Sahara lifedemo acs_client

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.Accommodator Consultancy Services Lucknow

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.

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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.

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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.

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Forecast (visuals)


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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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Market Basket Analysis (visuals)


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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.

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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.

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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

Date post:	29-Jun-2015
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