Lending Risk Analysis: Key Considerations (2024)

High inflation, rising interest rates, increasing consumer debt, supply chain issues, and the most significant conflict in Europe since World War 2 are working together to stunt economic growth worldwide. For example, the Federal Reserve Bank of New York found that U.S. consumer household debt increased by 1.3 percent to $17.3 trillion in the third quarter of 2023. Auto loan balances increased to $1.6 trillion, continuing an upward trend since 2011. Given the uncertain economic future, lenders should conduct a lending risk analysis to maintain a healthy loan portfolio.

Technology has become an essential tool for lenders, particularly when evaluating the borrowers to whom they’re lending. Risk analysis depends on reliable data sources that inform a lender of an applicant’s spending habits and financial strength. This is coupled with data analytics software that utilizes sophisticated machine learning algorithms to accurately assess risk within a lender’s portfolio. Let’s look at some of these capabilities and how important they are for predicting risk so lenders can reduce write-offs.

A Better Understanding of Financial Standing

Long-established credit bureaus like Equifax, Experian, and TransUnion stand on the front lines of lending risk analysis. Lease or loan applicants with excellent credit bureau scores breeze through the origination process. Often, loans to such borrowers are automatically approved, offering the best terms possible to the applicant. Yet credit scores don’t always accurately indicate an applicant’s current financial standing. When combined with these bureau scores, trended credit data offers additional perspective when assessing an applicant’s creditworthiness.

Because of this, many lenders have begun using trended credit data reports to more accurately establish lending risk. These data trends are analyzed by providers like TransUnion, which offers up to 30 months of credit card data. Such credit data reports contain details regarding credit limits, monthly balances, and any amounts past due, along with a record of an applicant’s minimum, actual, and late payments. While a traditional credit bureau score provides a snapshot of an applicant’s financial standing, trended credit data can reveal a better picture of an applicant’s financial situation.

However, incorporating trended credit data into a lender’s lease or loan origination process may require custom integration, especially for lenders using older lending software. Investing in modern, cloud-based solutions with trended credit data services already pre-integrated makes this task considerably easier.

Machine Learning Approach to Lending Risk Analysis

When the number of lease or loan applications drops during a challenging economic climate, lenders rely on technology that can thoroughly evaluate which loans to authorize and to whom they can recommend the best terms, doing all this without increasing lenders’ risk. Using machine learning techniques helps lenders more accurately assess financial standing and offer risk-adjusted terms for qualified borrowers.

Machine learning also provides lenders with a fairer credit model for loan applicants. By looking at thousands of variables, lenders can draw insights to predict which applicants are a good risk and which aren’t. Through the use of machine learning within their loan origination systems, lenders can additionally speed lending decisions. This allows lenders to offer deals that match an applicant’s creditworthiness and increase lending opportunities without escalating risk.

Machine learning plays an essential role in lending risk analysis by:

• Deciding for whom to approve loans through the utilization of past data.
• Evaluating credit history to determine how responsible borrowers have been with previous loans.
• Forecasting the ideal loan amounts for specific customers.
• Looking at employment history to ensure borrowers have a stable enough job to make regular payments.
• Offering loan amounts based on what customers can repay.
• Predicting which applicants are most likely to pay their debts on time.
• Tracking borrower behavior over time to identify changes to a borrower’s risk profile.
• Using demographic information such as age and gender to determine the best loan candidates.
• Utilizing factors like credit history, credit score, credit utilization, debt-to-income ratio, and employment history in combination to determine to whom and how much to lend.

By analyzing data obtained from loan or lease applications, consumer financial records, and lender’s portfolio, lenders can use machine learning algorithms to develop a single, dynamic credit model. This approach to lending risk analysis reduces operational costs and gives lenders a competitive advantage over those who only look at traditional credit models.

Integrated Analytics for Portfolio Risk Insight

Ideally, lenders want to minimize risk at the point of lease or loan origination. Invariably, a certain proportion of these leases or loans will still become delinquent due to various unforeseen circ*mstances. Rather than accepting this as inevitable, lenders can employ analytics to better understand their portfolios to identify the most likely sources of lending risk. Analysis of these risks is augmented through cloud-based resources for storing and evaluating data.

Integrated, cloud-based analytics offers benefits that include:

• Scalable pay-as-you-go services that allow lenders to only pay for the resources they need.
• Minimizes downtime due to cyberattacks or natural disasters by implementing data backups and recovery plans.
• Improved data security through factors such as multi-factor authentication and detailed security audits.
• Ensures lenders have the most up-to-date software and security through regular updates and security patches.
• Enhanced data integrity protects against data breaches.
• Can be quickly and easily implemented.
• Better user experience by reducing human errors, improving response time, and eliminating delays.
• Assists lenders in maintaining regulatory compliance through real-time classification, logging, storage, and reporting of data.

Careful portfolio analysis can reveal attributes that closely correlate with the likelihood of default. Using this insight, lenders can reduce the chances of future defaults by identifying leases or loans with similar characteristics. Lenders can better stave off defaults during economic uncertainty by carefully monitoring payment trends and proactively contacting customers to offer loan or lease modifications to help bridge financially challenging times.

Automation for Speedy, Accurate Document Verification

Automation makes lending risk analysis more efficient and accurate by automating repetitive tasks, improving accuracy, and providing timely insights. Lenders can automate data entry, document verification, and compliance checks, reducing human error and ensuring analysis is based on accurate information. This allows lending institutions to allocate their human resources more strategically, focusing on higher-level decision-making and complex risk assessments.

Automation allows continuous monitoring of borrower behavior and financial indicators, allowing real-time risk assessment and early detection of issues. With automation and advanced analytics, lenders can create a more agile and responsive risk management framework, adapting to changing market conditions and ensuring efficient and risk-aware lending practices.

Now May Be the Best Time to Take Advantage of Technology Innovations

From an investment perspective, definitive and quantifiable benefits can be attained through trended data, machine learning, integrated analytics, and automation. To properly evaluate the borrower to whom a lender is lending, risk analysis technology enhances these industry tools. There is no better time for a lender to invest in a loan origination system than during an economic downturn when the chance of default is higher, even for those with excellent credit scores. By helping to reduce write-offs now, it will improve a lender’s risk management well into the future.

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