TECHNICAL FIELD

The present disclosure relates generally to a computing device for machine learning based risk analysis, and more specifically, to a computing device for machine learning based risk analysis and decision automation in intelligent underwriting.

BACKGROUND

Risk analysis and risk management is a core process for global financial institutions. Risk management accounts for not only the profitability of financial institutions, but also the survival of financial institutions. Underwriting is a type of risk management process which a financial institution (e.g., bank, insurance company, investment house) uses to determine whether or not to guarantee payment in case of damage or financial loss, and accept the financial risk for liability arising from such guarantee.

Each financial institution has its own set of underwriting rules to determine whether or not the institution should accept the risk. To identify and control the risk of the applicant effectively, the rules can be very complicated. High volumes of underwriting cases typically require more underwriting employees to be recruited by financial institution, which leads to high cost and low efficiency. In the insurance industry, for example, insurers incur high costs for hiring underwriters, especially with billions of policies, even though most of the underwriting cases are duplicated. Underwriters in insurance companies spend a lot of working time manually checking the integrity of evidence from policy applicants.

SUMMARY

A computing device for machine learning based risk analysis for intelligent underwriting is described herein. In accordance with one aspect of the computing device, input data of underwriting cases is preprocessed and used to train a predictive model. The predictive model may be cross-validated and tested for accuracy. The predictive model may then be applied to a current underwriting case in order to automatically identify and assess associated risks and provide a decision recommendation.

With these and other advantages and features that will become hereinafter apparent, further information may be obtained by reference to the following detailed description and appended claims, and to the figures attached hereto.

