Benchmarking Credit Models: Why “Good Enough” Isn’t Good Anymore

In today’s fast-paced credit landscape, the phrase “good enough” is becoming a relic.

We’ve all felt the pressure — borrowers expect faster, fairer decisions, competition is heating up not just among traditional banks but also from agile private credit and direct lending firms, and regulators are demanding more transparency and rigor than ever before. Together, these forces are redefining what it means to have sound credit risk measures, and how institutions should validate and benchmark them.

But here’s the silver lining: strengthening model assurance is no longer just a compliance exercise. Done well, it’s a strategic advantage that can sharpen lending agility, improve capital deployment, and bolster investor confidence. It’s about showing that your internal credit models aren’t just compliant on paper, but genuinely aligned with how markets and counterparties behave in the real world. That’s what builds lasting trust with boards, regulators, and investors alike.

The good news? We already have the tools to make this happen. Advances in technology, the availability of robust benchmarking data, and the rise of standardized analytical frameworks are empowering institutions to test their models against credible external references with a level of precision that simply wasn’t possible before.

The opportunity now lies in applying these tools effectively: model risk managers can bring objectivity and comparability to credit model performance by using transparent frameworks and benchmarking to shadow model outputs. When used together with empirical default and recovery data, they don’t just improve the numbers, they elevate confidence in the decisions those numbers drive.

Regulatory driven Market Shift

The credit risk landscape has evolved faster in the last five years than in the earlier two decades. Competition for quality borrowers is intensifying, spreads are tightening while default rates remain elevated, and origination teams are under constant pressure to deliver faster credit decisions without compromising on risk discipline. In this environment, even small inefficiencies or model blind spots can translate into lost deals, mispriced risk, or higher capital costs.

Think about a time when your credit committee had to decide about a credit proposal on incomplete data, balancing speed against confidence. That tension is everywhere. One mid-sized European lender, for example, discovered that it was losing deals to competition within Oil and Gas Sector systematically at the investment grade part of the credit quality spectrum. They were measuring credit risk with their internal model, which was built five years earlier and internally validated and approved for regulatory capital calculation purposes, and feeding the outcome into a risk-based pricing model. By the time they realized the model bias leading to mispricing of the loan, the bank had lost millions in missed lending opportunities. That’s the hidden cost of “good enough.”

At the same time, regulators are turning up the heat on model governance and validation. The ECB’s multi-year Targeted Review of Internal Models (TRIM), completed in 2021, set a new benchmark for supervisory consistency across Europe, and its findings continue to shape the way internal models are assessed today. More recently, the ECB’s revised Guide to Internal Models (July 2025) further tightened expectations around model calibration, reporting, and governance. From the article 185 of Capital Requirements Regulation (CRR) to Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7), message from supervisors is clear: to ensure that internal models remain robust, comparable, and aligned with industry standards, especially in low-default portfolios, the validation must include quantitative benchmarking against relevant external data sources—both at initial and ongoing validation.

In the private credit space, where transparency standards are still evolving, European authorities are proposing measures to enhance transparency and risk management. Proposed measures include enhanced disclosure requirements, particularly concerning aggregate exposures to shadow banking entities, ESG risks, and equity exposures (EBA launches consultation on amended disclosure requirements for ESG risks, equity exposures and aggregate exposure to shadow banking entities | European Banking Authority

This shift exposes the limits of “good enough.” Many internal models were designed for a different era: when access to clean data was scarce, model teams were smaller, and regulatory expectations less stringent. Today, the stakes are higher. Credit processes are more automated, portfolios more diverse, and market dynamics more volatile. A model that worked well under stable macroeconomic conditions may now struggle to discriminate accurately under stress or to capture emerging risk drivers such as leverage, liquidity, and borrower quality in new lending segments. That’s why leading institutions are rethinking their validation and benchmarking strategies by moving beyond standard internal validation tests and by embracing broader, market-based comparisons. The question is no longer just “Does my model meet policy requirements?” but rather “How does it perform against industry benchmarks, peer data, and alternative models?” The difference between those two mindsets often determines who stays ahead, and who falls behind.

3. The Benchmarking Gap: Where Institutions Struggle

If benchmarking is the answer, why aren’t firms doing it more? The short answer: it’s harder than it looks. The hurdles we face are often practical, and they hit people as much as they affect processes.

Take data gaps, for instance. Many financial institutions tell us that their biggest constraint is data. For certain portfolios they simply don’t have enough default history to run statistically robust tests. Ironically, many of these models are built using limited internal data. That uncertainty reduces confidence and leads teams to stick with inward-looking validation instead of honest external comparisons. Benchmarking these low-default portfolios is crucial as the internal performance data alone can’t provide solid statistical conclusions.

Benchmarking effectively is also a resource-intensive endeavour. It’s far from a simple box-ticking exercise; rather, it requires ongoing commitment and effort. This involves everything from data ingestion and mapping to benchmark models to meaningful interpretation by skilled experts. Unfortunately, small model teams often find themselves stretched thin. With validators pulled into regulatory responses and ad-hoc requests, there’s little time left for the deep-dive analyses that truly drive improvement. There is also an operational element involved: Getting an external benchmark, understanding the sources of deviations, reconciling signals, and producing audit-ready documentation is non-trivial. It’s an operational project as much as an analytical one, therefore it needs project management muscle many teams may underestimate.

Let’s not forget the important emotional element: benchmarking can feel like an audit of professional judgment. Credit officers worry about being second-guessed and modelers worry about “false negatives” in challenger models. Organizations that prize internal expertise sometimes resist external reference points. Overcoming that requires leadership to reframe benchmarking not as a threat but as a tool that strengthens, not replaces, internal judgement.

The Economics of Benchmarking

Benchmarking isn’t just a matter of compliance — it’s economics, too. Every credit model has a cost attached to it, and it’s not just the cost of building and maintenance: the cost of capital misallocation, the cost of missed opportunities, and the cost of time lost in defending models instead of improving them.

Let’s go back to the mid-sized European lender in our example above. At first their problem seemed commercial; the origination team couldn’t understand why their investment grade deals were consistently being won by competitors. But when the credit analytics group ran a benchmarking exercise against S&P Scorecards and their implied default probabilities, the cause became clear.

The bank’s internal probability of default (PD) model was systematically overstating risk for strong borrowers and understating it for weaker names (see below an illustrated snapshot of the model performance).

Table 1 Benchmarking of Default Probabilities Sample Output