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Artificial Intelligence In Indian Financial Sector: Challenges And Opportunity For Millennials

In India, only a very small segment of the population has a credit score and the rest of potentially valuable customers do not enjoy the same borrowing opportunities

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India is among the top Fintech markets globally and the Indian financial services sector is emerging as a great place for creative analytical thinkers and data scientists. There are many business challenges that can be resolved only with advanced analytics and AI. 

Specifically to the lending industry, a critical question is how to assess the ‘Willingness and Ability to Pay’ of a new to credit applicant. This challenge is specific for India, as most of the developed markets do have robust coverage of credit scores on the majority of their population. In India, only a very small segment of the population has a credit score and the rest of potentially valuable customers do not enjoy the same borrowing opportunities. Such applicants may come from a high-potential segment of millennials entering the workforce. 

However, traditional lenders in India who rely on credit scores for lending decisions cannot capture this potential. This is a great opportunity to apply AI and predicts the credit behavior of new-to-credit consumers. The analytics team of new age digital lending companies are solving this gap using AI and ML algorithms, alternative unstructured data of new-to-credit customers, and their digital footprint.

The abundance of digital data in India is both an opportunity and a challenge. We, in India, consume more mobile data than anywhere in the world, leaving a significant digital footprint. There is an opportunity to derive true deep insights from this digital data, about customer behavior, needs, and intentions. 

For example, if the customers applying for a personal loan are willing to share their social media history, groups and forums they belong to, subscriptions and apps they use on their mobile. This allows Analytics teams to understand the digital and social behavior of such customers and make predictions of their credit behavior as well. Lenders do it by look-alike analysis of similar segments already in their portfolio, to make conclusions about the applicant’s creditworthiness. 

A great example of what AI and ML can achieve for lending organizations is the “Superwoman Loan” that Clix Capital just launched on this International Women’s Day. Clix Superwoman Loan is a personal loan targeted at urban self-employed female entrepreneurs of today. Most of these women do not have a credit history. 

Also, as women of today, they are adept to the digital processes and seek a seamless experience, thanks to all the mobile shopping apps. Keeping this in mind, we engineered a digital-only application journey exclusively for them. The application process is such that it does not require them to have a traditional credit score on the bureau. Instead, we make credit decisions based on advanced techniques as video analytics and psychometric analysis. 

There are many other challenges that analytics teams of Indian financial services organizations are working on a daily basis. For example the quality of the data and difficulties in merging and matching the data from disparate sources.  However, this also pushes us to arrive at more creative analytical solutions compared to developed and structured markets of USA, UK, and Europe. For millennials, this offers a very steep and enriching learning curve. 

My advice for millennials entering AI careers in India is to stay on top of the latest international developments in deep learning and AI. However, at the same time, use natural curiosity and drive to innovation that is already ingrained in Indian culture, and look for creative workarounds despite the data challenges. This is truly an exciting place to learn and hone analytical skills. 

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house



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