When looking for new acquisitions, the retail banking community understands the value of the data offered through credit reports, as well as their own internal data mining. Critical analysis of this data is important for sifting out high-risk applicants who can damage the bottom line. At the same time, customer data is used to identify those who are most likely to be solid, long-term revenue generators.
Filling in the information gaps
With the extreme highs and lows in the economy in recent years, standard risk assessment methods no longer provide a full picture of the risk level associated with a large segment of consumers. It is estimated that 35 to 45 million Americans are lacking the necessary credit information to obtain affordable credit, according to the PERC‘s Alternative Data Initiative. Many of these consumers are being forced into high-rate credit situations simply due to a “lack of information.” By going beyond the standard credit reports and pulling in alternative data sources, retail banks gain the opportunity to mine a rich revenue stream.
Mining the slush pile
In essence, alternative data can be used to filter through a slush pile of initial credit rejections and create a second level of risk criteria for this large group of consumers. This type of data offers a more personal examination of an individual’s financial patterns in comparison to the standard credit report. Alternative customer data can be drawn from everyday sources, such as utilities and rent payments, cell phone bill payments and Internet service provider information. The number of responsible household leaders consistently paying their monthly living expenses without a traditional bank account or good credit rating is a much higher percentage than many retail banks realize.
Hiding high-risk consumers
The entire credit and banking industry operates based on percentages. A large percentage of consumers with good credit scores typically can be viewed as a good risk for a retail banker. However, if the banker runs that same group of consumers through the fine sieve of alternative data, it is possible to weed out a larger percentage of high-risk consumers who may have managed to hide their riskier behaviors beneath the screen of traditional data mining.
Increasing your revenue pool, decreasing your risk
By making use of a larger, more detailed data pool, retail bankers can broaden their reach to another level of consumers that traditional credit data screenings have ignored. At the same time, this detailed analysis provides a more accurate view of the credit-worthiness of all consumers in the pool, thereby reducing the level of risk taken by the retail banks. The use of alternative customer data in your analysis creates a double win for the banking industry.