The rent-to-own market continues to expand in terms of demand and offerings.
Today, it’s a flexible product sales model that organizations use across furniture, electronics, automotive parts and services, and more, particularly for consumers shut out of traditional credit.
The flexibility and ease-of-access that rent-to-own contracts can provide also invite complexity.
Rent-to-own agreements often involve lessees with weak or incomplete credit histories, unpredictable income, or high debt-to-income ratios—factors that make risk more challenging to identify and interpret.
In this article, you’ll get a solid understanding of the state of the U.S. rent-to-own market and the specific challenges of the risk management that it requires. You’ll also see how open banking tools like Bankuity’s Advanced Banking Verification (ABV) turn raw and isolated customer financial data sets into connected insight across your full customer lifecycle.
The Growth and Risk of the Rent-To-Own Market
In 2023, the U.S. rent-to-own sector was valued at over $12 billion, with a predicted compound annual growth rate of 6.77% until 2031, when it will be worth over $19 billion.
The market now spans electronics, appliances, vehicles, and home rentals.
Its expansion into a wide range of goods appeals to a growing base of people, especially millennials and generation Z consumers, who prioritize flexibility and frequently work in non-traditional, gig-based roles, and may not have the financial means to make purchases via more traditional means.
The rise of e-commerce has also fuelled this expansion. Online platforms now embed rent-to-own options directly into checkout journeys, making it easier than ever for consumers to opt in, often with limited visibility into the long-term repayment implications.
The increased consumer demand and access options makes accurate risk profiling and consumer-level insights absolutely critical. The opportunity for rent-to-own providers is real, but long-term, predictable success depends on how well you understand your exposure.
The Risk Landscape in Rent-To-Own
While the market grows, many providers are still working with limited data and verification methods that can no longer accurately assess modern-day risk.
One major challenge is assessing customer reliability.
Many rent-to-own users don’t have conventional credit scores, formal pay stubs, or more predictable employment. For instance, the income of a freelance worker can fluctuate week to week, but they might also have a long history of never missing a lease or bill payment.
Without precise, verified data from traditional and non-traditional sources, and the ability to analyze it for customer behavioral patterns, it isn’t easy to gauge whether a customer can sustain their payments.
And while the leased item serves as a form of collateral (meaning you can always retrieve it if the renter misses payments), unreliable payments are still an issue. After all, repossessing an item comes with its own set of costs.
Fraud is another growing concern. Incomplete or outdated application data makes slipping through the net easier for bad actors. Whether online or in person, gaps in identity checks or income verification often lead to downstream losses from missed repayments and the operational cost of chasing delinquent accounts.
And then there’s the structural issue: most rent-to-own customers don’t meet traditional criteria.
This doesn’t mean they’re high risk by default, but it does mean their financial position is more challenging to evaluate. Past delinquencies, unpredictable income, undisclosed debt obligations, or a broader record of poor financial management may be missed without deeper insight.
But, on the flip side, you can also easily miss strong evidence of a reliable and trustworthy renter by overlooking their repayment history, transactional data, and money management trend analysis.
Most providers rely on static inputs, such as credit files, pay stubs, or self-reported information. But these data sources age quickly and within weeks, they no longer reflect what’s happening in a renter’s financial life.
Without dynamic and real-time visibility, every approval can become a gamble and increase the default risk in your portfolio.
Key Data Points and Metrics for Risk Assessment
With a predicted CAGR of 6.77%, the opportunity for massive market growth is substantial.
But a growing market is not the same as a predictable one. As a rent-to-own provider, you face persistent challenges, including:
- Verifying income and identity.
- Assessing repayment reliability.
- Preventing fraud in the absence of traditional credit data.
Many rent-to-own customers would not qualify for mainstream credit offers, which makes robust, data-driven risk management essential.
Let’s take a deeper look at the rent-to-own market landscape, assess key risks, and show how real-time renter insights can help you lend with confidence.
Income Verification
Verifying income is not as simple as reviewing a pay stub. In fact, many applicants in the modern workforce don’t have one.
They might earn from multiple gig jobs or side work, and they very often have irregular and unpredictable transfers in monetary amounts and frequency.
With traditional methods, it’s difficult to ascertain whether the income is consistent enough to cover their repayment obligations.
The global gig economy is experiencing rapid growth, with projections pointing to significant expansion over the next decade:
- Market size in 2024: $556.7 billion.
- Forecast for 2025: $646.77 billion.
- Projected value by 2033: $2.14 trillion.
