Pricing Risk In The Dark: Why Blanket Interest Rates May Be Ending In Retail Lending

By Winston Osuchukwu | Founder & CEO, Mathesis Analytics Inc.

Consider two small businesses seeking a N5 million term loan. Both have been operating for about the same period and generate similar revenues.

But there is a major difference.

Business A depends heavily on extended supplier credit to finance its working capital, while Business B turns over its inventory quickly and maintains more consistent cash flows.

Under a conventional credit scoring system, both businesses could qualify for the same loan and, in some cases, receive the same interest rate.

That may appear fair on the surface. But it can produce an unfair outcome.

The more financially stable borrower may end up paying for risks that do not belong to them, while another business that may be capable of repaying a loan could be denied simply because it does not fit neatly into the lender’s traditional risk categories.

This is one of the challenges facing retail and SME lending today.

The problem is not necessarily that lenders cannot identify risk. Rather, traditional lending systems often place borrowers with significantly different financial behaviours into broad risk categories.

When information is limited, such an approach is understandable. But as lenders gain access to more information about how individuals and businesses actually manage money, there is a strong case for moving beyond one-size-fits-all pricing.

The future of lending should not simply be about deciding whether a customer gets a loan or not.

It should also answer three important questions: how much should the customer receive, how long should the facility run and what interest rate fairly reflects the risk?

The Hidden Cost Of Blanket Pricing

At the centre of the traditional approach is the assumption that borrowers within the same risk category are sufficiently similar to justify the same pricing.

In reality, they may not be.

When borrowers with different risk profiles are placed in the same pricing bucket, lenders end up pricing the average borrower rather than the individual borrower.

This creates what could be described as a hidden subsidy.

Lower-risk borrowers may pay more than their actual risk warrants because the pricing also takes into account losses associated with riskier borrowers.

At the same time, businesses that could potentially repay their loans may be excluded because they fall outside rigid lending requirements.

The result is an inefficient credit market.

Good borrowers can overpay, while viable borrowers may be left without access to credit.

Better Data Can Change The Picture

A major opportunity lies in changing how lenders measure risk.

Instead of relying mainly on historical financial information and static credit scores, lenders can increasingly assess borrowers through their actual financial behaviour.

Nigeria’s financial system generates large amounts of transactional information.

Bank account activity, merchant transactions, mobile money usage, utility payments, supplier settlements and other financial activities can provide useful insights into the financial health of an individual or business.

When properly analysed, this information can help lenders develop a clearer picture of repayment risk.

For businesses, for instance, looking beyond revenue can reveal whether cash flows are stable, whether customers are paying on time and how quickly inventory is moving.

That can provide a more accurate picture of a company’s ability to service debt than a single financial snapshot.

More importantly, the assessment does not have to remain static.

If a previously stable business begins to experience a sharp decline in transactions or cash inflows, the change can be detected. Conversely, improving cash flows can also provide evidence of strengthening financial health.

This creates the possibility of a lending system that responds to the borrower’s current financial reality rather than relying solely on what happened months or years ago.

From Fixed Rates To Dynamic Pricing

With better visibility into risk, lenders can begin moving away from rigid interest-rate bands.

Rather than placing every applicant into a predetermined pricing category, technology can help determine the borrower’s risk more precisely.

The lender can combine its existing relationship with the customer with other relevant financial signals, including payment behaviour and transaction patterns.

The resulting risk assessment can then influence the price of the loan.

This does not mean that every borrower will receive a completely different interest rate. Lenders will still need to account for their cost of funds, operating costs, regulatory requirements and acceptable risk limits.

But within those boundaries, pricing can become more flexible.

A customer with a strong and consistent financial record could qualify for a more competitive rate, while a borrower presenting greater risk could pay more to reflect that risk.

Such a system would make the price of credit more closely connected to the borrower’s actual financial behaviour.

The Economics Of Precision

Risk-based pricing is not only beneficial to borrowers. It can also improve the economics of lending institutions.

When lenders are better able to measure risk, pricing can become a tool for improving profitability rather than simply protecting against losses.

A lender could offer more competitive rates to its strongest customers, helping to retain them, while appropriately pricing higher-risk customers.

The borrower also stands to benefit.

A clean and consistent transactional history could become an important financial asset. Instead of simply showing how much money passes through an account, the data could help demonstrate the customer’s ability to manage financial obligations.

For responsible borrowers, this could translate into lower borrowing costs over time.

A New Opportunity For Financial Inclusion

This approach could also help address one of Nigeria’s longstanding credit challenges.

There is a significant difference between someone who lacks a conventional credit history and someone who represents a genuinely high credit risk.

The two are not necessarily the same.

A small business may have operated successfully for years but have little formal borrowing history. Under a traditional system, that business may struggle to secure affordable credit.

However, its transaction history could tell a different story.

Consistent sales, regular customer payments, stable cash flows and responsible financial behaviour could provide lenders with evidence that the business is capable of repaying a facility.

Better risk assessment could therefore help distinguish between businesses that are simply underserved and those that are genuinely risky.

That distinction matters.

It could allow more productive businesses and individuals to enter the formal credit market while enabling lenders to manage their risks more effectively.

The End Of The Average Borrower?

The move away from blanket pricing may ultimately be driven by economics.

Static rate cards can leave value on the table because they treat significantly different borrowers as though they carry the same level of risk.

As competition increases and financial technology continues to develop, lenders may have less room to rely on broad risk categories.

The institutions that succeed in the next phase of banking are likely to be those that can combine technology, data and responsible risk management to understand individual borrowers more accurately.

For consumers and small businesses that have long been placed into rigid lending categories, the change could be significant.

Instead of asking only whether a borrower qualifies for a loan, lenders may increasingly ask a more important question:

What does this particular borrower’s financial behaviour tell us about the right amount, duration and price of credit?

That could mark a shift from lending based on the average borrower to lending based on the borrower in front of the bank.