The latest technological revolution pivots around artificial intelligence and machine learning technologies, leveraging their “smartness” for various purposes. One of these is the improvement of process efficiencies and customer satisfaction. It comes as no surprise that the artificial intelligence and machine learning market is growing at a CAGR of 17.9% (2027), according to Yahoo Finance.
The lending industry, particularly, is seeing the extensive application of artificial intelligence in the spheres of credit and risk assessment at financial institutions. Process efficiencies are higher than ever before, and the 24x7 online availability of systems ensures customer satisfaction – whether self-help services or advisory. Needless to say, the future of artificial intelligence is bright in the finance industry.
With that said, artificial intelligence still runs on algorithms; organizations run a risk of boxed-in decision-making, programming, training deficiencies, and learning errors - among other issues with such implements. However, with ensuring sound, realistic datasets for the AI / ML systems to learn from, these roadblocks can be easily bypassed.
When it comes to underwriting, especially, the assessment of risks becomes paramount. Training the artificial intelligence algorithms right is extremely important for transparently executing decision-making when it comes to lending approvals
Let’s dive in deeper to see how AI in mortgage industry can help speed things up, improve efficiencies, streamline workflows, and keep the customers happy.
The mortgage lending market in the UK dates back to the late 18th century. Since then, the market has seen extensive changes in the major players in the market.— Digilytics AI (@digilytics_ai) January 5, 2022
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Even though the trend originated from a pandemic, it remains true that the digital lending market is predicted to register a CAGR of 11.9% between 2021 and 2026, according to Mordor Intelligence. Additionally, their report hints at the fact that artificial intelligence is helping the BFSI industry in understanding the kind of services and solutions that their customers are seeking, which is helping them extensively in developing better customer relations.
In the lending and underwriting niche specifically, there are several tasks that artificially intelligent systems can handle capably with partial or no assistance at all. In essence, underwriting deals with application evaluation. According to a report published by Forbes, while the number of personal loan originations fell sharply in 2020 (because of the pandemic), the figures have more than rebounded in the ensuing years. They estimate a figure of 20 million loan originations for 2022 – which is higher than what was pre-pandemic. This only hints at one fact: manual underwriting is set to grow exceedingly busy and tedious.
In this scenario, implementing artificial intelligence and machine learning can help the lending and underwriting industry in the following ways:
Underwriters can find a great assistant in AI in mortgage & loan origination – not a substitute.
Today, retail is online: a trend that isn’t going to turn around or go away. Customers are used to experiencing speedy turnaround times in every sphere of services, and the same applies to financial services as well. To cater to the increasing loan demands of late, the backend needs to speed up – which can be handled by automating the tedious, clerical tasks using intelligent implements. A survey by DBusiness showed that digitalized, personalized self-service empowered with real-time attributes drastically speed up the loan approval process.
The report also established that while customer loyalty was impacted hugely by levels of customer satisfaction, the speed of approval also had a significant role to play.
For example, LendingClub, a P2P digital lending company, turned the tables with its artificial intelligence implement in the lending ecosystem. The company had been dealing with a billion dollars in loans each month, managing a dataset amounting to 150 million cells just in customer cases – and AI / ML made it possible to execute the processes speedily. They were able to strike a good balance between evaluating risks and customer loan needs, by way of utilizing the AI / ML implements for constant testing, market evaluation, loan servicing, and more.
LendingClub focused on speeding up the lending and underwriting processes to address the following consumer questions:
The bottom line always focused on fastest loan disbursals, and using AI financial services, LendingClub has achieved a NPS of 78.
Loan application rejection is frustrating. The reasons could be genuine, such as a bad credit score, or they could be avoidable, like underwriting errors, incomplete applications or simply too much information than one can handle. The good news is artificial intelligence and machine learning tools can help get applications processed right in three ways:
The complex environment around loan applications today can be greatly streamlined, organized, and processed with help from software equipped with artificial intelligence. Whether applications are received physically or through a mobile application, or through a web portal or a phone call, integrated systems and unified repositories ensure that there is little latency and high accuracy in application processing, helping the lending and underwriting ecosystem speed up at the backend.
RevEL is a simple tool empowered by artificial intelligence and machine learning that helps financial institutions accurately extract borrower data and achieve the delivery of first time-right applications. Three key functionalities make RevEL a well-rounded tool for the loan origination and underwriting ecosystem.
RevEL is equipped with Optical Character Recognition, a technology known for error-free, high-accuracy data extraction from physical or digital documents. The Natural Language Processing module and One-Shot Learning programming enable RevEL to deliver accurately extracted data to the tune of 95%.
RevEL measures the completeness, correctness, and consistency of borrower information from the extracted data. Based on credit history, borrowing patterns, credit score and other parameters RevEL aids underwriter’s decisions for accepting or rejecting loan applications.
Data Verification, Credit Risk Assessment
For the applications that are incomplete or where enough information can’t be furnished, RevEL fills the gaps by gathering information from online sources. In partnership with AccountStore, RevEL gathers bank transaction information and analytics straight from the concerned financing institution through open banking. This helps establish authenticity or fraud, helping underwriters better measure the credit risks associated with a borrower.
Consumer behavior has evolved because of the accelerated digitalization after the pandemic. Financial institutions are in a race to keep up - AI and ML can help greatly with speeding up the backend processes to achieve first-time-right application systems.
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