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FT: Risk management in financial transaction systems

 

Responsible: ITC,  WST & WUT  & BUTE

Assistance:  PRO, NOM, all partners

 

Our objectives in this project is to research patterns in financial transactions to aid in the assessment of financial risk and detection of fraud in the following areas:

The goal of any pattern recognition systems is to make good predictions for new data, they have to have enough adaptability and generalisation to be able to deal with new patterns.  It is essential in the use of pattern detection systems, to have an understanding of data that creates the patterns, this is particularly important where linear techniques are used e.g. a rule-based expert system. But it is also very important for artificial neural networks, although they allow for fewer restrictions because of their general-purpose nature.

All fraud detection systems are essentially pattern recognition systems; they are designed to find fraudulent behaviour using only the fraction of the data that is available and accessible to the system. 

This research is an ambiguous attempt to build a reference for people involved in financial risk management, and to encourage research it this area. It is the purpose of this research to describe and analyse available data with the intention of using it to form rules or features for patterns in fraud detection systems. In the process, it will describe the environment of fraud detection.

 

Moreover, in this JPA emphasis will  be put on Portfolio Management on one hand, Insurance on the other hand. A key element of innovation we would like to introduce into the project are beyond current state-of-the-art portfolio management and risk management strategies. Existing mathematical or stochastical models usually do only compare different assets of one class or type. What is required here is an extension, enabling the comparison of different assets belong to different asset classes in precise mathematical terms. This is to be described as Homogeneous Portfolio Model. If the overall amount of investments made by a client is regarded as a portfolio, the following questions are usually raised:

1)   Is the customer's portfolio really tailored according to his real risk tolerance?

2)   What strategies have to be applied in order to optimise the portfolio?

3)   Is the portfolio's diversification high enough in order to avoid certain (so-called non-systematic) risks?

Theories and methods to be applied here can be derived from Mathematical and Stochastical Portfolio Theory and Risk Management Theory. Special Programming Techniques include Agent Technology, Monte-Carlo Simulation and Genetic Algorithms. The goal is here to develop models enabling the measurement of the real risk tolerance of a client. Such models will have to rely on recent contributions to economic theory, as made by Kahneman, recent Nobel Prize winner in Economics, and others. Again such techniques are beyond current state-of-the-art.

 

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