AI and ML technologies are diversifying the lending ecosystem seamlessly and efficiently
We are living in a digitalized world where advancements in technology have benefited individuals and businesses to achieve the desired growth and stay ahead of the competition. With an increase in the accessibility of smartphones, many mobile lending applications have mushroomed in India over the past few years. This had led the government to encourage digitization in banking, which led to financial technology (Fintech) firms rushing to fill necessary gaps – particularly in the digital loans category.
Disruptive technologies like Artificial Intelligence (AI) and Machine Learning (ML) are gaining prominence in almost every sector. The financial industry is not far behind and is sitting on large data. They have exploited these technologies, designing products suiting their customer’s evolving needs. Machine learning has created a stir in the lending sector by allowing for more accurate and faster decision-making through analysis of consumer trends and patterns.
As such Machine Learning falls under the realm of Artificial Intelligence, where ML uses advanced algorithms and statistics to perform specific tasks virtually and in real-time by analyzing vast data sets. Together, AI and ML help lending enterprises identify, sort, and make accurate decisions based on multiple data points, rapidly and simultaneously.
Let’s look at some of the other benefits of these technologies:
1. Faster KYC
Traditional KYC methods are manual and time-consuming, while AI can make this process hassle-free. Customer data is analyzed to understand behavioral patterns and loans can be customized specific to their requirements, allowing lenders to gain a captive audience. In terms of service, AI-powered chatbots assist multiple customers at a time with prompt guidance and direct them to the desired products.
2. Arrive At Credit Score
The value of a loan is tied to the creditworthiness of the individual or business looking for a loan. Algorithms backed by ML technologies sieve through vast sources of data which includes social networks, mobile devices, payment systems, and web activity that help determine the creditworthiness of individuals. A potential applicants’ entire digital footprint is analyzed and turned into a credit score which helps lenders arrive at a loan value. The turnaround time in processing loans is significantly reduced owing to the hassle-free decision-making.
3. Detection of Fraud and Risk Management
In the lending sector, loan stacking is a common phenomenon where consumers take multiple loans from many lenders. To combat this risk, lending apps need AI and ML capabilities to profile customer behaviour, using vast amounts of customer data and transactions to flag suspicious patterns, which could lead to fraud. The insights gathered by ML technology provide lending firms with actionable intelligence for them to make informed decisions. Algorithms powered by ML technology can predict customers who are at risk for defaulting their loans and assist lenders in redefining their terms of loans.
4. Lower costs
Digital lending/fintech firms have technology-enabled business models which require minimal human intervention thereby reducing operational costs. The online process requires documentation to be uploaded directly without in-person submission which can be further verified and evaluated virtually, making the process more efficient. The applicant’s entire credit history and capability to pay back on time can be easily accessed through its digital footprint. In addition, forecasting and updating a borrower’s behaviour manually is extremely time-consuming and prone to errors.
Financial products backed by Artificial Intelligence and Machine Learning will evolve more with time and will radically shift the lending ecosystem with their agile solutions, streamline processes, and user-friendly approaches.
Source: analyticsinsight.net
