Using predictive analytics to reduce risk of loan default and credit fraud

Data-driven enterprises participating in the financial sector require tools capable of revealing credit fraud and loan default risks. When selecting such solutions, professionals must find those that not only indicate the severity of a potential risk but also identify the source of a fiscal hazard.

Predictive analytics often fulfills the aforementioned needs, informing users about the likelihood of certain situations transpiring. How can banks, credit unions and other financial institutions use this technology to improve their risk portfolios?

Assessing the risk of loan default
Determining whether a person will default on a loan entails collecting a comprehensive set of information that includes stability attributes, criminal records, credit inquiry characteristics and other details.

Assessing a person's credit history alone does not provide predictive models with the information necessary towards making accurate estimates. Therefore, collection is just as important as the analytics algorithm in question.

This concern is part of the reason why MicroBilt's iPredict gathers 165 data attributes before deducing the risk of an applicant defaulting on a loan. Businesses that integrate this solution into their loan approvals' process can improve their margins by carefully selecting individuals who pose good risk.

Spotting signs of credit fraud
Fake credit and debit transactions are the greatest threats to credit unions, according to a survey of 141 professionals conducted by Credit Union Times. A little more than 85 percent of the respondents pointed to these incidents as the most common source of fraud they encountered. Straw-buyer fraud rings and wire fraud were among the tactics credit unions often identify when investigating those events.

Only 20 percent of those who participated in the CU Times study said they used data and analytics to prevent credit fraud, despite the benefits of utilizing predictive technologies.

Operational Database Management Systems, a trade publication, spoke with Bart Baesens, a professor at KU Leuven in Belgium who specializes in management informatics, on the issue. Baesens noted predictive analytics is capable of estimating when credit fraud will occur, categorizing the behavior and determining the severity of the fraud incident.

Identifying white collar crime
The techniques Baesen outlined in his interview with ODBMS are also applicable to pinpointing white collar crime such as securities fraud, embezzlement and money laundering. Many of these tactics involve detecting anomalies through peer group analysis, association rules and break point analysis.

Peer group analysis, with respect to uncovering fraud, involves grouping individuals with analytically identical traits and specifying the behaviors to which those groups adhere, according to the International Monetary Fund. A financial institution may segment people according to their investment portfolios, asset compositions and other factors. Any deviations from the prescribed behavior could indicate that a member of that peer group may be committing some sort of financial crime.

Acquiring predictive analytics solutions isn't the be all and end all to reducing credit fraud and loan defaults. To gain the most ROI from these technologies, businesses must integrate them into their operations. Establishing this cohesion enables them to systematically reduce the risks of lending to consumers and collaborating with other businesses.