1. 1 Applications of Data Mining in Banking Maria Luisa Barja (Maria.Barja@ubs.com) Jesús Cerquides (Jesus.Cerquides@ubs.com) Ubilab IT Laboratory UBS AG Zurich, Switzerland

2. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 2 Outline Data Mining in Banking Application Areas Pitfalls in the Development of Data Mining Projects An Alternative: A Data Mining Framework Open Projects Summary

3. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 3 Data Mining in Banking Banks have many and huge databases Valuable business information can be extracted from these data stores Unfeasible to support analysis and decision making using traditional query languages Human analysis breaks down with volume and dimensionality Traditional statistical methods do not scale and require significant analysis expertise

4. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 4 Application Areas Four main areas Marketing Credit Risk Operational Risk Data Cleansing

5. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 5 Applications: Marketing Objective: Improve marketing techniques and target customers Traditional applications: Customer segmentation Identify most likely respondents based on previous campaigns Cross selling Develop profile of profitable customers for a product Predictive life cycle management: Develop profile of profitable customers X years ago Attrition analysis: Alert in case of deviation from normal behaviour Techniques:

6. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 6 Applications: Credit Risk Objective: Reduce risk in credit portfolio Traditional applications: Default prediction Reduce loan loses by predicting bad loans High risk detection Tune loan parameters ( e. g. interest rates, fees) in order to maximize profits Profile of highly profitable loans Understand characteristics of most profitable mortgage loans

7. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 7 Applications: Operational Risk Objective: Reduce risk originated by misbehavior Traditional applications: Credit card fraud detection Identify patterns of fraudulent behaviour Insider trading Detect sophisticated forms of insider trading and market manipulation.

8. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 8 Applications: Data Cleansing Objective: Detect outliers, duplicates, missing values,... Traditional applications: Data quality control Detect data values which do not follow the pattern Missing values prediction Predict values of fields based on previous values

9. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 9 Pitfalls in the Development of Data Mining Projects Data Mining is a process, not a package! Expensive, difficult to justify in first instance custom solutions Having substantial parts in common, most data mining projects provide custom solutions that: – Are more expensive – Take more time to develop – Have a higher risk of not being finished Ideally, use more than one technique to get a full view of the data

10. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 10 Proposed Alternative Identify the common functionality used for the development of data mining solutions Implement and pack this functionality in a way that it can be: – Reused in many projects. – Customized to meet the needs of each project. – Extended, so it grows with its usage.

11. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 11 Object Oriented Frameworks A framework is a reusable, “semi-complete” application that can be specialized to produce custom applications. Framework Ensemble Framework design expertise Programming language expertise OO expertise Domain expertise OO expertise Programming language expertise Framework usage expertise Coding expertise

12. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 12 Data Mining Framework: Benefits Reduces design and development efforts for building concrete applications. Lowers threshold for “proof of concept” data mining applications to be developed. Allows comparison of results across various methods. Facilitates selection of best method(s) for particular domains and business objectives. Eases extensibility to new types of methods and algorithms.

13. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 13 Data Mining Framework: General Architecture Project Management Technique implementation Component structure

14. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 14 Data Mining Framework: Component Structure Project Management Technique implementation DataProcessVisualization MetadataComponent

15. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 15 Data Mining Framework: Method Implementation Database Access Data UnderstandingData PreparationModeling Learning Data Project Management Component structure

16. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 16 Data Mining Framework: Modeling ClassificationClusteringRegression Prediction Description Learning Data Modeling roles

17. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 17 Data Mining Framework: Open Projects Design and development of: – A graphical user interface. – The prediction/description component (based on bayesian networks). – The clustering component. – The project management component. – The preprocessing component.

18. 5/11/98 ©1998 M.L.Barja, J.Cerquides Ubilab IT Laboratory, UBS AG 18 Summary Data Mining has emerged as an strategic technology for a large bank Several business areas where it can be applied Application development difficulties Proposed a solution based on OO framework technology