Alternative Credit Data Can Be Beneficial In Predicting Consumer Behavior

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In hard economic times, it is increasingly difficult for financial institutions (FIs) to make good lending decisions because of changing consumer attitudes and behaviors. Alternative credit data can help FIs make those decisions with more confidence and increase consumer trust and loyalty in the process.

As the economy fluctuates, so do consumer attitudes. In times of economic hardship, people feel vulnerable. They see their neighbors and co-workers saving money and worrying about losing their jobs. It is in times like this that people tend to save more and reduce their spending. Their behavior becomes hard to predict as the environment changes and events like Christmas, that cause major spending hikes, cause data to be skewed. When consumers become nervous about their future, they tend to react in different ways which increases the unpredictability of consumer behavior.

Alternative credit data is information about consumers that hasn't been traditionally used in decisioning. Traditionally, banks use information such as credit reports from the three major credit bureaus, FICO scores, and proprietary scores. Alternative data can be from many sources and can include information such as: addresses, phone numbers, utility and phone bills, driver's licenses, vehicle registration, hunting and fishing licenses, and payday lender information.

Many FIs use alternative credit data to predict the behavior and lifetime profitability of their consumers. For instance, they can determine whether the customer is a good fit for rewards programs. With fee revenues decreasing, some banks are cutting their rewards programs, but using alternative credit data they can predict which customers will create enough revenue to warrant giving them a rewards program.

Not only can this non-traditional data predict good consumer behavior, it can predict fraudulent consumer behavior. Preventing fraud is an ongoing war for the financial industry. Fraud costs banks money and it decreases consumer's trust in financial institutions.  Trust is a vital component of banks' relationships with their customers in any economic environment, but even more so when the economy is declining. Alternative credit data can predict fraud before it happens and it can also find fraudulent behavior from the past. When an application comes in, the name, address, age, and Social Security number on the application are crosschecked, first with each other and then with traditional credit reports from the credit bureau.

If the addresses don't match, instead of instantly declining the customer, FIs can see if there is any additional non-traditional data that supports the mismatched address. If the person has recently changed their address with the postal service, or started a utility service in the same name, the application is most likely not fraudulent, but if the address listed currently has 20 names residing at that address, it could indicate fraud. FIs can cross-check the name on the application with any previous fraudulent accounts to check for fraudulent behavior in the past as well.

It is imperative that banks have a good idea of who their customers are and if they are a good fit for their products. Alternative credit data can help them determine that while increasing customer trust and loyalty.

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