How fintech firms are utilising AI and machine learning to generate alternative loan scores | Technology

How fintech firms are utilising AI and machine learning to generate alternative loan scores | Technology
How fintech firms are utilising AI and machine learning to generate alternative loan scores 

How fintech companies are using AI, machine learning to create alternative lending scores?

AI and ML can help lending enterprises process, sort and make accurate decisions based on multiple data points to accelerate KYC, arrive at credit scores, and detect fraud and risk management.

Access to money and credit takes a person one step closer to realizing his or her financial dreams.  When such access is instant, one does not have to wait in line, or the amount of time that his or her credit score will be better to be eligible for credit.  It is a liberating experience which is good for the individual as well as the economy.

While traditional banking systems typically shy away from lending to certain segments of the population, leaving a large population disadvantaged and unserved, fintech companies have been able to bridge that gap by becoming an alternative source of credit.  Fintech has been able to underwrite India's diverse customer base residing in smaller towns, tier-3 or tier-4 cities, thereby expanding the government's mandate of financial inclusion.

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One can give credit to artificial intelligence and machine learning, which have helped in creating a conducive credit environment for a wide range of users, thus, providing an alternative lending score instrument that is based solely on an individual's bureau score.  does not become dependent, and thus, easing their financial access.

The need to adapt to new technologies and cater to a wider customer base with customized 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 to suit the evolving needs of their customers.  In fact, machine learning has had a major impact in the lending sector by allowing more accurate and faster decision making through the analysis of consumer data, usage trends and patterns.

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As such, Machine Learning (ML) comes under the purview of AI, where ML uses algorithms and statistical models to perform real-time analysis of huge data sets.  Together, AI and ML lending help lending enterprises rapidly and simultaneously identify, sort, and make accurate decisions based on multiple data points.  There are many benefits of using such disruptive technology, such as faster KYC, quicker access to credit scores, faster fraud detection and risk management, and lower costs.

Once credit is allocated to the user, ML models can detect any discrepancy in usage patterns.  Various micromodels can be used to analyze and predict changes in creditworthiness or 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 paid on time or not.  The ML model makes decisions based on the user's payment history, such as reducing the interest rate for those who consistently pay on time.  ML models also help users make informed financial choices.

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Financial fraud is not a foreign concept even in the fintech sector.  Like every financial institution that alerts users to fraud and has an internal framework to detect and prevent such frauds, fintech companies have also created a process that is easy to understand and detect fraud. 

To get started, a user needs to upload a government ID, take a live photo, and fill in relevant details.  The in-built AI-ML system uses thousands of variables to analyze the customer before making a credit decision.  These variables can range from bureau scores to analyzing how they interact with a particular platform, the time at which they are applying for credit, their banking history, etc.  With the increasing use of digital banking, cyber security and operational risks have also increased.  ,  Banking systems use ML and image recognition technologies to detect anomalies in user behavior and reduce fraud.

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It is heartening that RBI has released a handbook to educate people about financial frauds.  The booklet titled Be(a)ware talks about safeguards against some of the most common fraud techniques, such as SIM swaps, phishing, fake loan websites and digital apps. While users are urged to only approach RBI-regulated fintech companies and verify the respective app on various operating systems before downloading any financial services app, fintech companies have put in place systems to prevent such frauds. Has continued to develop.

With the backing of the government that encourages innovation, and the continued development of fintech, there will be more disruptions as far as AI-ML systems are concerned.  While one should remain vigilant and keep oneself abreast of industry developments, it is encouraging to note that alternative lending models have increased the digital financial footprint of a wide segment, enabling more and more people to make their financial dreams come true. 

Source: Parikshit Chitalkar, Outlook India, Direct News 99