The access to funds and credit takes an individual one step closer to realising his/her financial dreams. When such access is instant, one doesn’t have to wait in line, or for the time when his/her credit score would improve in order to be eligible for credit. It is a liberating experience that is good for the person as well as for the economy at large.
While traditional banking systems have usually shied away from lending to certain segments of the populace, thereby leaving a large population underserved and unserved, fintech companies have been able to bridge that gap by becoming an alternative source of credit. Fintechs have been able to underwrite a diverse customer base, one that lives in smaller towns, Tier-3 or Tier-4 cities of India, thereby extending the government’s mandate of financial inclusion.
One may well credit Artificial Intelligence and Machine Learning, which help in creating a favourable credit environment for a broader range of users, thus, providing means of an alternative lending score that doesn’t rely solely only on an individual’s bureau score, and thereby, easing their financial access.
The need to adapt to newer technologies and cater to a wide customer base with customised needs has become the need of the hour, with both traditional banking systems and fintech companies constantly innovating. The latter has successfully used AI-ML to design products suiting their customer’s evolving needs. In fact, machine learning has had a major impact in the lending sector by allowing for more accurate and faster decision-making through analysis of consumer data, usage trends, and patterns.
As such, Machine Learning (ML) falls under the realm of AI, where ML uses algorithms and statistical models to perform real-time analysis of vast data sets. Together, AI and ML help lending enterprises identify, sort, and make accurate decisions based on multiple data points, rapidly and simultaneously. The benefits of using such disruptive tech are many, such as faster KYC, prompt arrival at a credit score, swift detection of fraud and risk management, and lower costs.
Once a user is allocated credit, ML models can figure out any anomalies in the pattern of usage. Diverse micromodels may be used to analyse and predict creditworthiness or changes in risk. Some of these models are also self-reinforcing, for example, each time a user makes a payment, a model can identify where they stand in their credit cycle; whether they have paid on time or not. The ML model makes decisions based on a user’s payment history, like reducing the interest rate for people consistently paying on time. ML models also assist users to make informed financial choices.
www.dprg.co.in
