How AI and Machine Learning are Changing the Lending Landscape?
“Big data may be cheap to collect, but it’s expensive if you don’t know what you want to get out of it.” - Ozge Yeloglu, Data & AI Lead, Microsoft, Canada
This quote sums up why Artificial Intelligence (AI) and Machine Learning (ML) are the need of the hour for lending industry.
The financial services industry is probably the richest when it comes to consumer data and insights. Given the numerous applications they receive for new accounts, loans, insurance, along with FICO and credit reports, they are swimming in data gold.
Not knowing how to use this buried resource to the optimum will not only keep traditional banks and finserv companies stuck in time, but it will also steadily deplete their revenues as more and more fintech companies start dotting the lending landscape and taking up a share of their pie.
So, while they may have the Big Data, it is only useful when they put AI and ML into place to churn that data to get some value (read increased revenue) out of it.
In this article, we will scan the length and breadth of AI and ML along with the various benefits they promise upon integration.
The role of AI and ML in lending
While AI is a complete branch, ML is more of a subset of AI. It is often referred to as a “device” or sometimes a “process”. AI and ML together are regarded as a form or system that can simulate human intelligence.
Not only do they process data at a great speed they also do so with greater accuracy and sans bias. This makes them a great resource in the financial services industry, especially in lending, which sees millions of applications but is often unable to cater to them all due to human bias, lack of resources, and errors.
AI can be classified into two types: supervised and unsupervised.
Supervised are mostly found in large banks which have access to large amounts of data and have their own “lending rules” in place. Supervised AIs are fed these rules and given the task of sorting through the incredible amount of data and application based on these predetermined rules.
Unsupervised AI, on the other hand, are better for smaller banks and lending companies that do not have set rules in place and are okay with a more fluid sorting structure. This gives them the benefit of churning out more customized loans and plans for their customers.
To establish an unsupervised AI in place, a data scientist needs to feed in a large amount of data (Big Data) and let AI do its work. AI then gets to work reading the vast amount of data and finding structures and patterns, across variables, in the otherwise haphazard layout.
What machine learning does is that it supports AI in this task. It uses algorithms and various statistical models to perform a variety of “if-then” tasks based on the patterns recognized by AI. ML takes in input (also known as training data) from the sea of data, churns it through its algorithms and then gives an output in the form of decisions or predictions.
When it comes to lending, the ML process will give the lender a decision of whether a loan of a certain size and duration, at a certain interest rate, should be given to an applicant based on the applicant’s data and that of ones similar to their profile.
As such, lenders have a much more comprehensive understanding of what kind of loans to extend at maximum profitability while ensuring customer satisfaction.
ML removes bias from the entire lending process
Loan origination was initially a completely manual process that needed tons of paperwork. The dependence on human intervention not only impacted speed but also results. Human bias would often creep into the underwriting process, thereby leading to application rejections or higher/lower interest rates applied to certain loans which would lead to customer dissatisfaction or loss for the lender.
ML has removed this bias altogether by churning the applications through its algorithms to search for patterns and deliver insights and decisions based on the same; zero emotions involved. This helps lenders reduce error rates and churn out more profitable loans.
How have AI and ML changed lending substantially?
A 2019 survey by the FDIC found that nearly 95% of U.S. households were banked. Two years down the line, chances are that more households have become banked and added to this percentage further.
With increased financial literacy and awareness and more importantly, with access to banks and the internet, more people are now using banking services. They also know how institutional loans work and are more open to applying for loans at banks as compared to indigenous lenders who would charge a higher rate of interest and show little flexibility.
With more applications coming from fresh entrants in the banking system, lenders are now caught in a spot where they must extend loans to people with little to no credit history or forego a chance to earn from the said market. Thanks to AI and ML, the banking and lending industry have come a long way since 2015 when Javelin Strategy conducted a study that showed 15% of cardholders were incorrectly declined at least one transaction that cost banking companies $118 Bn as customers would dump their card upon being falsely declined.
AI and ML have played a huge role in turning this problem into a win. Right from reducing errors in churning loans and giving go-ahead to transactions by using an applicant’s digital footprint, search history, and social media hygiene to ascertain their creditworthiness, in the absence of a FICO score. ML has helped lending companies make the most of every application they receive.
Automating the complicated loan origination process, hence streamlining it to provide results in a matter of days by using AI and ML is yet another win for the lending industry. The faster the lenders churn loans, the better are their revenues and profit margins and the happier the customers. Customer delight has become a fact rather than a myth in the age of AI and ML in lending.
Which lenders can access ML-powered lending systems?
While larger lending institutions can have their supervised AI processes in place, digital lenders, smaller lenders, including P2P lenders now can integrate the power of AI and ML into their lending processes by using cloud-based lending software.
These cloud-based lending management systems let lenders buy licenses or use the ML-powered lending software for a monthly fee on a subscription model. They do not need to invest in tools, hardware, and data scientists to receive similar results and efficiencies as larger banks. Cloud-based lending management system providers have made it simpler for lenders of all sizes to lend smart.
Conclusion
AI and ML are no longer just an option to consider for the lending industry. It is the truth of the industry right now. The quicker lenders hop on to the AI and ML-powered bandwagon, the higher are their chances to grab a profitable portion of the market and gain customers with higher lifetime value.
