Model for Technology Risk Assessment in Commercial Banks

Wenhao Kang () and Chi Fai Cheung
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Wenhao Kang: Behaviour and Knowledge Engineering Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Chi Fai Cheung: Behaviour and Knowledge Engineering Research Centre, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China

Risks, 2024, vol. 12, issue 2, 1-20

Abstract: As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Keywords: bank IT risk; BP neural network; risk factors; risk level (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
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