Data Mining Proposal for Sahara Life Insurance

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

Data Mining Definition
 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”. Its part of
data warehousing which is defined below:
 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 agents
performance.

Proven Uses of Data Mining in
Life Insurance Industry
1. Rate Setting.
2. Acquiring new customers.
3. Attrition analysis/Retaining customers
4. Developing new product lines
5. Creating geographic exposure reports
6. Detecting fraudulent claims
7. Performing sophisticated campaign management
8. Estimating outstanding claim provision
9. Forecasting, planning and budgeting..
10. Understanding customer preferences, their payments of
premium and customer queries.
11. Performance Evaluation of Insurance Agents

1. Rate Setting
 Rate setting of each policy is an important problem for the
actuary. Traditionally likelihood and size of claim determine the
rate.
 Attributes of existing customers are automatically analyzed
(iteratively) 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 derived.
 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 can be easily
assigned a rate for known risk, profit and behavior.

Risk Assessment for Rate Setting

Price Optimization Possible
 We can develop specialized software that would let you figure
out which groups of customers are more likely to accept a
price increase and which are more likely to shop around for a
new policy.
 A 2013 marketplace survey done by Earnix, a global leader in
price optimization, found that 26 percent of all auto insurance
companies and 45 percent of the large insurance companies
(more than $1 billion in annual revenue) in North America
currently optimize their prices. An additional 36 percent of all
companies surveyed said they plan to do this in the near
future.
 We can also help you set appropriate rates for crucial urban
customers by using Big Data that we specialize in mining.
 Socio economic data associated with geospatial data would be
utilized for price optimization and informed rate decision.

Price Optimization Prerequisites
 Data Quantity and Quality of claims and customer should be
good.
 High Performance Analytics is required to process large amount
of data and evaluate complex what if scenarios. Special
software is required with nice visualizations.
 Competitive Intelligence – competitive landscape, pricing
strategies and customer buying preferences and demographics.
 Data exploration - Data needs to be thoroughly analyzed
through a variety of tools.
 Predictive Modeling– Insurers must use analytical tools to
perform what-if simulation and scenario testing to forecast future
behavior and improve the underwriting performance of the
insurance company.

2. Acquiring New Customers
 Data Mining is used to maximize marketing campaign’s
ROI by targeting customers with attributes indicative of
greater loyalty and better profits over the lifetime of
customer’s stay with the company. This ensures optimum
use of limited marketing budget which research shows
can be up to 15% of the total cost of insurance.
 Data mining can also be used to identify best time, best
season and best media to reach out to potential
customers. If the data is not being captured, we can help
setup the system to capture this data which can be very
useful.

3. Attrition Analysis/Retaining
Customers
 Customer attrition rate is high in insurance industry. It is far
more expensive to acquire a new customer than to retain
existing ones. Hence makes sense to retain customer.
 Data Mining can easily lead to factors that contribute to
customer attrition and predict customers likely to attrite so they
can be retained through targeted campaigns. Preventing
policy lapse is focus of all such studies. Neural network and
DT is more likely to yield good results.
 The graph shows classification.
»

4. 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 can
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.
 Gen Re in Germany taps into the vast pool of disability
data, to determine which occupations result in disabilities
for better risk assessment and appropriate products.

5. 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.
 Data Mining tools provide for such visual reports that
facilitates quick and easy decision making.

6. Detecting Fraudulent Claims
 Data Mining facilitates fraudulent claims detection.
Possible saving from detecting fraud fully justifies the
investment required.
 One of the techniques employed is to profile existing data
and compare against old fraud data to accurately detect
likelihood of a bogus claim.
 Blue Cross Blue Shield saved an estimated $4 million in
1997 alone on account of saving from fraud detection.

7. Estimating Outstanding Claims
Provision
 In the event of huge exposure spread out among large
number of individual policies as opposed to 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.

8. 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.

9. Forecasting and Budgeting
 Time series modelling can help management with their
budgetary requirements.
 A report can be made that highlights the relationship
between demand as experienced by Sahara Life and a
specified set of explanatory variables pertaining to
general economy to assist forecasting. Such variables
are freely available and can help with accurate forecasts.
 A number of modelling techniques can help management
do the general planning related to finance, HR and
operations etc.

