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Data Mining in Banking

Data Mining in Banking. CS548 Xiufeng Chen. S ources.

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Data Mining in Banking

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  1. Data Mining in Banking CS548 Xiufeng Chen

  2. Sources • K. Chitra, B.Subashini, Customer Retention in Banking Sector using Predictive Data Mining Technique, International Conference on Information Technology, Alzaytoonah University, Amman, Jordan, www.zuj.edu.jo/conferences/icit11/paperlist/Papers/ • Dr. B. Subashini Data Mining Techniques and its Applications in Banking Sector. Website: www.ijetae.com • Boris Kovalerchuk, EvgeniiVityaev, DATA MINING FOR FINANCIAL APPLICATIONS Petra Hunziker, Andreas Maier, Alex Nippe, Markus Tresch, Douglas Weers, and Peter Zemp, Data Mining at a major bank: Lessons from a large marketing application http://homepage.sunrise.ch/homepage/pzemp/info/pkdd98.pdf • Rene T. Domingo, APPLYING DATA MINING TO BANKING http://www.rtdonline.com/BMA/BSM/4.html • Predicting Returns from the Use of Data Mining to Support CRM http://insight.nau.edu/downloads/CRM%20Mining%20Returns%20Paper.pdf

  3. Purposes of Data Mining in Banking • As banking competition becomes more and more global and intense, banks have to fight more creatively and proactively to gain or even maintain market shares. • 1. Discover new customers. Clustering different customers into some clusters. • 2. Remain customers. Especially the VIP customers. In general, 20% of customers bring 80% of revenues. Using association rules can find association between services. • 3. Risk Management. Using decision tree to classify high risk people.

  4. Bank of America • Bank of America identified savings of $4.8 million in two years (a 400 percent return on investment) from use of data mining analytics. (source: Bank of America) • This analyzing method was used to allow Bank of America to detect fraud and find eligible low-income and minority customers to ensure B of A’s compliance with the Fair Housing Act. source: Bank of America

  5. Flow of data mining technique Source: Customer Retention in Banking Sector using Predictive Data Mining Technique

  6. Preprocessing the data • Customer relationship management (CRM): • is a strategy that can help bank to build long-lasting relationships with their customers and increase their revenues and profits. Source: Predicting Returns from the Use of Data Mining to Support CRM

  7. CRM Source: Predicting Returns from the Use of Data Mining to Support CRM

  8. Discover new customers • k-Means: k-Means is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters. Each cluster has a centroid (center of gravity). Cases (individuals within the population) that are in a cluster are close to the centroid. For example, segment customer profession data into clusters and rank the probability that an individual will belong to a given cluster, and give them banking services they might need.

  9. Remain the number of customers • 1) measurement of customer retention; • 2) identification of root causes of defection and related key service issues; • 3) development of corrective action to improve retention. • Apriori: Apriori performs market basket analysis by discovering co-occurring items (frequent itemsets) within a set. For example, find the items or attributes which comes from the lost customers and specify their association rules. Therefore, the bank can take much care of those customers.

  10. Risk Management • In this approach, risk levels are organized into categories based on past default history. • Decision Tree technique can be used to build models that can predict default risk levels of new loan applications. • 1. Credit Cards 2. Deposits – Savings A/C • 3. Internet Banking 4. Housing Loans • 5. Term Loans 6. Cheque / Demand Drafts • 7. Cash Transactions 8. Cash Credit A/c(Types of Overdraft A/C] • 9. Advances 10. ATM / Debit Cards

  11. Conclusion • Data Mining techniques are very useful to the banking sector for • (1) better targeting and acquiring new customers, • (2) most valuable customer retention, • (3) automatic credit approval which is used for fraud prevention, fraud detection in real time, • (4) providing segment based products, • (5) analysis of the customers, • (6) transaction patterns over time for better retention and relationship, • (7) risk management and marketing.

  12. The End Xiufeng Chen

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