How Fintech Companies Are Using AI and Machine Learning to Create an Alternative Lending Score
Access to funds and credit brings an individual closer to achieving their financial dreams. When such access is instant, you don’t have to wait in line or until your credit score improves to qualify for credit. It is a liberating experience that is good for the individual as well as the economy as a whole.
While traditional banking systems have generally been reluctant to lend to certain segments of the population, leaving a large population underserved and unserved, fintech companies have been able to bridge this gap by becoming an alternative source of credit. Fintechs have been able to subscribe to a diverse customer base, who live in smaller cities, Tier 3 or Tier 4 cities in India, thereby expanding the government’s financial inclusion mandate.
One can well credit artificial intelligence and machine learning, which help to create a favorable credit environment for a wider range of users, thus providing means of an alternative loan score that does not rely solely on the an individual’s office score, and thus, facilitate their financial access.
The need to adapt to new technologies and cater to a large customer base with personalized needs has become the need of the hour, with traditional banking systems and fintech companies constantly innovating. The latter has successfully used AI-ML to design products adapted to the evolving needs of its customers. In fact, machine learning has had a major impact on the lending industry by enabling more accurate and faster decision-making through the analysis of consumer data, usage trends, and patterns.
As such, Machine Learning (ML) falls under the domain of AI, where ML uses algorithms and statistical models to perform real-time analysis of large data sets. Together, AI and ML help loan companies identify, triage, and make accurate decisions based on multiple data points, quickly and simultaneously. The benefits of using such disruptive technology are numerous, such as faster KYC, quick arrival at credit score, fast fraud detection and risk management, and lower costs.
Once a user is assigned credit, ML models can identify any anomalies in the usage pattern. Various micromodels can be used to analyze and predict solvency or the evolution of risk. Some of these models are also self-reinforcing, for example, each time a user makes a payment, a model can identify where they are in their credit cycle; whether they paid on time or not. The ML model makes decisions based on a user’s payment history, such as reducing the interest rate for people who regularly pay on time. ML models also help users make informed financial choices.
Financial fraud is not a foreign concept, even in the fintech space. Like any financial institution that warns users against fraud and creates internal frameworks to identify and prevent such fraud, fintech companies have also created a process that is easy to understand and detect.
To get started, a user needs to upload a government ID card, take a live photo, and fill in relevant details. The built-in AI-ML system uses thousands of variables to analyze a customer before making a credit decision. These variables can range from the bureau’s score to analyzing how they interact with a particular platform, when they apply for credit, their banking history, and more. With the increased use of digital banking, cybersecurity and operational risks have also increased. . Banking systems use ML and image recognition technologies to identify anomalies in user behavior and reduce instances of fraud.
It is heartening to note that the RBI has published a pamphlet to educate people about financial fraud. The booklet, titled BE(A)WARE, discusses safeguards against some of the most common fraudulent techniques, such as SIM swapping, phishing, fake lending websites and digital apps. While users are advised to contact only RBI regulated fintech companies and check respective apps on different operating systems before downloading any financial services app, fintech companies have developed and continue to develop systems to prevent any such fraud.
With government support encouraging innovation and with the constant evolution of fintech, there will be further disruption when it comes to AI-ML systems. While staying vigilant and keeping abreast of industry developments, it is encouraging to note that alternative lending models have improved the digital financial footprint of a large segment, helping more and more people realize their financial dreams.
The author is co-founder of Stashfin
(Disclaimer: The views expressed are those of the author and Outlook Money does not necessarily endorse them. Outlook Money will not be liable for any damages caused to any person/organization directly or indirectly.)