Data Mining and Risk

Approval or risk models are unique to certain industries that assume the potential for loss when offering a product or service.

The most well-known types of risk occur in the banking and insurance industries. Banks assume a financial risk when they grant loans. In general, these risk models attempt to predict the probability that a prospect will default or fail to pay back the borrowed amount. Many types of loans, such as mortgages or car loans, ares ecured. In this situation, the bank holds the title to the home or automobile for security. The risk is limited to the loan amount minus resale value of the home or car. Unsecured loans are loans for which the bank holds no security. The most common type of unsecured loan is the credit card. While predictive models are used for all types of loans, they are used extensively for credit cards. Some banks prefer to develop their own risk models. Others banks purchase standard or custom risk scores from any of the several companies that specialize in risk score development.

For the insurance industry, the risk is that of a customer filing a claim. The basic concept of insurance is to pool risk. Insurance companies have decades of experience in managing risk. Life, auto, health, accident, casualty, and liability are all types of insurance that use risk models to manage pricing and reserves. Due to heavy government regulation of pricing in the insurance industry, managing risk is a critical task for insurance companies to maintain profitability.

Many other industries incur risk by offering a product or service with the promise of future payment. This category includes telecommunications companies, energy providers, retailers, and many others. The type of risk is similar to that of the banking industry in that it reflects the probability of a customer defaulting on the payment for a good or service.

The risk of fraud is another area of concern for many companies but especially banks and insurance companies. If a credit card is lost or stolen, banks generally assume liability and absorb a portion of the charged amounts as a loss. Fraud detection models are assisting banks in reducing losses by learning the typical spending behavior of their customers. If a customer’s spending habits change drastically, the approval process is halted or monitored until the situation can be evaluated.

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