An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers Nan-Chen Hsieh

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1 An Integrated Data Mining and Behavioral Scoring Model for Analyzing Bank Customers Nan-Chen Hsieh Xin Ji (Jane) Institute of Financial Services Analytics University of Delaware

2 Agenda Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

3 Introduction Target profitable customers Based on individual needs or purchasing behaviours

4 Introduction Most existing data mining approaches Discovering general rules Predicting personal bankruptcy Credit scoring Account data of customers Credit transactions Discover patterns in the data to provide customer marketing strategies

5 Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

6 Recency Frequency Monetary Repayment behavior Customer purchasing histories Self-organizing map Demographic & geographic characteristics

7 Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

8 Desired outcome Segment customer into three profitable groups Revolver users Roll over bills Pay considerable interest on outstanding balance Transactor users Pay in full Not incur any interest payments or finance charges Only transaction revenue Convenience users Periodically charge large bills Install payments over several months Pay significant amounts of interest

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10 Recency (R) Frequency (F) Monetary (M) Average time distance between the day of makes a charge and the day pays the bill Average number of credit card purchases made Amount of consumption spent during a yearly time period Repayment Ability (RA) = no. of months without delayed pay off no. of months of holding the card Revolver user Convenience user Transactor user

11 Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

12 Data sets Provided by a major Taiwanese credit card issuer Two data sets Effective credit card account information of 158,126 customers until June 2003 Over 20 millions individual transaction records for these accounts from January 2000 to June 2003 After data pre-processing, 32 attributes 10 character attributes 22 continuous attributes

13 Assessing the Self-organizing Map (SOM) for customer behavioural scoring First phase rough estimation Capture the gross data patterns Second phase tuning phase Adjust the map to model the fine features of the data

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16 Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

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18 Introduction Behavioural scoring model Two-stage behavioural scoring model Analyzing behaviour of customers Assessing the Self-organizing Map (SOM) for customer behavioural scoring Determining relative important variables Apriori association rules Conclusion How To Get Serious About Bank Customer Segmentation

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20 Conclusion Neural Networks Association rule inducer Divide existing customers into profitable groups

21 Article by industry leader How To Get Serious About Bank Customer Segmentation If you are a start-up business, the first thing you do is to conduct a detailed segmentation analysis to outline the demographics, psychographics and importances of the customers they seek to target. Unlike start-up businesses, banks already have an idea of who their existing customers are. Banks can use customer segmentation to deepen their understanding of their existing customer base and identify new customer segments that may be growth opportunities for them.

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