6 Dynamic Roles of AI/ML in Lending

In the fast-paced world of finance, where numbers have more significance than celebrity Twitter followers and a misplaced decimal point can spell disaster, we find ourselves at the crossroads of human ingenuity and artificial intelligence. In the ever-evolving landscape of lending, where trust is paramount and decisions hold the power to shape financial destinies, the emergence of AI/ML in lending is nothing short of revolutionary.

You remember the old days of banking, right? We’re talking about the mid-1950s: The days when you’d walk in, shake the hand of a man in a professional suit and tie, sit across the table, and hash out all the details of the things that you need to discuss.

If the banker knew you, had some sort of third-party connection to you, or knew your reputation then it’s likely they’d count you as trustworthy, agree to a loan with you, and do business with you – regardless of whether you planned to pay back the loan. Fast forward to today. The antiquated approach seems not only outdated but also inherently biased.

Imagine you’re applying for a loan from the comfort of your favorite armchair. As you sip your morning coffee, an AI-powered algorithm is scrutinizing your financial history, predicting your repayment behavior, and determining your creditworthiness. It’s almost like having a financial advisor who never sleeps and never gets tired of crunching numbers. Welcome to the age of AI/ML in lending, where algorithms are the new loan officers, and data is the currency of trust.

AI/ML in lending ecosystem is transforming the industry by leveraging vast data to understand consumer trends and patterns and helping lenders make quick and intelligent decisions. These new-age technologies help engage each customer individually across the customer lifecycle, from acquisition, upsell, and cross-sell to risk segmentation and debt collection. AI/ML in lending enables lenders, like you, to provide a digitally smart experience to their customers through intelligent underwriting, real-time intelligent decision-making, and offering products that suit customers’ evolving needs. It not only helps you predict those prospects who have a high possibility to convert but also safeguards you from defaulters.

Let’s understand the significance of AI/ML in lending:

1. Customer Acquisition

According to a 2021 survey by Deloitte, 32% of banking products and services consumers said timely offers help them make a purchase decision, and 34% said knowledgeable customer service helped them in doing so.

AI helps predict customers, who have the propensity to take up loans and who do not. If you go through the entire loan initiation and execution processes but are unable to convert the prospect into a customer, it can adversely impact you. AI/ML in lending can screen all applications, analyze data, and provide insights into the most promising prospects. Based on the results, you can plan a relevant and timely campaign, and offer personalized lending rates, thereby increasing conversions.

2. Credit Risk Assessment and Credit Decisioning

By leveraging the AI-powered lending solution, you can generate prompt credit decisions for corporate clients, small, and medium-size enterprises, retailers, or customers looking for personal loans.

The continuous learning model segments the customers to better understand their risk profile. AI-powered decision-making processes help underwriters approve and reject loans based on predictive scores, with only deviations to the outliers. It helps you offer optimal loan amounts with risk-based pricing to the right customers and reduces non-performing loans (NPL) on accurate predictive risk models.

3. Loan Payment Prediction

Modern customers are constantly looking to reduce their interest burden and channel more capital toward investments. Pre-pay loans or transfer loan balances to a competitor can impact lenders’ revenue prediction and working capital. Once the loan is approved, AI helps you strategically identify those customers who are likely to pre-pay and adopt a differentiation strategy to retain them and upsell/cross-sell to them.

4. Loan Default Prediction

A lender’s NPA (non-performing asset) is a closely watched figure, which helps you review and fine-tune your credit assessment strategy. To predict defaulters, AI is used to review customers’ financial health based on credit rating, past payment history, etc. This allows banks to engage, communicate, and work towards loan restructuring to reduce defaults. Additionally, lenders’ exposure to default risk can be tracked to predict yearly losses accurately.

5. Debt Collections

AI allows you to define a debt collection strategy from the loan origination stage by predicting customers who are most likely to pay the debt. Scores can determine your collection strategies, which also predict your collection costs. Lenders can accurately predict collections, plan their budgets, and forecast incentives to be paid out to debt collectors. AI can help you predict the customers most likely to repay their debts. Scoring can help you plan your budget and make strategies to maximize recoveries and reduce costs.

A large lending institution employs AI-driven predictive models to optimize debt collection strategies. By prioritizing collection efforts based on customer likelihood to repay, they have reduced collection costs by 15% while increasing overall recoveries by 10%, ensuring efficient use of resources.

6. Cost Minimization

AI-powered business models require minimal human intervention that significantly reduces the cost of document handling. It can analyze the incoming applications, identify the concerned areas, verify, and evaluate virtually, and send it to straight-through processing if it meets all the required criteria. In case any fraudulent activity is tracked in the pattern, underwriters are instantly alerted to take immediate steps of action.

A digital lender automates document verification using AI-powered optical character recognition (OCR). By automating data extraction and verification, they’ve reduced processing times by 50% and cut operational costs by 30%. AI also flags potentially fraudulent documents, enhancing security.

The Growth of Digital Lending Platforms

According to RESEARCH AND MARKETS, the global digital lending platform market is expected to reach USD 26.08 billion by 2028, with a CAGR of 24.0% from 2021 to 2028. This data underscores the necessity of AI/ML-powered lending solutions to remain competitive and future-ready.

The use of AI/ML in lending goes beyond theoretical concepts; it is creating tangible benefits for lending institutions of all sizes. They are making lending more efficient, inclusive, and secure. As the industry continues to evolve, embracing AI/ML in lending is essential for staying competitive and ensuring success in the ever-changing world of finance. By harnessing these technologies, lending institutions can not only improve their bottom line but also provide better financial services to borrowers, thereby shaping the future of lending.