1. A Decision Support Based on Data Mining in e-Banking Irina Ionita Liviu Ionita Department of Informatics University Petroleum-Gas of Ploiesti

2. Topics Data mining Stages in the lending process Credit scoring Lending process automation SADM – the automated system based on data mining SADM and e-banking

3. Data Mining Data mining refers to automated discovery of unknown patters and relations in enormous data warehouses in order to use this knowledge in decision making process.

4. Data Mining algorithms Supervised algorithms: Simple and multiple regression; k-NN algorithm; Artificial neural networks; Decision trees; Naive Bayes classification algorithm. Unsupervised algorithms: Rule association; Clustering.

5. Stages in the lending process A customer applies for a loan; Loan officer collects the preliminary data from customer and computes a score; Customer is informed about the obtained score and the options he can make; In favorable case, the customer meets the preconditions and completes a credit application form; The data are stored in the credit bureau database and analyzed; A credit agreement and/or an insurance certificate is signed, if it is required; After data analysis and credit scoring, a decision is made and the customer receive the answer for it’s request; If the bank’s decision is favorable, an account is created and fed with the established amount.

6. Credit scoring A bank is able to choose eligible customers by calculating the score based on various parameters such as: monthly income, credit rate, number of dependents, years of employment, years on actual addresses etc. By implementing a scoring model, each customer receives a “note”, based on a set of variables that takes into account all available information in the database, not only the negative one.

7. Lending process automation

8. SADM – the automated system based on data mining The proposed system owns eleven modules: –data acquisition module; –preprocessing module; –preprocessing methods module; –adjustment classification criteria module; –data mining module; –credit scoring module; –prediction/classification module; –interpretation module; –comparison module; –adjustment module; –decision module.

9. The automated system architecture

10. The hybrid credit scoring model RegLog1 – a regression logistic model; RegLog2 – a regression logistic model; NN – a neural network model; DT – a decision tree model.

11. SADM and e-banking

12. SADM – graphical user interface

13. Conclusion The proposed system (SADM) can be considered a useful tool both for banks and clients. The banks can minimize the credit risk and the clients receive objective answers. A new approach of decision support is presented by designing an automated system based on data mining techniques used in banking area. A hybrid credit scoring model is proposed for computing the probability of default associated to bank’s customers. SADM can be integrated on a bank’s website and customers can access it after they accept the bank’s conditions.

14. Thank you for your attention!