The first clear win for credit assessment based on non-traditional or alternative sources of data is inclusivity – by giving ‘thin-file’ borrowers with little or no credit history, a chance to benefit from the expanding digital lending space.

Traditionally, credit scores and lendability models have been evaluated on structured data comprising credit history, repayments, high credit utilization, credit cards, and assets, which makes it easily readable and searchable by algorithms. While this, by default, excludes a large percent of credit applicants like migrants, young adults or lower financial segments of society, it also conversely does not provide explicit information about user income or cash flow. Financial institutions and lenders often rely on self-reported income during the application process, which may not always be accurate or verifiable.

The advancements in technological innovations have furnished credit scorers with new-age tools to leverage non-traditional alternative data, also called ‘consumer permission data’. This includes a matrix of information drawn from user messaging content, social media footprints, digital payments history, online browsing behavior, telecommunications, and locational data.

With the entry of Machine Learning (ML) and AI, trends and patterns in consumer behavior and their capacity and willingness to repay loans can be clearly identified. Therefore, credit bureaus, lenders, and other financial organizations can effectively address some of the limitations associated with traditional credit data, enabling an expanded assessment of creditworthiness for a wider population.

The definition of such alternative data is likely to evolve with technological developments as it sets the stage for lenders to develop better and more relevant innovative products and services to address the needs of consumers – some of which include peer-to-peer lending platforms, bank-fintech partnership models, and online marketplaces selling financial products.

As with every new technological leap we have seen in the recent past, experts are debating the possibility of the alternative credit assessment frameworks replacing the traditional lending scores and methods. However, experience points to an amalgamation of the two sources of data to create a more robust, secure, dependable, and pervasive banking and financial landscape.

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