Reports from Hong Kong Polytechnic University Add New Data to Research in Risk Management (Model for Technology Risk Assessment in Commercial Banks): Insurance – Risk Management

2024 FEB 15 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New study results on risk management have been published. According to news reporting originating from Hong Kong, People’s Republic of China, by NewsRx correspondents, research stated, “As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle.”

The news editors obtained a quote from the research from Hong Kong Polytechnic University: “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.”

According to the news editors, the research concluded: “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.”

For more information on this research see: Model for Technology Risk Assessment in Commercial Banks. Risks, 2024,12(2). (Risks - http://www.mdpi.com/journal/risks). The publisher for Risks is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/risks12020026.

Our news journalists report that additional information may be obtained by contacting Wenhao Kang, Behaviour and Knowledge Engineering Research Centre, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong 999077, People’s Republic of China. Additional authors for this research include Chi Fai Cheung.

ORCID is an identifier for authors and includes bibliographic information. The following is ORCID information for the author of this research: Chi Fai Cheung (http://orcid.org/0000-0002-6066-7419).

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