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 detection10. Asset Liability Management11. Solvency Analysis12. 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.

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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.

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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.

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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.

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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.

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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.

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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

Technology

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