Abstract
Modern financial institutions require sophisticated risk assessment tools to integrate human expertise and historical data in a market that is changing and broadening qualitatively, quantitatively, and geographically. The need is especially acute in newly developed countries where expertise and data are scarce, and knowledge bases and assumptions imported from the West may be of limited applicability. Second order logical models can be a valuable tool in such situations. They integrate the robustness of neural or statistical modeling of data, the perspicuity of logical rule induction, and the experience and understanding of skilled human experts. The approach is illustrated in the context of risk assessment in the Korean surety insurance industry.
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