How do loan delinquencies affect your business?

By Danielle Sesko, Director, Product Management TruStage™

In today’s shifting consumer finance market, loan delinquency remains a significant concern for both lenders and borrowers alike. As economic conditions fluctuate and societal dynamics shift, understanding the intricacies of delinquency rates across various loan types and demographics is important for achieving success.

In this article, we delve into the current state of loan delinquencies in the United States, explore demographic variations, dissect economic drivers, analyze lender strategies for mitigating risk and spotlight innovative solutions like Payment Guard.

Trends across different loan types

In the past year, American consumers have been a steadfast driver of economic growth, bolstering the economy with unexpected resilience. Yet, amidst this robust consumer spending, a concerning trend has emerged: escalating levels of debt and delinquency.

Delinquency rates rose across most debt categories except student loans. Roughly 8.5% of credit card balances and 7.7% of auto loans shifted to delinquency annually. Mortgage delinquency rates saw a slight 0.2% increase, remaining historically low.1 Serious credit card delinquencies surged in all age groups, notably exceeding pre-pandemic levels among younger borrowers.

Americans have seen the cost of everyday living rise and a surge in layoffs over the past two years. Layoffs in 2023 increased by 98% over 2022 with 721,677 announced job cuts.² According to the Federal Reserve’s 2022 Survey of Consumer Finances, 77% of households have some form of debt.³ With nearly 72% of Americans living paycheck to paycheck, having less than $2,000 or less in savings⁴, a large portion of workers can fall behind on any loan payment with an income shock such as a layoff or disability.

Debt and demographics

The latest American debt statistics show us one trend: younger generations are increasingly struggling with debt, particularly in credit cards and auto loans, surpassing pre-pandemic delinquency rates. Here's a breakdown of average nonmortgage debt by age group:⁵

18-29-year-olds: $69 billion total, $12,871 average
30-39-year-olds: $1.17 trillion, $26,532 average
40-49-year-olds: $1.13 trillion $27,838 average
50-59-year-olds: $98 billion, $23,719 average
60-69-year-olds: $64 billion, $16,661 average
70 and older: $36 billion, $9,827 average

Debt-to-income ratio (DTI) is on the rise across all demographics. Not surprisingly, wealthier individuals tend to carry more debt, but also have the means to manage and eliminate it. Conversely, lower-income earners allocate a larger portion of their income towards debt payments. Larger families experience exponential increases in credit card and auto loan balances, underscoring the correlation between household size and financial obligations.

Racial disparities persist in lending and credit, impacting borrowing options and costs. Black, Hispanic, and Native American borrowers face lower credit scores on average, limiting their access to favorable borrowing terms. The average credit card balance for white families was $6,930 in 2022. For Black families, it was $4,360, and for Hispanic families it was $4,115.⁶

Women, despite making significant economic strides, often carry more debt than men. The gender wage gap, along with factors like discrimination and disproportionate family care responsibilities, contribute to this imbalance. Women also face challenges in retirement savings, with 50% of women aged 55-66 having no personal retirement savings.⁷ More women report carrying unmanageable levels of debt than men (39% versus 31%), because women have lower incomes and are more often responsible for caring for children as a single parent.⁸

Student loan debt disproportionately affects people of color, with Black, Hispanic and Native American borrowers often incurring more debt and facing financial struggles post-graduation. Women bear a larger burden of student loan debt and take longer to pay it off, exacerbating their financial challenges.

Overall, debt poses significant challenges across demographic lines, impacting individuals' financial well-being and delaying major life milestones. Addressing these disparities requires systemic changes in lending practices, income equality and support for vulnerable populations. This presents another opportunity for digital lenders to take strategic steps to address borrower needs as we continue to live with economic changes.

Economic drivers

Persistent inflationary pressures have eroded the purchasing power of consumers, straining household budgets and making it difficult for borrowers to meet their repayment obligations. Additionally, interest rate hikes by central banks aiming to curb inflation have elevated borrowing costs, making it harder for individuals and businesses to service their debts.

The lingering effects of the global pandemic have also played a significant role, with job market uncertainties and supply chain disruptions exacerbating financial strain for many borrowers. Additionally, the end of the COVID-era government stimulus programs has been a catalyst for consumer loan delinquency rates climbing.

Shifts in employment patterns towards gig and contract work have introduced income volatility, making it challenging for individuals to maintain consistent cash flows to cover loan repayments. These combined factors have created a challenging environment for borrowers, leading to an uptick in loan delinquencies across various sectors of the economy.

