by Analytics Insight
March 7, 2022
AI and ML technologies diversify the lending ecosystem seamlessly and efficiently
We live in a digitized 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 loan apps have mushroomed in India over the past few years. This had led the government to encourage digitalization in the banking sector, which led financial technology (Fintech) companies to rush to fill the necessary gaps, especially in the digital lending category.
Disruptive technologies such as artificial intelligence (AI) and machine learning (ML) are gaining prominence in almost every industry. The financial sector is not far behind and sits on big data. They have harnessed these technologies, designing products to meet the evolving needs of their customers. Machine learning has caused a stir in the lending industry by enabling more accurate and faster decision-making through the analysis of trends and consumption 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 large data sets. Together, AI and ML help loan companies identify, triage, and make accurate decisions based on multiple data points, quickly and simultaneously.
Let’s take a 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 to their needs, allowing lenders to gain a captive audience. In terms of service, AI-powered chatbots assist multiple customers at once with quick advice and direct them to desired products.
2. Get to the credit score
The value of a loan is tied to the creditworthiness of the person or business seeking a loan. Algorithms supported by ML technologies sift through vast sources of data, including social media, mobile devices, payment systems, and web activity that help determine the creditworthiness of individuals. A potential candidate’s complete digital fingerprint is analyzed and transformed into a credit score that helps lenders determine loan worth. Loan processing time is drastically reduced with hassle-free decision making.
3. Fraud detection and risk management
In the lending industry, loan stacking is a common occurrence where consumers take out multiple loans from many lenders. To combat this risk, lending applications need AI and ML capabilities to profile customer behavior, using large amounts of customer data and transactions to flag suspicious patterns, which could lead to fraud. . The information gathered by ML technology provides lenders with actionable insights to make informed decisions. Algorithms powered by ML technology can predict which customers are at risk of defaulting on their loans and help lenders redefine their loan terms.
4. Cost reduction
Digital/fintech lending companies have technology-based business models that 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 assessed virtually, making the process more efficient. The applicant’s entire credit history and ability to repay on time is easily accessible through their digital footprint. Additionally, manually forecasting and updating borrower behavior is extremely time-consuming and error-prone.
Financial products supported by artificial intelligence and machine learning will further evolve over time and radically change the lending ecosystem with their agile solutions, streamlined processes and user-friendly approaches.
Parikshit Chitalkar as co-founder of hiding place
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