Sommaire : projet TRACK

1. Sommaire : projet TRACK • Présentation : le consortium et les objectifs • Formalisme : • Données temporelles • Fusion : multi-classifieur semi supervisé • Applications • CEE : prévision des taux de change • Nationwide : offre de carte de crédit • Banco Santander : utilisation de carte de crédit • Caisse d’Épargne : optimisation ATM • Conclusion : intégration pas à pas des connaissances a priori

2. Présentation : consortium Track Project Utilisateurs Concepteurs R & D Nation Wide Anite system Instituto de Tecnologia del Conocimiento Caisse d’Epargne ISOFT Banco Santander Ibermatica

3. Common Interface Tools Output API. F U S I O N DT results NN results BA results Decision Trees Neural Network Statistical Approach Tools input API TRACK Data Base Plate-forme Décisionnelle : Combinaison d’agents / experts Instituto Universitario de Tecnología del Conocimiento Universidad Complutense.

4. Fusion : outils mathématiques et résultats: Mesurer les liens entre les informations Compression et réduction du volume d’information FUSION Concept de la fusion pour le data mining : Cumuler des pièces d’information (EAReL group) Dans quel but ? Synthétiser de multiple information Faire la synergie objective entre différents opinions Limiter les erreurs de décision (True Knowledge) Système Optimisé par fusion d’information Fusionner des information issues de résultat de Software Détecter des gènes de comportements dans le temps

5. Modèle  : min(Eexterne() + Einterne() ) Formalisme : management des données temporelles ... Time t0 t1 t2 T+1 Ft F’t C1 t-1,t t,t+1 C2 t-1,t+1 C3 Prévision : Filtre de Kalmann

6. Formalisme : les données R experts : données symboliques Espace discret L £Õr=1,…,R |Lr| Matrice carrée L x L N mesures sur T périodes Projection

7. Modèle de fusion P(c) = M! / (n1! n2! ... nL!) . l=1,...,L Plnl M états sur L niveaux d’énergie : Algorithm boosting : Stochastic Mechanic Iteration (Boosting – Bartlett) 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 Step T0 Step T-> 0 Algorithm Energy Cost = U Minimum Energy = U*

8. Résultats "Generating overlapping clusters", [Cole-Wishart-71] "An Improved Algorithm for the Jardine-Sibson Method of Generating Overlapping Clusters".

9. The TRACK Project Active Decision Support Toolkit for the Financial Sector Application Time Series Data Analysis

10. The TRACK Project Active Decision Support Toolkit for the Financial Sector Time Series Data View (6) Dollar Against Yen

11. The TRACK Project Active Decision Support Toolkit for the Financial Sector Time Series Application Exchange Rate between Money Against Models estimating Exchange Rate during 218 days Model Characteristic Lower: 5 standard Volatility Models Model Characteristic Upper: 5 standard Volatility Models +A Priori Knowledge Model Characteristic Sub: 1 being the average of all models Final CEC Review

12. Root Mean Square Error Mean Absolute Error Mean Absolute Percentage Error Root Mean Square Error On Realisation The TRACK Project Active Decision Support Toolkit for the Financial Sector Track Input Source Data Content: the *zi.csv file Scoring each volatility Exchange Rate money-model Statistic Variables Adding Knowledge: Temporal Handling Variables

13. Temporal Variable Dif2 Static Variables MoyDif2, CumulDif2 The TRACK Project Active Decision Support Toolkit for the Financial Sector Time Series Normalisation: Error Trend (TTDM)

14. The TRACK Project Active Decision Support Toolkit for the Financial Sector 10 analyses Experts add a priori knowledge about what do they want Experts with specific questions: Expert-Model:Is there a set of model better than other ? Expert-Money: Is there a set of money having special behaviour ? Expert-Info: Could we separate models according to their characteristics ? Experts looking for variables explaining behaviour: Expert-Global:Scoring the Statistic variables (Good or Bad) Expert-Cluster: 3 classifications supervised (2, 3 and 4 classes) Expert-Alice: 3 classifications with label meaning (5, 6 and 9 classes)

15. The TRACK Project Active Decision Support Toolkit for the Financial Sector Track Output A Priori Data Content: the *of.csv file

16. The TRACK Project Active Decision Support Toolkit for the Financial Sector Evaluation des analyses

17. Comparaison : SOFI & Cluster 4C

18. Modèles retenus

19. Error Estimation Error Trend The TRACK Project Active Decision Support Toolkit for the Financial Sector Description fusion *so.csv file: Class number 3

20. The TRACK Project Active Decision Support Toolkit for the Financial Sector S.O.F.I. Results Dollar Against Yen Deutsche Mark Against Yen

21. The TRACK Project Active Decision Support Toolkit for the Financial Sector Conclusion de l’expérience Quels sont les modèles les mieux adaptés à l’évaluation ? - Caractéristiques des taux de change - Dans le cas où plusieurs modèles sont considérés bons, le meilleur est la moyenne des modèles

22. Nationwide End User Application Nationwide’s Questions: Which of our customers should be offered credit cards? Which of our customers will be profitable as credit card customers?

23. Banco Santander • Which are the characteristics of the customers who cancel their credit/debit cards? • And which are the characteristics of the canceled cards? • How can we analyze credit/debit card customer behavior in order to minimize the number of cancellations? • Regarding Client Data the universe to try should include: • Particular people (no companies) • All titular of Credit/Debit cards • Historical information (15 Months)

24. Essential complements to open interchange fee strategy bank trade evolution indirect profitability direct profitability services appropriated by offices personnel Caisse d’Épargne : ATM

25. Activity analysis Act 20 • Customer activity on other banks : • + 18 % per year • Non customers activity : • from + 11 to + 7 % . Clients 18 non Act 16 . clients 14 12 10 8 6 4 2 0 95 96

26. The TRACK Project Active Decision Support Toolkit for the Financial Sector Conclusion

27. The TRACK Project Active Decision Support Toolkit for the Financial Sector Conclusion Pour faire du datamining : - le recueil des données laborieux est la clé de la réussite - les outils pour manipuler les données - des experts métiers : savoir quoi sur quoi, qui et quand.