- Expected compound annual growth rate (CAGR): 16.18% from 2025 to 2033.
These figures signal a major shift in how people earn and how to evaluate them for all types of credit applications.
Open banking data makes these income flows visible and comprehensible.
With credit applicant permission, you can track patterns over time and detect irregularities that might suggest instability. A sudden drop, delayed payments, or changing sources are all signals that static forms would miss.
Stronger income verification for the modern economy supports faster onboarding, stronger risk decisioning, increased approval rates without additional overhead, and fairer access for underserved customers.
Transaction Analysis
Transaction data reveals patterns no credit score can capture.
A renter might appear stable on paper, but their daily spending could tell a different story. Open banking gives you access to a higher level of insight, helping you to understand how a customer manages their money and not just whether they have it or not.
Comprehensive transaction analysis of massive amounts of data is especially valuable in identifying subtle risk markers like erratic purchases, unusual transfers, or changes in payment timing.
With the help of open banking data, you can assess renter discipline, spot emerging financial stress, and tailor repayment strategies accordingly, as well as flag unusual and potentially fraudulent activity.
By building detailed and dynamic transaction analysis into your application stage, you get a deep and broad view of renter behavior that targets risk and stamps it out before it leads to delinquency.
And if eventually a renter begins prioritizing certain bills or slipping into overdrafts, you can engage with them early on, before a missed payment occurs.
Payment Behavior
Monitoring a renter’s payment history is a strong predictor of their future repayment behavior. Patterns of late or missed payments can inform risk assessments, and collection and repossession strategies.
Late payments generally don’t show up on credit reports for at least 30 days after the date of a missed payment. This delay underscores the importance of proactive monitoring to detect issues in real time.
By leveraging open banking technology like ABV, you can access real-time payment data, enabling you to automatically monitor payment behaviors and identify anomalies at scale. This proactive approach allows for early detection of potential issues, facilitating timely interventions.
Implementing robust payment behavior analysis protocols strengthens your risk assessment and enhances the overall leasing process by providing a comprehensive view of a renter’s financial health.
Debt and Other Obligations
Many rent-to-own customers already carry significant debt and ongoing expenses before they apply to rent merchandise from you.
For instance, they might already have debt repayment obligations on credit cards, an overdraft, or student loans. Of course, such a financial load can reduce their capacity to manage new payments.
In 2025, 42.7 million Americans owe a combined total of $1.6 trillion in student loan debt. Of that total, 5 million haven’t made a repayment in over 360 days and 4 million are in late-stage delinquency. A mere 38% of holders of student loan debt are current on their repayments.
Credit card debt across the country stands at $1.18 trillion and auto loan balances stand at $1.64 trillion.
These cumulative obligations can strain affordability behind the scenes, and can often slip past traditional credit analysis systems, increasing your risk as a consequence.
If your risk analysis stage fails to fully capture the scope of each applicant’s current debt obligations, you might be increasing your exposure to avoidable defaults.
The right open banking solution addresses this problem.
It gives you real-time visibility into all recurring debt payments, including from credit cards, buy-now-pay-later plans, auto loans, and student debt.
By assessing obligations up front and throughout a renter’s repayment period up to maturity, you can approve smarter and faster, reduce and manage your risk, and power long-term repayment outcomes more effectively.
Your applicants’ credit scores only tells part of the story, but real insight for the modern economy comes from understanding how they earn, spend, repay, and manage all their debts.
A Smarter, Safer Way To Scale Rent-To-Own
The rent-to-own model is expanding, but so are the risks that threaten your ability to offer it over the long term as a viable product.
As renter profiles become more complex, less easily predictable, and the traditional methods of assessing creditworthiness lose their strength, you require a way to get holistic visibility of an applicant’s financial life.
Real-time income verification, deep and broad transaction analysis, assessment of traditional and non-traditional data sources, and behavioral monitoring combine to give you absolute clarity to approve the right customers and avoid preventable losses.
Bankuity’s Advanced Banking Verification transforms raw open banking data into actionable insights that support every phase of your rent-to-own lifecycle:
- Application: Structure live financial data to evaluate renters without formal credit histories.
- Servicing: Monitor spending and income patterns to detect emerging risk.
- Collections, repossession, and retention: Engage earlier and personalize outreach based on live signals.
In a model where flexibility is key, rent-to-own risk management needs to be fast, precise, and scalable. With ABV, you can manage risk and lay the groundwork for smarter growth in a changing market. Schedule a demo today and learn how ABV can help your business.
