By Rakesh Kumar
Rewari is a small town in Haryana where Surekha was gearing up to sell her handwoven crafts through e-commerce. She and some other women in her family, who would create such artefacts, were planning to make use of this whole-new world of retail for the first time. The inventory needed investments but it was difficult for these wives of daily wage labourers to pool in the required capital, though they had a backup to sell off their stock through the good-old offline channel if there were not enough takers online.
When the rigorous process of applying for a bank loan with extensive paperwork had left her worried, Surekha found a digital lending app that promised to help her resolve the matter. It provided customised funding solutions to budding entrepreneurs like Surekha, helped them devise the customer journey, decide the loan amount, size of EMI, interest rate and the tenure to best of their convenience. This unique experience with digital lending firmed up Surekha’s trust as part of India’s dream mission of financial inclusion. The women had a growing confidence in a New India.
Surekha’s story talks about democratising the access to financial services to the bottom of the pyramid, providing these people – underserved by the traditional banking system – with the ultimate choice of devising the customer journey as they would want it.
India is uniquely positioned as mobile-first economy in a rapidly changing financial services ecosystem. The underlying factors nurturing this great transformation are of huge significance. Without artificial intelligence (AI) and machine learning (ML) used by non-banking finance companies (NBFCs) and fintechs (financial technology companies), Surekha’s dreams would have been limited to the legal formalities and paperwork associated with traditional banking, if not nixed in the lack of the credible documentation or because of unviable repayment conditions.
AI and ML are enabling financial service providers to leverage data and analytics, creating affordable and convenient financial solutions for the underrepresented section of the society. Even individuals in remote and vulnerable areas may now gain access to essential financial resources. These technologies are revolutionising the design and delivery of financial products by aggregating and tailoring them to the unique needs and preferences of low-income households, empowering families and businesses in this segment to firm up their financial resilience and well-being.
Financial Inclusion
The Reserve Bank of India (RBI) has developed an index to measure the progress of financial inclusion in India. It is based on three pillars: access, usage and equality. The index was launched in 2014 and is updated every year since then. In 2022, the index value was 56.4 – up from 53.9 a year back. The apex bank in its 2020-21 Financial Stability Report set a target of achieving a financial inclusion index score of 75 by 2025. This shows that India is planning rapid progress towards financial inclusion. However, we have a long way to go to realise the desired level of financial inclusion.
AI and ML have been playing an extremely important role in achieving this goal with better responses compared to humans. They are being used to identify and target the underserved segments, provide personalised financial products and services, automate processes and minimise frauds. These points are elaborated below:
- Identifying and targeting underserved segments: AI and ML are extremely handy to analyse large volumes of data to identify and target underserved segments of the population. This can help NBFCs reach out to people who are not being served by the formal financial sector and who could be potential borrowers.
- Providing personalised financial products and services: AI and ML help develop customised financial products and services that meet the specific needs of different segments of the population. This has great potential to help and ensure that the financial products are accessible and affordable to everyone.
- Automating processes: AI and ML can automate many of the manual processes involved in lending, such as credit underwriting and customer onboarding. This can help reduce costs and improve efficiency, which can make the financial products and services more accessible to a wider range of people.
- Reducing fraud: AI and ML can be used to identify and pre-empt frauds and thereby protect the lender as well as the borrower. This helps in building trust and confidence in the financial system which can, in turn, encourage more people to participate in it.
Challenges faced by finance companies
Finance companies encounter few challenges along the course of implementing AI and ML to step up financial inclusion. These include the following:
- Lack of data: In order to use AI and ML effectively, finance companies need to have access to large volumes of data. But this data may not be available readily or it may be of poor quality which would eventually affect the identification of the target category or customisation of products and services.
- Lack of expertise: Finance companies may not have the adequate in-house expertise to develop and deploy AI and ML solutions. This may require them to partner with third-party providers or hire external consultants.
- Regulatory challenges: There may be regulatory challenges associated with the use of AI and ML in the financial sector. These challenges need to be carefully considered before any AI or ML solutions are deployed.
AI and ML, therefore, hold the potential to play a significant role in helping NBFCs achieve financial inclusion by widening their reaches and building faith and confidence among the target group with tailored products and services. All this, in turn, would help in the development of the marginalised cross-section of the society and contribute greatly to the economic growth of the country. By making the best use of the technologies, India can create a more conducive financial ecosystem to foster economic and social development.
The author is founder and CEO, Light
