For technical teams, the hardest part of competitive testing is not generating a scorecard; it is building a test harness that reflects production reality. That means controlling for data lineage, sampling windows, decision thresholds, and downstream policy rules so the evaluation measures the whole decision system, not just a model in isolation. If the test setup differs materially from production, the winner on paper can become a liability in deployment.
Integration detail matters as much as model quality. A prospective solution may look strong until it is wired into case management, orchestration, fraud review, or credit decisioning flows. Teams should examine how outputs are consumed, logged, overridden, and monitored across systems, because a small uplift at the score layer can disappear once human review, policy constraints, and latency requirements are applied. The practical question is whether the solution fits the existing decision architecture without creating brittle dependencies.
Governance also needs to be part of the test design. Competitive evaluations should preserve auditability: versioned data sets, repeatable runs, documented feature assumptions, and clear separation between tuning and final validation. That discipline reduces the risk of accidental leakage, hidden overfitting, and post-hoc rationalization. It also gives security, compliance, and operations teams enough evidence to challenge claims that are hard to verify later.
Finally, the operational cost of ownership should be treated as a first-class metric. Recalibration effort, monitoring burden, vendor dependency, and the frequency of rule changes all affect whether a solution remains useful after launch. For institutions, the real test is not which product dazzles in a demo, but which one continues to perform when the environment, adversaries, and internal policies keep changing.
What is Competitive Testing?
Competitive testing is a careful evaluation of prospective solutions’ performance uninfluenced by dubious promises and conducted thoughtfully, in a way that enables fair comparisons with current system outputs and real-world outcomes. It’s easy to overlook the true motivations of the solutions providers pitching for your business. Some are great at winning business – and will make a big song and dance about it – but the true value of competitive testing lies in establishing a meaningful and lasting partnership, as many of our returning clients will attest. Here I’ve outlined several key insights based on extensive experience with similar processes in the hope that it helps others to build an optimal approach and perhaps goes some way to establishing an industry standard practice for this important process.Key Insight #1: Avoid Only Looking at High-Level Metrics Without Context
In competitive testing, risk managers should use specific key performance indicators (KPIs) closely aligned with their business needs, not generic metrics like Kolmogorov-Smirnov Statistics (KS). High-level metrics like KS often require contextual interpretation to avoid suboptimal decisions. A higher KS score might indicate better differentiation between good and bad populations, but its significance depends on the operating range of the business, which in turn depends on the nature of the business. For example, subprime lenders operate at different parameters to near-prime lenders who operate at different parameters to prime lenders. Fraud-related KPIs tend to be focused in the riskiest tail and depend on the specific fraud typology fraudsters use. Ultimately, testing requires tailoring measurements to operational contexts, being sure to inform decisions with relevant, actionable insights.Key Insight #2: Don’t Review Scores in Isolation to Existing Strategy
Assess results beyond face value instead of simply prioritizing the highest-ranking predictive scores. Consider the broader context, including the net benefit and incremental lift a solution may provide over existing strategies. For example:- Lift Over Legacy: Always measure a prospective solution’s benefit in comparison to the existing decision strategy (referred to as “lift”). A solution that integrates complementary, non-correlated data with current systems may provide greater incremental value than one with the highest KPIs.
- Avoid Redundant Data: Adding too much of the same type of information results in diminishing returns. Incorporate a mix of highly varied data sources like credit bureau data, alternative data, device information, email data, behavioral data and/or biometric data to create a holistic and multi-dimensional risk assessment. Multiple uncorrelated signals enrich predictive power, reduce risks and improve fraud detection.
Key Insight #3: Don’t Use a Ruler to Measure Wind Speed
It’s crucial for risk managers to align performance metrics with the specific problem they aim to address when testing and implementing scoring algorithms. Using a score calibrated for third-party fraud to tackle first-party fraud will yield suboptimal results, as the frauds differ significantly in their typologies. Equally, specific fraud categories demand distinct performance definitions. A mismatch between a score’s calibration and a lender’s business metrics can lead to ineffective decisions. In that sense, the best-performing scoring models are not necessarily the ones with the most “accurate” definitions but those that are calibrated to align with your organization’s specific fraud problems and operational metrics. Misaligned definitions negatively impact outcomes for all parties, underscoring the importance of tailoring fraud analytics to meet individual performance needs.Key Insight #4: Avoid the Risk of Overfitting
When testing competitor products, it’s crucial to avoid the pitfall of overfitting, where providers may intentionally or unintentionally manipulate algorithms to achieve high performance metrics on test samples, at the cost of long-term efficacy. Overfitted models often degrade quickly when applied to broader populations, leading to suboptimal results. The most accurate and sustainable scoring models use a three-sample test:- Development Sample: Share performance data with the solutions provider to optimize scoring algorithms.
- Validation Sample: Provide an out-of-time sample from a different period to test the score’s robustness.
- Independent Sample: Request scoring on a final sample without performance data to confirm validation across independent data sets.
Key Insight #5: Beware of Truncation Bias
Truncation bias refers to the development sample not accurately reflecting the broader “through the door” population. Most samples are based on legacy products and strategies, resulting in a skewed outcome during head-to-head testing because the sample was shaped by the legacy solution. This bias often places the legacy score at a disadvantage because it has already filtered outcomes, giving the challenger score an artificial advantage. The challenger score need only find a few additional red flags in the booked population to look like the stronger score. This is an unbalanced conclusion insofar as the legacy score doesn’t have the opportunity to add insight to a challenger score. To mitigate truncation bias, adopt champion-challenger testing methods on the full population of applicants rather than solely on a booked sample. This approach ensures a more accurate assessment of both legacy and challenger solutions by reflecting their true potential impact on decision making.
Key Insight #6: Never “Set-and-Forget” Scores
There is a false perception that scores don’t need much maintenance. However, changing economic conditions, business strategies, target markets and risk tolerances are among the drivers that cause current operational practices around a credit risk or fraud solution to lose effectiveness over time. In the case of fraud solutions, fraudsters change their tactics regularly to evade defenses. Practitioners should track and recalibrate their credit risk scores at least annually and fraud scores even more often.Key Insight #7: Be Wary of Marketing Hype
Marketing hype or exaggeration is unfortunately common in our space. I see it every day and I urge risk managers to exercise caution, particularly when it involves buzzwords like Artificial Intelligence (AI) and Machine Learning (ML). Many providers pitching for business exaggerate the role of these in their solutions, leading to misunderstandings about the actual sophistication and effectiveness of the technology. I advise asking critical questions to distinguish genuine AI solutions from inflated claims. At LexisNexis Risk Solutions we emphasize transparency. We only reference AI or ML in our offerings where these technologies are demonstrably in use. Don’t get caught out: choosing solutions that make overstated AI claims inevitably results in unnecessary operational and compliance burdens that will further negatively impact efficiency. In short, validate every marketing claim until you are satisfied it really does what it promises.Conclusion
There are so many moving parts that make competitive testing a complicated balancing act. By following a structured approach and applying due diligence in all the right places, while avoiding the pitfalls of overstated promises, any business can discover the optimal mix of solutions to best serve their customers and business objectives. Above all, they can find the solutions provider that represents the perfect technical and cultural fit that will result in a long and productive partnership.Don’t Use a Ruler to Measure Wind Speed: Establishing a Standard for Competitive Solutions Testing
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