In complex litigation, a settlement administrator is supposed to act as the final line of defense for the class. But many legacy administrators continue to operate with a toolkit designed for the 1990s. Applying "napkin and pencil" math to a Big Data problem creates results that are, at best, inefficient, and, at worst, can prove catastrophic to the settlement and class members’ hopes of recovery.
Below, we break down the most common data science failures in traditional claims administration and how they compromise the integrity of the class.
1. Using "Black Box" Confidence Scores
In response to widespread claims fraud, legacy administrators recently started using third-party "confidence scores" to flag fraud. While this sounds technical, it actually creates a significant liability. Not only are they often ineffective at catching fraud, these scores often rely on undisclosed black box technology that cannot be explained in a deposition. If you cannot describe the methodology behind a denied claim, you cannot defend it to a judge. Modern litigation requires total transparency where every decision is grounded in repeatable and documented logic.
2. Neglecting Data Enrichment and Identity Resolution
Data produced by the defense is rarely complete or directly tied to actual people. Success requires knowing how to ask for the right data points. Details such as Persistent IDs, timestamps, and device IDs are essential, yet are not always obvious. Beyond knowing what to ask for, allowing the data to arrive fragmented then using enriched data to fill in the gaps will yield the strongest, most defensible result.
This data science practice allows you to conduct independent identity resolution rather than relying on the defense to provide a finished list, when they often don’t have all of the necessary data or resources to resolve the class in a way that fulfills the case objectives. If you lack the facility to enrich the data, you essentially allow these data gaps to dictate outcomes that are less than optimal for the class.
3. Manual Review for Bot-Scale Problems
Traditional administrators often tout "anti-fraud teams" consisting of human reviewers looking at spreadsheets. In a recent case, an administrator's manual review was nearly 100% wrong, meaning almost every claim they approved was a fraudulent bot claim while rejecting nearly all of the valid human claimants. Humans cannot spot the behavioral patterns of sophisticated "fraud farms" or automated scripts. Only data enrichment and foundational data science can distinguish fraudulent claims from valid ones at this scale.
4. Applying Inconsistent Fraud Logic
Without a centralized data science foundation, different reviewers often apply different criteria to similar claims. This inconsistency leads to valid claims being rejected and fraudulent claims being approved, usually resulting in diluted recoveries where the class fund is drained by errors. Accuracy is a fiduciary duty. Using a systems-engineering approach ensures that the same logic applies to every claim every time with a full audit trail for the court.
5. Engaging in "AI-Washing"
The legal industry is currently flooded with "AI-washing." Administrators quote advanced tools in their marketing materials that they do not actually use in their internal workflows. There is a definitive line between using a chatbot to summarize an email and using data engineering to find truths in 15 million records. If your administrator cannot explain the difference between structuring unstructured data and performing deterministic identity resolution, they are selling you a buzzword.
Today, complex litigation means dealing with complex, large-scale data. Too many administrators are relying on ineffective, legacy approaches as opposed to defensible data science. And courts are increasingly demanding that lawyers spot the difference.
6. Blocking VPN Users as a "Security" Measure
Privacy-conscious users make up a significant portion of the population. Yet many legacy administrators simply block all VPN traffic to stop bots. This lazy solution creates an ethical trap for the plaintiff firm. It forces class members to choose between their digital privacy and their right to a claim. A true fraud stack will include frontend tools to stop bots, and a data science approach to identifying sophisticated fraud that bypasses frontend tools, without penalizing humans who value their security.
