How financial institutions are using AI and machine learning in 2023

Applications of AI

Artificial intelligence (AI) and machine learning (ML) technologies continue to expand in their uses, applications, and benefits for lenders and financial institutions. With this maturity and growing adoption rate, AI/ML is helping solve highly complex solutions that generate positive ROI across business segments.

A majority of financial service providers and lenders are turning to these technologies in their businesses to support areas such as risk management, reducing friction in loan origination departments, managing income and verification, reducing fraud, and compliance and audit processes. I am aware that it is expanding throughout.

Ultimately, financial service providers are using AI/ML to reduce credit costs and continue their efforts toward real-time transparency, increased financial inclusion, and improved compliance. Here are some key use cases for how financial institutions are leveraging AI/ML in 2023:

conversational chatbot

Conversational chatbots help lenders interact with their customers in a more conversational way. Consumers want the same level of customer service they receive from cutting-edge tech companies like Amazon, Netflix and Lyft. AI-powered chatbots and virtual assistants help him 24/7 on account balances, recent transactions, and more. Most impressively, these chatbots allow customers to send funds using conversational language.

Customer sentiment analysis

For years, financial institutions struggled to integrate customer sentiment into big data and automation platforms. Today’s major lenders have access to vast amounts of data about their customers, but historically much of it was unstructured and difficult for computers to understand. But AI can analyze what customers are saying and identify the emotions they are expressing in real time. These systems alert the lender’s customer service team so that the issue can be resolved effectively and quickly.

Thin file/no file creditworthiness

AI/ML can also help you get a clearer picture of your customer’s credit worthiness. It’s especially helpful if your customers have thin credit history, no credit history, or have additional sources of income, like many of today’s gig economy workers.

Let’s take a closer look at a specific use case for the use of AI/ML in auto finance, where various indirect and direct lenders provide loans for millions of new and used car transactions each year.

How AI identifies loan defects in auto finance

The Consumer Financial Protection Bureau (CFPB) has increased the level of scrutiny of loan accuracy and documentation between lenders and dealers (known as Deal Jackets). Investigate whether lenders misrepresented costs in loan agreements that may have forced customers to take out large car loans, often in violation of the Consumer Financial Protection Act of 2010 An audit will be conducted for

This scenario is the latest example of regulators pushing boundaries by introducing new laws or enforcing existing ones, using interpretations to put administrative pressure on lenders and their compliance teams. represents one. Many lenders remain vulnerable to fines and penalties that adversely affect their operations and bottom line.

Lenders can more rigorously mitigate these scenarios by implementing AI-powered systematic controls. This avoids this additional scrutiny and audit environment. Today’s AI-powered software helps lenders comply with regulatory requirements and prepare for audits. The solution provides clear, standardized policies, and lenders are guided through model governance compliance for internal audits, providing expert advice and sample documentation as needed.

Using the AI ​​Model Document

Today’s AI software model documentation includes qualitative assessments of the various potential impact risks in models built for lenders. The audit process involves conducting a different quantitative impact assessment on a quarterly basis. The analysis is based on race, ethnicity, gender, and age (62+), and although the process does not collect race and ethnicity data, it does include race, ethnicity, and gender using the latest census data.

Today’s software leverages advanced AI technology to simplify and automate the process of data collection and analysis, allowing loans to be funded as quickly and efficiently as possible while reducing funding costs. The goal is to make Improve compliance and reduce the cost of consent decrees related to sensitive regulatory matters (MRA) and unjust, deceptive, or abusive conduct and practices (UDAAP).

As with financial providers in all industries, car lenders are not AI/ML experts and not core competencies in AI/ML, so find and help outside qualified AI/ML experts today I understand the importance of doing Trusted partners are utilized to help find flaws in these loans that can flag inappropriate transactions that are not ready for funding. Being able to focus on the complete transaction allows the team to quickly address identified issues with dealers. It also enables automation of dealer defects and instantly notifies dealers of document defects to reduce contracts in transit, fund deals faster, and reduce compliance and regulatory risk.

It’s also important to note that AI and automation are increasingly being deployed in auto-lenders beyond simple loan deficiencies. A recent survey of lender executives found that 63% plan to implement AI and automation technology for securitization this year, 61% for loan servicing, and 52% for loan processing and research. I know you plan to use it.1.

AI and ML are still in their early stages for financial service providers, but adoption of these technologies continues to grow. More importantly, these institutions recognize the positive impact on operational revenue, employee morale, and overall customer experience.

1: InformedIQ automation survey presented to over 2,500 auto finance executives. March 2023

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