Bad debt is one of those things lenders can’t wish away, so it makes sense to treat it as part of the cost of doing business rather than a nasty surprise at the end of the quarter. The smart way to approach it is by looking at past write-offs and converting them into a simple percentage of total lending. That number may seem small on its own, but it gives lenders something solid to work with. It’s a good starting point for figuring out how much to set aside in provisions, how to price loans more realistically, what to tighten or loosen in underwriting, and how aggressively collections should be managed.
On the surface, the formula is simple, it only starts to mean something when you dig deeper and break it down by borrower groups. Looking at customers in groups shows patterns that averages can’t capture, like whether first-time borrowers default more often than repeat ones or if certain income brackets carry more risk. Adding roll rates (which tracks how loans progress as they become overdue) to the mix makes things even clearer by showing how loans shift from being slightly overdue to becoming full write-offs over time. A loan that is late by 30 days might roll into 60 days, then 90 days, and eventually become a write-off. Watching how loans move along this path gives lenders a realistic view of where risks are building up. If too many loans are rolling forward in a particular group, it’s a sign that something needs to be adjusted quickly.
The real value comes when these insights are tied back to day-to-day action. A lender that spots a high-risk group can tighten its underwriting rules before more bad loans slip through. If roll rates are climbing, pricing can be adjusted to absorb the likely losses. Collections can also be focused on accounts that are most likely to fall into default, instead of spreading efforts too thin. What starts as a basic calculation of bad debt becomes a useful tool to guide decisions across the lending cycle.
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What is bad debt expense?
Bad debt expense represents the portion of money a business (in this case, alender) has lent that it realistically does not expect to recover. This can include unpaid invoices from customers, loans where borrowers stop responding to calls or emails, or outstanding balances that are lost due to bankruptcy or legal proceedings. When a payment is late, businesses generally do not immediately label it as bad debt. Accounts can remain overdue for weeks or months while the customer communicates, makes partial payments, or works out a plan to settle the debt. It is only when communication ceases, collection efforts do not succeed, or it becomes legally clear that repayment is impossible that the business formally records the balance as a bad debt expense.
Understanding when to classify a receivable as bad debt is important for both accounting accuracy and financial planning. By tracking which amounts are likely to be uncollectible, businesses can better estimate how much of their revenue will actually convert to cash. This ensures that financial statements present a realistic view of income and liquidity. Recognizing bad debt at the right time helps avoid overstating profits and provides a clearer picture for managers, investors, and other stakeholders about the health of the business’s lending or sales operations.
From a tax perspective, bad debt carries additional implications. Tax authorities require concrete evidence that a debt cannot be collected before allowing any deduction. Businesses must show the steps they took to recover the amount, such as repeated reminders, negotiations, or legal action. Documentation of these efforts and a clear record of why the debt is considered uncollectible are fundamental. This ensures that any claimed deduction is supported and defensible, preventing potential disputes or penalties during a tax review.
Why bad debt happens
Bad debt happens for a variety of reasons, and understanding these causes is necessary for lenders who want to manage risk effectively. At the borrower level, financial instability is a common driver. A borrower may experience a sudden drop in income, lose a job, or face unforeseen expenses that make it impossible to meet repayment obligations. Businesses that lend to other companies also face the risk of counterparty bankruptcy, which can turn expected payments into losses almost overnight. Beyond financial hardship, disputes over products or services can also lead borrowers to withhold payment. For example, if a business receives goods that do not meet agreed specifications or a customer believes services were not delivered properly, they may refuse to pay the invoice, creating a situation that eventually becomes a bad debt if it is not resolved promptly.
Process and operational issues on the lender side can also increase the likelihood of bad debt. Weak underwriting that fails to accurately assess a borrower’s ability to repay, unclear contract terms that create confusion about obligations, and incomplete or poorly maintained documentation all contribute to higher losses. Even when two loans appear identical in terms of amount and duration, their actual risk profiles may be very different. Treating every loan the same can mask these differences, leaving lenders exposed to avoidable losses and making it harder to take proactive steps to recover overdue amounts.
External and macroeconomic factors also play a significant role in shaping bad debt levels. Rising unemployment, shifts in interest rates, inflationary pressures, or declines in the prices of commodities that borrowers rely on can quickly turn previously healthy loans into delinquencies. Lenders operating in regions dependent on volatile sectors or markets need to monitor these external signals carefully, because changes in the broader economic environment often precede spikes in delinquencies and eventual write-offs.
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The formula and how to use it
For lenders, the bad debt expense formula is a useful tool for turning historical losses into actionable planning numbers. At its core, the formula calculates the percentage of credit sales that have become uncollectible. The formula is expressed as:
Percentage of Bad Debt = Total Bad Debt ÷ Total Credit Sales
This simple calculation produces a percentage that reflects the portion of loans that were not recovered over a given period. While it is straightforward, its real power shows up when it is applied to current credit activity, allowing lenders to plan for expected losses rather than being caught off guard by them.
Once the percentage is determined, it can be used to calculate a bad debt allowance for the current period. This is done by multiplying the percentage of bad debt by the total credit sales for the period in question. The formula for this step is:
Bad Debt Allowance = Percentage of Bad Debt × Total Credit Sales (current period)
The allowance represents the amount of revenue that should be considered at risk of non-collection and ensures that financial statements and cash-flow forecasts reflect a realistic picture of the lender’s expected inflows. By applying this formula, lenders gain a clearer understanding of potential losses, which informs lending policies, pricing strategies, underwriting standards, and collection priorities.
