which the forecast will take place.
2. The time periods should conclude at same point and there should not be any
gaps. Gaps if any can be removed automatically through options in mining
framework, namely previous value, mean etc. in addition to by changing
source.
3. Time Series algorithm is used for forecast. It supports both short term ARTXP
and long term ARIMA as well as a blend and a host of other options for better
accuracy and customization.
4. According to TS algorithm, large fluctuations are repeated and amplified.
5. For new products or newly introduced region which don’t have enough
historical data we can average out the rest of products/regions, forecast and
apply to new dataset. Here you would need to aggregate the data to be applied
collectively to different products or regions. Target is filtered model with data for
a newly introduced table. In case of Cross Prediction use parameter
REPLACE_MODEL_CASES.
6. If new data arrives that needs to be automatically considered, use parameter
EXTEND_MODEL_CASES.

Market Basket Analysis
1. Inbuilt MS Association model does duty to aid in cross selling.
2. Support and Probability parameters are available for better control. Both are
specified in %. Support is setting the rule of minimum occurrences. Setting
probability means specifying the minimum probability for condition to be true.
Importance is calculated by engine based on usefulness of rule. Ex: setting
Support to .01% means only those cases will be returned which occur in at
least 1 out of every 100 records and remaining associations will be ignored.
3. By using Singleton prediction query, its possible to recommend an additional
product to a customer given a/set of complementary product/s he/she buys.
This recommendation comes with probability and support for better decision
making. Of course this can be automated to show recommendation for each
customer in the database in one go based on product bundles frequently
purchased.
.

Sequence Clustering
1. Inbuilt MS Sequence Clustering model does duty to find out the sequence of
purchases in a single transaction on internet.
2. Support and Probability parameters are available for better control. Both are
specified in %. Support is setting the rule of minimum occurrences. Setting
probability means specifying the minimum probability for condition to be true.
Importance is calculated by engine based on usefulness of rule. Ex: setting
Support to .01% means only those cases will be returned which occur in at
least 1 out of every 100 records and remaining associations will be ignored.
3. By using Singleton prediction query, its possible to recommend an additional
product to a customer given a/set of complementary product/s he/she buys.
This recommendation comes with probability and support for better decision
making. Of course this can be automated to show recommendation for each
customer in the database in one go based on product bundles frequently
purchased.
.

Improving Customer
Satisfaction
1. We can go for Neural Network when we have no prior expectation of what data
will show. We will use this data to suggest improvements in a call center with 30
days of data available to us. The questions that will be answered is: what
factors affect customer satisfaction and what can call centers do to improve
customer satisfaction?
2. Once we have the answers we can use logistic regression model for
predictions. It can be used to do financial scoring and predict customer
behavior based on customer demographics.
.

How we can help
 Start with a specific module say targeted mailing.
 Collect relevant structures and data. Data can be
massaged for privacy and security.
 Have the marketing department spell out a
problem/hypothesis.
 Analyze the data in view of the
hypothesis/requirement and collect more data if
necessary.
 Clean the data modify as necessary and apply
algorithm towards arriving at a solution.
 Present the findings along with accuracy validations.
 If useful start the work formally after signing
necessary contracts.

Value ACS Would Add
 We have vast experience in implementing data
warehouses and data mining models in companies.
 We have the skills to be able to work with Big Data
(Hadoop) source system (if its required).
 We are based in Lucknow and will give you the attention
you deserve.
 Vast experience with configuring different kinds of
software since we make them and hence can help you
with the necessary software validation, verification and
audit trails.
 We also understand SAS and can hence also help you
with the organizing of assay results in SAAS compatible
data sets for filing with the regulatory authorities.
 Team comprises DWH experts with vast experience thus
rendering it a complete look.

Questions/Comments?
Our contact details:
Ankur Khanna: Director Technical
+91 945 166 8432
Dr Vibhor Mahendru: Director Business Development
+91 800 536 5132
THANK YOU

Data Mining in Life Insurance Business

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