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
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
Introduction Target profitable customers Based on individual needs or purchasing behaviours
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
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
Recency Frequency Monetary Repayment behavior Customer purchasing histories Self-organizing map Demographic & geographic characteristics
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
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
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
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
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
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
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
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
Conclusion Neural Networks Association rule inducer Divide existing customers into profitable groups
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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