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High inflation not seen in decades, rising interest rates, continuing supply chain issues from China’s zero COVID policy, and the biggest conflict in Europe since World War 2 are all coalescing to stunt economic growth worldwide. This is worrying news for consumer lenders, who do best when economic conditions are more stable. Should the current downturn become a full-blown recession, it’s important for lenders to evaluate their lending risk, analysis of which is essential to maintaining a healthy loan portfolio.

Lending risk analysis is more important than ever during a financial crisis, and it doesn’t matter why an economic downturn occurs. Lenders will always walk a fine line between accepting new lease or loan applications while minimizing potential defaults. With innovations in lending technology, however, lenders can significantly reduce the risk of defaults, which will, in turn, lower a lender’s total risk exposure.

Technology has become an essential tool for lenders, particularly when it comes to 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 then coupled with data analytics software that utilizes sophisticated machine learning algorithms to accurately assess risk within a lender’s portfolio. Let’s take a look at these capabilities and how important they are for predicting risk so that 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. Trended credit data, when combined with these bureau scores, 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 from 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 actual financial situation.

Credit ScoreTrending Credit DataLending Risk Analysis
Excellent (740+) 
  • During the past 6 months, monthly balance increased by an average of $720.
  • Borrower pays a minimum due each month. 
  • Recent payments contrast starkly with the prior 12 months, where monthly balances were paid in full.
The applicant is facing cash flow problems, indicating greater risk than suggested by the credit score.
Acceptable (620-680)
  • Recent 6 months of credit data counter the dismal national trend.  
  • Applicant added a new credit card. 
  • Monthly balances increased by an average of $345. 
  • Cardholder pays the full monthly balance.
Improving financial position. Lower risk than indicated by the credit score and deserving of better terms.
Subprime (550-620)
  • Six months of credit payments indicate missed payments in the past two months. 
Higher-risk applicant than indicated by credit score. Most likely an auto-decline.

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 difficult 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. The use of 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 not only match an applicant’s creditworthiness but also increase lending opportunities without escalating risk.

Machine learning plays an important 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 candidates for loans.

  • 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 circumstances. Rather than accepting this as inevitable, lenders can employ analytics to gain a better understanding of their portfolio to identify the most likely sources of lending risk. Analysis of these risks is augmented through the use of 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 through the implementation of 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. By carefully monitoring payment trends and proactively contacting customers to offer loan or lease modifications to help bridge financially challenging times, lenders can better stave off defaults during times of economic uncertainty.

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

From an investment perspective, there are definitive and quantifiable benefits that can be attained through the use of trended data, machine learning, and integrated analytics. In order 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.

Getting Started

defi SOLUTIONS offers solutions for a lender’s complete loan or lease lifecycle. Partnering with captives, banks, credit unions, and finance companies, defi’s market-leading solution helps lenders exceed borrower expectations. From digital engagement through the complete lending process, defi sets new standards for flexibility, configurability, and scalability in originations, servicing, and managed servicing. defi SOLUTIONS has the backing of Warburg Pincus, Bain Capital Ventures, and Fiserv. For more information on lending risk analysis and how defi can help, please visit  

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