How AI and Machine Learning are revolutionising the lending landscape

Synopsis: Artificial intelligence (AI) and machine learning (ML) can be seen as a set of intelligent services that are meant to mimic human intelligence. But how are they changing the lending landscape? Let’s understand.

More people are now using banking services as a result of increasing financial literacy and awareness, but more significantly, as a result of access to banks and the internet. In addition, they are more willing to apply for loans at banks since they are more familiar with how institutional loans operate than indigenous lenders, who would demand higher interest rates and exhibit little flexibility.

The banking and lending sectors had advanced significantly since 2015, when Javelin Strategy conducted a study that revealed 15% of cardholders had at least one transaction mistakenly refused, costing banking corporations $118 Bn in lost card sales.

This issue was solved in large part thanks to artificial intelligence and machine learning. By using an applicant’s digital footprint, search history, and social media hygiene to determine their creditworthiness in the absence of a CIBIL score, it is possible to reduce errors in loan processing and approve transactions. Lending institutions have benefited from using ML to maximize each application they get.

Using AI and ML, the complex loan origination process may be automated and streamlined to produce results in a couple of days, which is another victory for the banking sector. Customers are happy, and revenue and profit margins are better for lenders who process loans more quickly. In the era of AI and ML in lending, customer satisfaction is no longer an urban legend but rather a fact.

5 Benefits of AI and Machine Learning in Lending

Lenders can dramatically lower their operational expenses by utilizing AI-led lending solutions because the majority of their redundant and laborious operations will be automated, reducing the need for human labor. Not only would this greatly save operating costs, but it will also allow for a more intelligent and targeted allocation of human resources.

Here are a few more ways that AI and Machine Learning are revolutionizing the lending industry:

1. Credit Score

The worth of a loan is based on the borrower’s or lender’s business’s creditworthiness. To assess a person’s creditworthiness, algorithms supported by ML technologies sift through a tremendous amount of data from social networks, mobile devices, payment systems, and web activity. 

A credit score is generated by looking at a potential borrower’s whole digital footprint and is used by lenders to determine the loan value. The quick decision-making reduces the turnaround time for processing loans significantly.

2. Lower Costs

A loan’s value is determined by how creditworthy the borrower or lender’s company is. Algorithms aided by ML technologies comb through a vast quantity of data from social networks, mobile devices, payment systems, and web activity to determine a person’s creditworthiness. 

Lenders use a credit score to calculate the loan value by analyzing a potential borrower’s whole digital footprint. The processing of loans is completed much more quickly, thanks to prompt decision-making.

3. Risk Management

Loan stacking is a frequent practice in the lending industry where borrowers take out multiple loans from various lenders. Lending apps need AI and ML capabilities to profile client behavior and detect questionable patterns that could result in fraud using massive volumes of user data and transactions.

BFSI companies may make informed judgments thanks to the actionable intelligence that ML technology’s insights provide. ML-enabled algorithms can identify clients who are likely to default on their loans and work with lenders to change the terms of loans.

4. Faster KYC

Traditional know-your-client (KYC) procedures need manual labor and a lot of time – AI can simplify this process. Customers’ data is studied to identify trends in their behavior, and loans can be tailored to each individual’s needs, giving lenders access to a captive market. 

In terms of customer service, chatbots powered by AI guide numerous customers at once to the required products while providing fast assistance.

5. Eliminates Biases

At first, loan origination was a labor-intensive, entirely manual operation. The reliance on human intervention affected outcomes as well as pace. Human bias would frequently seep into the underwriting process, causing application rejections or higher/lower interest rates to be applied to some loans, which would result in consumer displeasure or loss for the lender.

By running apps through its algorithms to look for patterns and give insights and conclusions based on the same, ML has eliminated this bias. This lowers error rates and produces loans that are more profitable for lenders.

The Future of Lending

For the loan and banking sector, AI and ML are now a must rather than just a possibility. It is currently the industry’s reality. The sooner lenders jump on the AI and ML-powered bandwagon, the better their chances of capturing a profitable share of the market and gaining customers with higher lifetime value.

While larger lending institutions can implement supervised AI processes, digital lenders, smaller lenders, and P2P lenders can now integrate the power of AI and ML into their lending operations & extend their weaponry at scale & in a cost-effective manner.

Remember that AI and ML are complicated technologies that are only evolving right now. In the years to come, they will play an essential role for any company driving progress and market leadership.