1. Bucharest, 10-February-2004 Neural Risk Management S.A. Scoring solutions Making full use of your data

2. Bucharest, 10-February-2004 Agenda 1.Neural Risk Management & Neural Technologies 2.Neural computing 3.Scoring solutions 4.Scorecard development 5.Database consistency analysis 6.Optimum cut-off value 7.Applications of Decider 8.Business benefits of Decider

3. Bucharest, 10-February-2004 NRM & NT Neural Risk Management  Independent company, opened in 2002;  Neural Technologies local business partner; Neural Technologies  Privately owned company in Hampshire, UK  Focus on telecom & financial sectors  Strategic Partnerships  India, Egypt, Mexico, Singapore, Malaysia & Indonesia, etc.  5+ years experience of credit scoring & risk management  Unique solutions delivering huge savings  Solution not technology  Used by leading telecom & financial companies

4. Bucharest, 10-February-2004 Neural Computing  It has the capacity to learn from experience and apply what has been learnt;  A neural system decision is purely objective and not subjective;  It can understand relationships between variables hidden deep within data;  The ability to cope with little or incomplete data;  It develops highly accurate scorecards and models – it reaches 30-40% increased bad debts prevention;  Our solutions are based on neural computing  Strengths of neural technology:  Pattern Recognition e.g. customer profiling  Prediction e.g. customer purchasing patterns  Anomaly Detection e.g. fraudulent trading

5. Bucharest, 10-February-2004 Scoring solutions  Scorecard development - based on customers behaviour history for Romanian specific market, bank or a certain credit product.  Database consistency analysis - analyse the separation from the two classes (GOOD & BAD), characteristics relevance and other statistical charts. Our software application: Decider™

6. Bucharest, 10-February-2004 Scorecard development Step 1: Cleaning, remodeling and randomizing the initial database; Step 2: From the main data base we remove 10% for the final EVALUATION database; Step3: From what is left, we remove 20% in order to build the VALIDATION database and the other 80% becomes the TRAINING database; Decider is building the model by processing the training/validation files (called Epochs) hundreds of times until the algorithms results difference is minimised …and everything takes several hours including the database consistency analysis! Step 4: Final evaluation by introducing the EVALUATION file and computing the score - “batch” - based on the new scorecard;

7. Bucharest, 10-February-2004 Database consistency analysis The higher is separation between the two classes, the better is the consistency of the database and we can obtain a better scorecard. If the two curves “Gauss bell” are slimmer and apart the separation is higher, in other words the grey area is decreasing. Kolmogorov-Smirnov coefficient is the maximum difference between the GOODs (blue curve) and the BADs distributions by score (red curve). Quality of data history – Kolmogorv Smirnov chart

8. Bucharest, 10-February-2004 Database consistency analysis Sometimes, information like salary, time at work, age, dependants, phone type or others can have no relevance for a certain credit product and we should change them with others. Decider can analyse also the sensitivity for each of the characteristics propensity to fraud depending on its own values. Characteristics relevance

9. Bucharest, 10-February-2004 Database consistency analysis Scorecard performance – Lorenz chart „Lorenz Chart shows how the proportion of the „Bads” accepted varies with the proportion of the „Goods”. The red line demonstrates the scorecard performance on the selected data-set and the Blue line indicates the levels where applicants are accepted at random. The statistical performance (Gini Co-efficient) is a measure of the „area” between the two lines.

10. Bucharest, 10-February-2004 Optimum cut-off value – Trade-off chart Cut-off Value No. Goods No. BadsTotalBad Rate Good / Bad Ratio % Goods % Bads [FN]% Total % Goods [FP]3:1 0.5250112614.2122.775.764.0743.524.2490.89 0.55243102533.9524.373.643.742.1726.3690.64 0.624082483.233072.732.9641.3327.2790.96 0.6523572422.8933.671.212.5940.3328.7990.86 0.723272392.9333.170.32.5939.8329.790.63 0.75 22752322.1645.468.791.8538.6731.21 92 0.822342271.7655.867.581.4837.8332.4290.79 0.8522032231.3573.366.671.1137.1733.3390.84 0.920922110.95104.563.330.7435.1736.6790.28 Over 90% accuracy for neural scorecard! Trade-off chart

11. Bucharest, 10-February-2004 Applications of Decider™ Decider can be used throughout the total customer life cycle  Scoring is not just about risk assessment, it can be used for:  Marketing applications  Reducing customer attrition or churn  Cross-selling products and services  Streamlining litigation cost and collection procedures

12. Bucharest, 10-February-2004 Applications of Decider™ By Industries:  Banks;  Consumer finance companies;  Telecom operators;  Leasing  Insurance  Retailers  Utility companies.

13. Bucharest, 10-February-2004 Business Benefits of Decider™  Minimum resources for maximum revenue  Identifying profitable customers  Attracting and retaining quality customers  Identifying suitable payment methods for the customer  Minimising bad debt  Improved identification of frauds  Saving marketing effort for worthwhile prospects  Building long term relationships with quality customers