Risk Management in Smes with Financial and Nonfinancial Indicators Using Business Intelligence Methods

Chenyang He and Kevin Lu
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Chenyang He: Brunel University, United Kingdom
Kevin Lu: Brunel University, United Kingdom

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Abstract: This study assesses the usage of financial and non-financial indicators in risk management process with a Business Intelligence (BI) approach and data mining methods. The paper focuses on the selection of Key Risk Indicators (KRIs) KRIs amongst risk indicators for performance measurement and risk control. 846 Chinese listed SMEs in the Shenzhen Stock Exchange have been studied as part of this research. After comparing Logit regression (LR), genetic algorithms (GA) and Chi-square automatic interaction detection (CHAID), it has been found that the CHAID is more accurate with non-financial indicators, which is also able to provide roadmaps to improve risk management performance. This study also used a BI approach to quantise and standardise information from government reports and firms’ annual reports to generalise data for non-financial indicators. This study considered four completely different types of risks following the enterprise risk management framework. By using CHAID as the main method, the threshold values and roadmaps of KRIs have been found. This study provides an integrated method for the risk management process in SMEs by using financial and non-financial information generalised by a BI approach.

Keywords: data mining; enterprise risk management; business intelligence; KRIs; nonfinancial indicators; SMEs; CHAID (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:tkp:mklp18:405-418

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