Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42846
Title: Risk management and regulatory compliance: A data mining framework based on neural network rule extraction
Authors: Setiono, R. 
Mues, C.
Baesens, B.
Keywords: Data mining
Neural networks
Regulatory compliance
Rule extraction
Issue Date: 2006
Source: Setiono, R.,Mues, C.,Baesens, B. (2006). Risk management and regulatory compliance: A data mining framework based on neural network rule extraction. ICIS 2006 Proceedings - Twenty Seventh International Conference on Information Systems : 71-86. ScholarBank@NUS Repository.
Abstract: The recent introduction of various regulatory standards such as Basel II, Sarbanes-Oxley, and IFRS stimulates the need to develop new types of information systems based on data mining that will help improve the quality and automation of the decisions that need to be taken. Although neural networks have been frequently adopted in data mining applications, their opacity and black box character prevents them from being used to develop white box, comprehensible information systems for decision support in a financial context. In this paper, we introduce a new neural network rule extraction algorithm, Re-RX, that can be efficiently adopted to develop a data mining system for risk management in a Basel II context. The novelty of the algorithm lies in its new way of simultaneously working with discrete and continuous attributes without a need for discretization. Having extracted the Re-RX rules, we discuss how they can be used to build Basel II-compliant ICT systems taking into account the operational and regulatory requirements.
Source Title: ICIS 2006 Proceedings - Twenty Seventh International Conference on Information Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/42846
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