As we move through 2024, signs indicate a softening in consumer spending. Retail sales growth was lackluster in the first few months of the year.

Buy now, pay later (BNPL) plans are growing in popularity and might further dampen future spending as bills start coming due. There are concerns however that consumers may overspend when using delayed payment methods. BNPL services are not subject to the same regulatory reporting standards as more traditional consumer loans, meaning there is a risk of substantial debt accumulation, potentially making it hard for borrowers to repay. According to the Consumer Financial Protection Bureau, 88% of BNPL users also hold an active credit card. On average they tend to have higher levels of debt, carry balances on their credit cards, exhibit delinquencies in conventional credit products and resort to high-interest financial services.¹⁰

Mitigating risk as a digital lender

Lenders should employ various strategies to mitigate loan delinquency risks, recognizing the potential financial repercussions of defaulting borrowers.

A multifaceted approach integrating prudent lending practices, proactive risk management and holistic data aggregation are the cornerstones of effective loan delinquency risk mitigation strategies.

Here are some notable strategies that lenders should be employing to mitigate risk.

Payment protection

With nearly 72% of Americans living paycheck to paycheck, and having less than $2,000 or less in savings⁴, this puts borrowers in a precarious position.

This is where digital lending solutions like TruStage Payment Guard Insurance come in. Payment Guard provides borrowers peace of mind that their loan payments may be covered in case of unexpected job loss or disability. For lenders, it is a backstop to delinquencies and possible default from covered job losses with no friction in the loan application process.

According to our What Matters Now™ research, Black, Multiracial, Gen Z and Millennial consumers have expressed a higher interest in payment protection products. While all demographics report credit card payments as the primary motive for purchasing payment protection, Black and Multiracial consumers particularly emphasize safeguarding new auto loans.¹¹

Payment protection is a viable solution for achieving financial wellbeing that consumers are open to. Digital lenders should explore how payment protection products like Payment Guard could fit into their current structure.

Credit scoring and predictive models

Credit scoring and predictive models serve as indispensable instruments in maintaining the stability of financial markets while promoting equitable access to credit for deserving borrowers. However, conventional credit scoring models have been the standard in the industry. These models hinge on a few key factors like credit history, income and debt-to-income ratio to gauge creditworthiness. While these metrics offer some understanding, they fall short in encompassing the entirety of an individual's financial circumstances.

Additionally, AI (Artificial Intelligence)-based credit scoring and predictive models is quickly becoming the norm, and for good reason. AI-driven credit scoring offers a pivotal advantage in its quick and accurate data processing capabilities. By swiftly analyzing extensive datasets, AI algorithms outpace human capacity, empowering lenders to render quicker and more dependable lending judgments. This agility and accuracy streamline loan applications, furnishing borrowers with prompt responses.

Alternative data sources

Alternative data sources offer a valuable opportunity to augment traditional credit scoring methods, enabling lenders to make more informed decisions and extend credit to a broader range of applicants.

• Social media activity: Social media platforms contain a wealth of information reflecting users' lifestyles, interests, and connections. Analyzing this data can reveal valuable insights into spending habits, employment status, and even personality traits that traditional credit reports may overlook.
• Bank account transactions: Transactional data offers a granular view of an individual's financial behavior, capturing spending patterns, income sources, and cash flow dynamics. By scrutinizing these details, lenders can assess an applicant's income stability, debt management skills, and propensity for default.
• Utility payments: Timely payments demonstrate responsible financial behavior and reliability, factors that positively influence credit scores. Consistent utility bill payments reflect a stable living situation, which correlates with lower default probabilities.
• Alternative credit scoring models: Incorporating alternative data into credit scoring models enables lenders to evaluate a broader spectrum of borrowers, including those with limited or no traditional credit history. AI-based models can adapt to evolving consumer behaviors and economic conditions, enhancing predictive performance over time.

As the financial landscape continues to evolve, collaboration between industry participants, policymakers, and data providers will be crucial in harnessing the full potential of alternative data for credit assessment and risk management.

Loan delinquency remains a critical issue in today’s consumer finance landscape, requiring proactive measures from digital lending leadership and policymakers. By understanding the underlying trends, demographic variations and economic drivers, lending leaders can implement effective strategies to mitigate risks and promote financial stability for borrowers. Embracing innovative solutions, payment protection solutions and alternative data sources will be crucial in navigating the complexities of loan delinquency in an ever-changing economic environment.

Talk to your team to learn how Payment Guard can help provide an added layer of protection.