Dec 2, 2021
Traditional Credit Scoring Transformed with Alternative Data
When deciding whether or not to grant credit to a consumer, many lenders rely solely on information from traditional credit data or on scores from three consumer reporting agencies across the country. While this can provide a useful indication of how well the consumer has handled their past obligations, it does not give a complete picture of their overall financial stability. It also doesn’t help the 39 million Indians who have “invisible credit” (no credit history) or the 190 million “unqualified” Indians, those with incomplete or outdated credit histories and which do not build traditional credit points.
Lenders need a way to meet consumers with little or no credit history. With little information from major agencies, rating models cannot accurately predict the risk of issuing a line of credit. As a result, rural Indian consumers with little or no credit history have considerably less access to credit. And often when credit is available it comes at a high cost to the consumer.
Additionally, 30–40% of qualified consumers do not have favorable credit scores and may have difficulty accessing competitive credit products.
Lenders also need more information on this population to better assess risks and optimize product positioning.
Cast a wider net for the complete picture
As the world becomes increasingly digital, an ever-increasing volume and variety of data are being created, especially financial information. Along with increased automation, a broader range of alternative data sources can be accessed and analyzed to make more informed financial decisions.
As the world becomes increasingly digital, an increasing volume and variety of data are being created, especially financial information. With greater automation, access to more information can be developed and a wider range of other sources can be used to make more informed financial decisions.
The Data Sutram suite helps bridge the gap between traditional credit data and consumers’ alternative credit and banking history. By providing approximately 1000 unique attributes, lenders can gain insight into the underserved consumer by supplementing their existing decision criteria with our unique data. This solution also provides new information about a consumer’s financial history, providing information about a consumer that would otherwise be invisible to traditional credit scoring ways.
Not including these new forms of data can lead to lost opportunities and smart growth. Considering alternative data in credit decision-making can help improve risk assessment and provide the opportunity to access a market of new consumers that have traditionally been overlooked.
The reality is that managing risk, fraud and compliance is an ever-evolving responsibility and Data Sutram continues to develop innovative ways forward.