Artificial Intelligence (AI) and Machine Learning (ML) technologies proceed to expand of their applications, uses and advantages for lenders and financial institutions. For this reason maturity and expanded adoption rate, AI/ML helps to unravel highly complex solutions that generate positive ROI across business segments.
A majority of monetary services providers and lenders acknowledge they’re deploying these technologies across their businesses to support areas equivalent to risk management, reducing friction in loan origination departments, income and verification controls, fraud reduction, and the compliance and auditing processes.
Ultimately, financial services providers proceed to strive toward lowering the fee of credit using AI/ML for real-time transparency, greater financial inclusivity, and improved compliance. Listed below are some critical use cases of how financial institutions are leveraging AI/ML in 2023:
Conversational chatbots
Conversational chatbots help lenders interact with customers in a more conversational way. Consumers desire the identical level of customer support they receive from leading tech-forward firms like Amazon, Netflix and Lyft. AI-driven chatbots and virtual assistants offer 24/7 assistance to customers on many items equivalent to account balances and up to date transactions. What’s most impressive is that these chatbots enable customers to send funds using conversational language.
Customer sentiment evaluation
For a few years financial institutions had a difficult time combining customer sentiment into their big data and automation platforms. Today’s leading lenders have access to a plethora of information about their customers, but historically a big portion has been unstructured and difficult for computers to know. AI, nonetheless, can analyze what customers communicate and pinpoint the emotions they’re expressing in real time. These systems can alert lender customer support teams in order that they will resolve issues effectively and faster.
Creditworthiness for skinny file / no file
AI/ML also help provide a clearer view of a customer’s creditworthiness, especially once they have a skinny file of credit, no file of credit, or in the event that they have supplemental sources of income, equivalent to lots of today’s gig economy employees.
Let’s take a better have a look at a particular use case of using AI/ML in automotive finance, where a wide range of indirect and direct lenders provide loans for hundreds of thousands of latest and used vehicle transactions annually.
How AI identifies loan defects in automotive finance
The Consumer Financial Protection Bureau (CFPB) has increased its level of scrutiny on the accuracy of loans and the paperwork documentation (called deal jackets) that takes place between a lender and dealership. In lots of cases, audits happen to analyze if a lender could have misrepresented costs in loan agreements which will have placed customers in high-cost loans for cars in violation of the Consumer Financial Protection Act of 2010.
The scenario represents one in all the most recent examples of regulators pushing the boundaries by introducing recent laws or enforcing existing ones which leverage interpretations that place administrative pressure on lenders and their compliance teams. Many lenders remain at risk of fines and penalties which can be detrimental to their operations and bottom lines.
Lenders can more stringently mitigate these scenarios through the implementation of AI-powered systemic controls that help them avoid this extra scrutiny and audit environment. Today’s AI-powered software enables lenders to comply with regulatory requirements and be audit-ready. The solutions offer policies which can be clear and standardized, and lenders are guided through model governance compliance for internal audits while providing expert advice and sample documentation, if needed.
Using AI model documentation
Model documentation from today’s AI software features a qualitative assessment of the potential for disparate impact risk within the models built for lenders. The auditing process performs quarterly, quantitative disparate impact assessments. The analyses are based on race, ethnicity, gender, and age (62+), and while the method doesn’t collect race and ethnicity data, it does employ the CFPB’s Bayesian Improved Surname Geocoding (BISG) proxy method for race, ethnicity, and gender using essentially the most recent census data.
The software today leverages advanced AI technology to simplify and automate the strategy of collecting and analyzing data, with the goal of helping to fund loans as quickly and efficiently as possible while lowering cost to fund, lowering the fee of processing GAP refunds for early payoffs, improving compliance, and lowering the fee of regulatory Matters Requiring Attention (MRAs) and consent decrees related to unfair, deceptive, or abusive acts and practices (UDAAPs).
Like financial providers across all industries, auto lenders usually are not AI/ML experts, and it’s not their core competency, so that they understand the importance of finding quality outside experts in AI/ML today who may help. Trusted partners are being tapped to assist catch these loan defects, where improper deals might be flagged that usually are not ready for funding. AI software allows funders to deal with complete deals, enabling their teams to quickly address any identified issues with dealers. It also enables automation of dealer defects, immediately notifying dealers of document defects to cut back contracts-in-transit, and fund deals faster and reduce compliance and regulatory risk.
It is usually vital to notice that AI and automation are increasingly being deployed for auto lenders outside of easy loan defects. A recent survey of lender executives found that 63% plan to implement AI and automation technologies this 12 months for securitization, 61% for loan servicing, and 52% for loan processing and finding1.
While AI and ML are still of their infancy stages for financial services providers, the adoption of those technologies continues to grow. More importantly, these institutions are realizing the positive impact it has on their operational bottom line, worker morale, and the general customer experience.
1: InformedIQ automation survey presented to over 2,500 auto finance executives; March 2023