Digital Footprint And Behavioural Patterns Could Increase Your Credit Score

The credit landscape continues to evolve and the pandemic-initiated realignment in 2020 has accelerated the digital onboarding of customers. Mobile app-based strategy has moved from a “good to have” to a “must-have”. Anonymised, consented and ethical access to the digital footprint of the consumers/ borrowers opens up new vistas for effective credit analysis. 

Traditional sources of data, including the credit bureau check, have remained the cornerstone of credit assessment for decades and will probably continue to have relevance going ahead too. The “ability” to repay needs to be augmented with the “willingness” to repay. This calibrated augmentation helps provide an additional tool in the toolbox as the lenders look to evaluate and assess the creditworthiness of digitally savvy borrowers.

Financial inclusion is an important factor and potential booster in India’s aspirations to be a $5 trillion economy by 2025. Credit fuelled, consumerism driven expansion will aid the Indian aspirational middle class to be the engine of growth contributing to this aspirational goal. 

Currently, 190 million people in India are financially excluded for the lack of traditional data parameters for evaluation. Strong smartphone penetration in the population empowers some fintech lenders to provide credit scores, analysing the digital footprint of borrowers, and aiding the lenders with a credible parameter to expand their loan book. Accumulating alternative data based on behavioural patterns can help improve the predictive power of existing credit frameworks at banks and other financial institutions. This equips the lenders to exercise “risk-based” pricing and driven differentiation in the marketplace. 

The pandemic has accelerated the adoption of alternative credit scoring across lending industries globally. The lockdowns had a severe impact on the economic condition of millions of people globally impacting their “ability” to service the loans. Despite these changes, alternative scoring methods and related models witnessed stability during this phase, reinforcing the relevance of alternate data-based scoring solutions. The key was understanding behavioural patterns and digital footprints that helped predict, with a high degree of confidence, the probability of default.

Enhanced technologies such as machine learning algorithms are able to convert the digital footprint information into millions of features analysing them against features demonstrated consistently by defaulting customers and not by the “good” customers. Some examples of these features include combinations such as a low number of images in one’s gallery, a high storage phone, or an empty calendar. Millions of such combinations are created in order to ensure that the most nuanced behavioural patterns are captured and evaluated.

Alternative credit scoring techniques could be the answer to consumers' financial access to loans. Alternative methods are not the “silver bullet” in themselves but help improve decision-making through additional insights to traditional banks, digital lenders, finance companies and Buy Now Pay later players. Behavioural patterns based scoring continues to be one of the most stable, individualistic, and predictive ways of scoring, with the wide-based analysis ensuring the system cannot be gamed or manipulated.

With India’s young, tech-savvy population, financial inclusion could be beneficial in economic growth and reducing income inequality. Extending financial services to low-income households and those who have never had the privilege of financial security will open untapped doors especially in this digital era. While traditional credit scoring frameworks will continue to be relevant, alternate data-based scoring will augment, complete, and enhance the scoring methodologies for the digitally savvy. 

The author is Head of Global Sales, CredoLab