Here’s to a great week! We’d like to wish everyone a safe and happy Thanksgiving. While many people are taking time away from the office this week, still plenty of others – especially for financial institutions – continue to punch their proverbial timecards. Another hot topic in our industry that seemingly never takes a day off is fraud.
As holiday spending ramps up toward the end of the year, more and more crooks out there are scheming ways to get something for nothing. Credit fraud continues to be a growing concern in the lending industry. Financial institutions need to find ways to increase fraud detection and prevention capabilities within their automated decisioning technology and processes. Analytics continues to provide a trusted and viable counterpunch to rising threats of fraud.
Predictive variables are often used in the development of scorecards based on multiple information sources. The following types of variables are often analyzed to provide an assessment of fraud:
- Standard credit bureau variables: The main information source for these variables is usually the credit bureau file. These variables are typically used for (and are also more oriented to) credit risk. For fraud prediction purposes, some variables offer decent predictive ability but less effectiveness than customized fraud variables. Some examples of predictive credit attributes include the number of inquiries during different periods of time, age of the accounts, age of the credit bureau file and the number of satisfactory accounts over different periods of time.
- Custom fraud variables: The main sources of information for these variables are often the credit bureau file and application. These variables are sometimes the strongest predictors of all the different types of variables calculated. Unlike standard attributes built by risk teams to predict risk, custom fraud attributes built with the intention of predicting fraud tend to be more effective. Some examples included address stability, inquiries based on more sensitive and specific periods of time, and inquiries of different contract types.
- Geocoding variables: Geolocation variables are calculated from mapping software based on precise locations. Strong fraud predictors include calculations of distances and locality matching flags based on client home address, dealer address and employment addresses on the credit bureau file.
- Zip code variables: These variables are calculated to assess the fraud prediction by geographic area (zip code). Zip code variables are sometimes weaker than the same variables at individual levels but still useful for building area-level profiles to compare to the applicant information. They identify patterns of credit activity in specific areas that demonstrate fraud characteristics. For example, specific types of credit recently opened in a particular area.
The demand for fraud detection and prevention tools continues to increase in the lending industry, and these four variable types are just the start when referring to best practices for building scorecards. The CRIF Achieve team’s latest Fraud Detection & Prevention eBook goes into greater detail regarding how these variables, and perhaps two more with even greater impact, can be leveraged to effectively and efficiently safeguard your financial institution against fraud.
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