Hybrid Kernelized Fuzzy Clustering and Multiple Attributes Decision Analysis for Corporate Risk Management

This study introduces an emerging risk management architecture by extending balanced scorecards (BSC) with risk exposure considerations for corporate operating performance assessment and then constructs a hybrid mechanism that combines kernelized fuzzy C-means (KFCM), multiple attributes decision analysis (MADA), and extreme learning machine (ELM) for corporate operating performance forecasting. KFCM is implemented to do the clustering task for each corporate under each aspect of BSC. No specific corporate reaches optimal performance under each assessing measure—that is, dissimilar assessing criteria leads to dissimilar outcomes. This method can be transformed into a MADA task and a MADA algorithm that can yield a reliable outcome systematically. Sequentially, the outcome is fed into ELM to construct the performance forecasting mechanism. The introduced mechanism with outstanding forecasting performance comes with a critical challenge: it lacks interpretability, which impedes its real-life usage. To cope with this problem, the rough set theory (RST) is employed to extract the inherent decision logics from the black-box model and visualize it in human readable formats. The introduced model has been examined by real cases and is a promising alternative for corporate risk management.