Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Intelligent systems in banking industry: survey and future Rimvydas Simutis Kaunas University of Technology, Lithuania Penkių Kontinentų Bankinės Technologijos (BS/2), Lithuania

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Outline   What are intelligent systems?   Why these systems are “hot” now?   Competitive edge and intelligent systems   Intelligent systems techniques   Where are we today?   Some application in banking sector   What is the future?   BS/2 and intelligent systems

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13   What are intelligent systems?

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Intelligent systems – a lot of discussions but none unified definition till now. Definition in our applications: Intelligent systems – systems which combine an a priori knowledge and real-time information (knowledge) for decision making A priori knowledge Real – time knowledge Extendedknowledge Decisions Intelligence Knowledge used 0 1

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Three important components for design of intelligent systems Human Experts/Fundamentals – DATA – Evolution A priori knowledge Real – time knowledge ExtendedknowledgeObjectives Humanexperts ImprovementsthroughEvolution Decisions DATA Fundamentals

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Why these systems are “hot” today?

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Computation power of today's computers is strong and is improving Basic techniques for design of intelligent systems are already developed Global communication and data basis systems are powerful Prices for human intelligence are increasing and are coming closer to “silicone intelligence” prices

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Computation power and point of “singularity”

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Competitive edge and intelligent systems

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Starting 2005 traditional IT Co are like utility companies (water supply, electricity supply - Internet supply building material warehouse - data warehouse..etc) Traditional IT Utilities (no competitive edge) Breakthrough in competition and innovation 2005 IT Companies IT based on Intelligent Systems

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Task for innovative IT companies: switching part of recourses for developing of software and tools for automatic knowledge extraction and decision making A priori knowledge Real – time knowledge ExtendedknowledgeObjectives Knowledgeextractiontools ImprovementsthroughEvolution Decisions DATA Knowledgeextractiontools Decisionmakingtools

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Intelligent systems techniques

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from human experts Extraction knowledge from human experts   Formal models   Expert systems   Fuzzy expert systems, hierarchical fuzzy systems   Neuro – fuzzy systems   “Cloning” of human experts

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from human experts Extraction knowledge from human experts   Fuzzy systems

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from human experts Extraction knowledge from human experts - Experts are expensive; - Experts are always busy; - Experts are ‘critical recourses’; - Experts can leave the company;   ‘Cloning’ of human experts

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from human experts Extraction knowledge from human experts   ‘Cloning’ of human experts

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from data Extraction knowledge from data   Data mining techniques, advanced visualization   Decision tree techniques   Extraction of Fuzzy rules   Case based reasoning   Artificial neural networks   Self-organizing neural networks   Hierarchical learning of structures (Jeff Hawkins) --- Every day, week or month ---

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from data Extraction knowledge from data   Decision tree techniques

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from data Extraction knowledge from data   Extraction of fuzzy rules

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from data Extraction knowledge from data   Artificial neural networks

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Extraction knowledge from data Extraction knowledge from data   Self-organizing neural networks

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Techniques for Decision Optimization Techniques for Decision Optimization   Traditional numerical optimization methods   Stochastic optimization   Evolutionary/genetic programming   Swarm intelligence, ant colony optimization   Multi-agent systems

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Techniques for Decision Optimization Techniques for Decision Optimization   Swarm intelligence, ant colony optimization

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Where are we today?

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Some application in banking sector   In all applications only one or two components of the intelligent systems frame are used

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 ApplicationPrediction with ANNClassification with ANNClustering with SOM Marketing and Sales Forecasting customer response (Bounds, 1997); Market development forecasting (Wang, 1999); Sales forecasting (Kong, 1995); Price elasticity modeling (Gruca, 1998) Target marketing (Zahavi,1997); Customer satisfaction assessment ( Temponi, 1999); Customer loyalty and retention (Mozer, 2000, Smith, 2000) Market segmentation (Reutterer, 2000); Customer behavior analysis (Watkins, 1998); Brand analysis (Reutterer, 2000); Market basket analysis (Evans, 1997); Storage layout (Su, 1995) Risk Assessment and Accounting Financial health prediction (St. John, 2000); Credit scoring (Jensen, 1992); Insolvency prediction (Brockett, 1997); Compensation assessment (Borgulya, 1999); Bancrupcy classification (Wilson, 1997); Credit scoring (West, 2000, Long, 2000); Fraud detection (Holder, 1995, He, 1997); Signature verification (Ageenko, 1998) Credit scoring (West, 2001) Risk assessment (Garavaglia, 1996) Signature verification (Abu-Rezq, 1999)

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Business Policy, Managements and Strategy Evaluating strategies (Chien,1999); Assisting decision making (Wu, 1999) Impact of strategy on performance (St. John, 2000); Impact of management practices on performance (Bertels, 1999) Impact of strategy on performance (Biscontry, 2000); Assisting decision making (Lin, 2000) FinanceHedging (Hutchinson, 1994); Futures forecasting (Grudnitski,1993); FOREX forecasting (Leung, 2000); Investment management (Barr, 1994); Stock trend classification (Saad, 1998); Client authentication (Graham, 1988); Bond rating (Dutta, 1993) Economic rating (Kaski, 1996); Interest rate structure analysis (Cottrell, 1997) Mutual fund selection (Deboeck, 1998) ApplicationPrediction with ANNClassification with ANNClustering with SOM

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 uzzy-enhanced score card system used in BMW bank Fuzzy-enhanced score card system used in BMW bank

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 Portfolio management application using multi-agent system technology WARREN

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 What is the future?

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13   All depends on I_Price_Ratio I_Price_Ratio = Silicone Intelligence Price/ Human Intelligence Price   Low I_Price_Ratio will stimulate development of advanced tools and software for intelligent systems and their applications   Combination of all three components of intelligent systems technique is crucial in the future (crucial for high IQ !)   Development of user friendly tools for design of intelligent systems is highest priority (basic methods are known)

Institute of Automation and Control Systems KTU BS/2 Conference, Vilnius, 2008 June 13 BS/2 and intelligent systems   Application of multi-agent systems and optimization techniques for decision making   Application of artificial neural networks, support vector machines and associative neural networks for knowledge extraction from data   Combination of expert knowledge/data knowledge for warehouse operations