Quantitative rigor in an adversarial forum. We build the models, sanitize the data, and deliver the analysis that attorneys and their experts need to present a financially and statistically defensible case.
Litigation is not a business context. The standard of scrutiny is adversarial, the audience is skeptical, and the opposing party's entire job is to find flaws in your analysis. The quantitative work that drives a case must be built with that environment in mind from the first line of code.
We build litigation-grade analytics: documented, reproducible, assumption-disclosed, and stress-tested against the challenges your analysis will face before it ever enters a courtroom.
In a standard business context, a model that produces a reasonable answer is good enough. In litigation, a model that produces a reasonable answer but cannot be fully documented, independently reproduced, or defended against methodological challenge is not good enough — it is a liability. We build models that can do all three.
Every assumption is disclosed. Every calculation is traceable to source data. Every methodological choice is grounded in established statistical and economic literature and documented in a format that can be produced in discovery and defended under Daubert without apology. We build for the worst-case scenario in opposing scrutiny, because that is the only standard that matters in this context.
The most sophisticated damages model in the world is only as credible as the data it is built on. Financial records produced in discovery are rarely clean — they contain duplicates, coding errors, inconsistent categorization, missing periods, and inter-entity transactions that distort any analysis built on them without remediation.
We treat data sanitization as the non-negotiable first step in every engagement. We reconstruct and validate the underlying dataset before any analysis begins, and we document every cleaning decision so that the data provenance of every figure in the final report can be established with an unbroken chain of custody from source record to expert opinion.
Judges and juries do not follow statistical methodology. They follow stories. The job of litigation analytics is to produce quantitative work precise enough to survive expert scrutiny and communicate it in a way that a non-technical decision-maker can understand, believe, and remember.
We do both. The underlying models are built to the standards of the academic and professional literature. The exhibit package and the expert's summary are designed to communicate those models' conclusions with the clarity and narrative force that persuades a trier of fact — not just an opposing economist who will read every footnote.
Each service addresses a distinct analytical need in the litigation lifecycle. Most complex matters require more than one — the damages model depends on clean data; the expert testimony depends on a defensible model; the claim analysis depends on both.
The mathematical record of what was taken. We build the but-for world using regression against pre-harm performance data, quantify the actual vs. expected performance gap, isolate causation from market factors using econometric controls, and apply present value discounting with explicitly documented rate selection methodology.
Every figure is source-traceable. Every assumption is disclosed. The model is stress-tested against the scenarios opposing counsel is most likely to raise before the report is finalized. Lost profits, business interruption, lost earning capacity, reasonable royalties, unjust enrichment — all computed to the same standard.
Explore Economic Damages →When the claim universe is too large for individual review, statistical methodology provides the defensible aggregate picture that class certification, mediation, and trial require. We stratify the claim population into cohorts, run Monte Carlo simulation across each to produce probability-weighted settlement ranges, and screen the full dataset for fraud indicators and statistical anomalies.
The output is a settlement range with explicit probability weights, a class cohort analysis supporting predominance arguments, and a fraud flag report that targets discovery and can reduce aggregate exposure by identifying the subset of claims most likely to be meritless or misrepresented.
Explore Claim Analysis →Raw financial records are rarely litigation-ready. We reconstruct missing periods, eliminate duplicates, reconcile inconsistent categorization across periods and entities, run Benford's Law analysis for fabrication indicators, and apply isolation forest algorithms for statistical outlier detection — producing a sanitized dataset with an unbroken chain of custody from source file to final analysis.
Every transformation is logged. Every anomaly is documented. The sanitized dataset is producible in discovery without embarrassment, and every figure in the downstream damages model is traceable to a specific record in a specific source file — the foundation of a defensible expert opinion.
Explore Data Forensics →We build the quantitative infrastructure behind expert testimony — the calculation models, the sensitivity analyses, the court-ready exhibit packages — and we independently review and critique the opposing expert's methodology. Before any deposition, we run adversarial scenario testing so the expert can respond from documentation when the most likely cross-examination challenges arise.
On the rebuttal side, we conduct a systematic methodological review of the opposing report, produce an alternative damages calculation using corrected inputs, and develop the specific cross-examination questions most likely to undermine the opposing expert's credibility. A rebuttal that produces a different number is far more powerful than one that only identifies flaws.
Explore Expert Witness Analytics →A data-driven search for what was not disclosed — bank accounts, brokerage holdings, real property, business interests, vehicles, retirement plans, and foreign assets. We cross-reference disclosed financial positions against public records, database searches, and transaction pattern analysis to surface the gap between what a party has admitted to owning and what the data indicates they actually hold.
Account indicator detection mines available financial records for traces of undisclosed institutions: interest income not traceable to disclosed accounts, 1099s from unknown custodians, wire transfers to unidentified account numbers. The findings are packaged as a structured discovery target list — giving counsel the specific document requests and subpoenas most likely to produce the missing accounts and assets.
Explore Asset Search →Business analytics and litigation analytics share the same statistical tools. They do not share the same standard. Litigation analytics is built for an adversarial environment where every assumption will be challenged, every source will be demanded in discovery, and the trier of fact is non-technical.
We have built our litigation practice around the specific demands of that environment — not as an adaptation of our standard consulting methodology, but as a distinct discipline with distinct standards that we apply from the first data intake through the final exhibit package.
All techniques are grounded in the peer-reviewed economic and statistical literature. We document methodology against Daubert's reliability criteria from the first draft of the report, not as a retrofit when challenged.
Every number in every report traces back to a specific record in a specific source file, through a documented chain of transformations. No calculation is unverifiable. No assumption is undisclosed. The full workpapers are producible in discovery without modification.
Before any report is finalized, we run it through the most likely opposing challenges — alternative assumptions, alternative methodologies, alternative data interpretations. The sensitivity analysis is not appendix material. It is part of the core analytical package.
We engage equally for plaintiff and defense counsel. Our analytical standard does not change based on which side retains us. The math is the math, and our obligation is to get it right — not to reach a predetermined conclusion.
Sophisticated statistical analysis must communicate to judges and juries who are not statisticians. We design every exhibit to convey the analytical conclusion with precision and narrative clarity — so the numbers persuade as well as prove.
Explore White Oak's suite of free legal calculation tools — pre-judgment interest, statute of limitations analysis, personal injury settlement estimation, and more. Built on the same quantitative methodology we apply in our engagement work.
Whether you are building a damages model, sanitizing a financial record for production, or developing rebuttal analysis for an opposing expert — reach out to discuss the specific needs of your matter.
White Oak Intelligence provides quantitative analysis, statistical modeling, and data services in connection with legal matters. We do not provide legal advice. All litigation support engagements are conducted at the direction of and in coordination with retaining counsel. Engagement terms and privilege structure are established with counsel prior to any work beginning.