10. Understanding customer preferences,
payment of premiums & customer queries
 Companies have large database but purchase pattern is
usually hidden and can be uncovered using DM easily.
Ex. Chi Square Automated Interaction detection (CHAID)
for identification of profitable customers likely to persist,
predicting future behavior and enabling firms to make
proactive knowledge based decisions.
 Can be used to segment customers and then use these
segments judiciously for increasing business.

11. Evaluating performance of Agents /
Brokers
 Agents can be scored on various factors, including:
■ Early lapse experience and/or policies not taken up
■ Comparisons of disclosure rates identifying agents or
brokers that are good at encouraging policyholder
disclosure
■ Sales figures, such as volumes, policy size, etc.
Models are emerging that help Insurance companies manage
agents and provide incentives.

How US Life Insurers Use DM
1. Ideal underwriting is expensive with insistence on blood and urine reports for
setting price. DM can identify people at low risk who don’t need such tests.
Alternatively high risk customers can be identified who need extensive 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. Use data mining to recruit better underwriters with suitable traits by screening
their applications.
7. No legal issues are faced as it facilitates effective and efficient decisions.
8. Modeling mortality rate is impractical, hence underwriting decisions are
modeled.
9. Fraud detection.
10. Asset Liability Management.
11. Solvency Analysis.

Data Mining Process (in brief)
1. Identify Business Problem
(ex. not enough referrals availed)
2. Transform Data into Information
(collect n clean data n apply rules)
3. Take Action on Information
(design campaign for such customers)
4. Measure the Outcome
(measure campaign and remodel)

Data Mining Process (in detail)

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
and applying necessary rules.
 Build model, verify validity, deploy and measure results.

Demo
1. Campaign for 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 important
ratios /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/upselling and
controlling customer churn.
4. Sequence Analysis – We demonstrate how order of navigation on website can
be determined and how it can be leveraged for better user experience.
5. Call Center Improvement – We demonstrate how Neural Network algorithm
may be used to identify hidden patterns in previously unknown information.
.

Targeted Mailing Campaign
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.
.

Targeted Mailing
Campaign(visual)
If insurance buyer has so many attributes believed to be in play
by Campaign Manager, DM algorithm determines the order of
importance of such attributes for campaigners to concentrate on.

Targeted Mailing (Decision
Tree)
Decision Tree rules as determined by the Algorithm.
Here complex data is split into simple tree by taking into account only top few
important attributes, rest are disregarded. Darker the node, stronger the case.
Here people with 0 cars, <44 years of age and region <> ‘North America’ are likely to buy insurance

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.

Forecast (visuals)
.

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.
.

Market Basket Analysis
(visuals)
.
Example taken from retail industry sample data !

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 Call Center
Customer Satisfaction
1. We can go for Neural Network when we have no prior expectation of what data
will show (i.e. if the call center has not had any analysis done so far). 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.
.

Improving Call Center
Customer
Satisfaction(visual)

Our offerings
1. Data Mining in Insurance – Once the business
problem/challenge has been shared with us, we
analyze the problem, identify how useful data mining
would be, and design the entire data mining solution.
2. Cloud Services – We specialize in helping our
customers move their SQL Server databases to Cloud
and suggest appropriate package based on usage and
future growth.
3. Data Cleansing and Migration Services- We specialize
in cleansing data and data quality services.
4. Text Mining – We are capable of extracting data from
social media sites and any other web sites for further
use.
5. Big Data – We can help our clients use Big Data for
decision making.

Why ACS?
 We have vast experience in implementing data
warehouses and data mining models in companies such
as Fidelity, Capital One, GMAC, UTC, GE, VWG and FM
Global Insurance.
 We have the skills to be able to work with Big Data
(Hadoop).
 We are based in Lucknow and will give you the attention
you deserve.
 Dedicated SME will be involved in the project along with
data mining experts.
 We believe in delivering value for money solutions and
cost would be the lowest and result based.
 Our engineers are multi faceted and can help you with
your other data related problems as well.

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

Data Mining Proposal for Sahara Life Insurance

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