To illustrate, consider lender A whose historical write-offs total 30,000 on 750,000 in credit sales for the previous year. Using the first formula, Percentage of Bad Debt = $30,000 ÷ $750,000, the result is 0.04, or 4%. If the lender expects $1,000,000 in credit sales for the current year, the bad debt allowance is calculated by multiplying the 4% by the current sales: 0.04 × $1,000,000 = $40,000. This means that of the $1,000,000 in projected revenue, $40,000 should be considered potentially uncollectible.
The calculation also provides a foundation for operational decisions. Lenders can use it to adjust provisions for higher-risk loans, prioritize collection efforts on accounts most likely to default, and fine-tune loan pricing or credit limits. Over time, regularly applying the formula allows lenders to spot trends in repayment behavior, assess shifts in portfolio risk, and make preemptive adjustments before small delinquencies escalate into larger losses.
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How lenders make the percentage truly useful
On its own, a bad debt percentage can feel like not so much. It gives you a number, but it doesn’t tell you much about why losses are happening or where they are concentrated. To get real value, lenders need to operationalize that percentage and build it into a wider credit risk process. This is where structure, detail, and the right lending technology make all the difference.
Segment the portfolio
Losses are rarely spread evenly across an entire loan portfolio. Breaking it down by product type, origination channel, borrower segment makes the percentage far more meaningful. The overall number may look manageable, but when examined closely, you might find that one channel is consistently underperforming or a certain borrower risk band carries a disproportionate share of write-offs. For example, personal loans originated through a mobile app may have very different performance from SME loans booked through agents. With good lending software, this segmentation can be done automatically, giving lenders a clearer view of which groups of loans drive most of the write-offs.
Use roll-rate analysis
Bad debts don’t happen overnight; they build up through stages of delinquency. Tracking how loans move from current to 30, 60, and 90 days past due gives a sharper forecast of potential defaults. These “roll rates” highlight the likelihood of loans slipping deeper into trouble and help lenders anticipate write-offs more accurately, adjusting strategies earlier. With a platform that tracks delinquency in real time, lenders can monitor these shifts effortlessly and spot risks long before they turn into final losses.
Bring in forward-looking signals
Past numbers alone don’t always capture the risks ahead. External factors like rising unemployment, industry slowdowns, or even small increases in early delinquency rates can be warning signs that loss rates are about to climb. If you see those indicators worsening, review your interest rates and tighten lending criteria before defaults actually rise.
Make it part of credit decisions, not just reporting
A lot of lenders keep the bad debt percentage in finance reports with little impact on day-to-day lending. The real value comes when it feeds into pricing, credit policy, and portfolio reviews. If a loan product shows higher write-offs, you might need to adjust eligibility rules, raise pricing to cover risk, or slow down originations in that area. This way, the number becomes part of running the business, not just reporting on it.
Also read: How some Nigerians get into chronic debt, and how to get out
7 steps to reduce bad debt
Measuring bad debt is only useful if it leads to concrete actions. Lenders that consistently keep their portfolios healthy often do so by combining disciplined credit practices with technology that makes monitoring and intervention much easier. Some practical steps include:
Strengthen documentation and contract clarity: A surprising number of loan write-offs stem from weak documentation or contracts that are open to interpretation. When loan agreements are clear, detailed, and easily retrievable, it becomes harder for borrowers to dispute repayment terms.
Price risk accurately instead of absorbing it: If a lender keeps treating higher-risk borrowers the same as low-risk ones, the cost eventually shows up in bad debt. A more disciplined approach is to set risk-based pricing so the interest rate reflects the borrower’s likelihood of default.
Automate reminders and simplify repayment channels: Many delinquencies are not deliberate; they happen because borrowers forget due dates or find repayment methods inconvenient. Automated reminders by SMS, email, or in-app notifications keep repayment top of mind. When paired with easy repayment options such as direct debit or one-click transfers, the risk of accidental late payments drops.
Segment collection strategies: Collections work best when lenders don’t treat all late borrowers the same way. A first-time missed payment should not trigger the same effort as a 90-day delinquency. By segmenting the collections process, lenders can assign automated nudges to early delinquencies and reserve human effort for borrowers where recovery chances are higher.
Monitor recovery rates and net charge-offs: Collections need to be measured with the same rigor as loan disbursements. Tracking how much is actually recovered versus written off helps lenders identify what strategies are working. This feedback loop is often difficult to manage manually, but with the right technology, lenders can generate reports that clearly show whether interventions are improving recovery rates or not.
Tighten loan origination controls: Prevention is always cheaper than chasing after repayment. Reviewing borrower identity verification, fraud checks, and credit scoring at the point of origination reduces the likelihood of problematic loans entering the portfolio in the first place. Strong lending platforms integrate these checks into the onboarding flow, so fewer weak loans slip through.
Align staff incentives with portfolio quality: If field officers, digital loan agents, or even customer service teams are rewarded only for disbursements, portfolio quality suffers. Designing incentives that also reflect repayment outcomes ensures that staff are motivated to lend responsibly.
Making the formula work for you
At the end of the day, the bad debt expense formula is really just a way of holding up a mirror to your lending. It shows you, in numbers, what is happening with your loans and where the trouble spots are. When you check it by borrower groups, run it against roll rates, and play out different scenarios, you give yourself the chance to step in early. That might mean adjusting your pricing, tightening underwriting, or simply being more realistic about provisions. Lenders who do this regularly are the ones who avoid nasty shocks in their cash flow.
This is where technology like Lendsqr makes all the difference. The platform puts the formula into practice with tools for loan origination, collections, decision model configuration, and real-time repayment tracking. It also allows lenders to organize customers into groups and link those insights directly to lending decisions. Instead of finding out too late that losses are eating into your business, you can see it coming and manage it as a cost.
Want to see how it works in action? Book a demo with the Lendsqr team and explore how you can stay ahead of defaults.
