When the universe of claims is too large for individual review, statistical modeling provides the rigorous, defensible aggregate picture that courts and negotiating parties require.
Statistical claim analysis applies the same quantitative rigor as individual damages work — scaled to handle thousands of claims simultaneously, with defensible sampling and estimation methodology that survives class certification and Daubert challenges.
We begin by ingesting the full claim dataset — transaction records, claim submissions, adjuster notes, payment histories, policy data — and running it through a validation protocol that identifies duplicates, miscoded fields, missing values, and entries that fail internal consistency checks.
Claim data is notoriously messy. Raw insurance or litigation claim files contain coding errors, duplicate submissions, and missing fields that can distort an aggregate damages figure by orders of magnitude if not addressed before analysis. This phase ensures the model is built on a clean, verified dataset with documented handling for every anomaly.
Not all claims in a portfolio are equivalent. We use clustering algorithms and classification models to stratify the claim population into cohorts with similar characteristics — injury type, exposure duration, claim size, geographic region, claimant demographics — allowing separate and more accurate valuation of each cohort.
This stratification is critical for class action matters where the adequacy of class representatives and the predominance of common questions depend in part on demonstrating that the class is homogeneous enough to permit class-wide statistical treatment. We build the classification framework to support these legal arguments directly.
For each claim cohort, we model the distribution of likely outcomes — not a single expected value, but a full probability distribution across the range of defensible claim valuations. We run Monte Carlo simulation across the cohort, propagating uncertainty in the per-claim value distribution through to the aggregate estimate.
The output is a settlement range with explicit probability weights: there is a 90% probability that aggregate damages fall between $X and $Y, with a median estimate of $Z. This is what settlement negotiations and mediation actually require — a defensible range, not a point estimate that opposing counsel will attack as arbitrary.
Statistical analysis of large claim portfolios almost always surfaces anomalies: claims with characteristics inconsistent with the stated injury, submissions with implausible timing, providers or claimants appearing across unusually high numbers of claims. We apply isolation forest algorithms, Z-score analysis, and network analysis to flag these claims for further review.
For defendants, this analysis is directly actionable — it identifies the claims most likely to be fraudulent or materially misrepresented, supports targeted discovery, and can significantly reduce aggregate exposure. For plaintiffs, it strengthens the portfolio by identifying and segregating claims that may undermine class cohesion or credibility.
Final deliverables include a written statistical report structured for expert disclosure, a supporting data appendix with full methodological documentation, and a visual exhibit package suitable for mediation, class certification hearings, or trial. All exhibits are designed to communicate complex statistical findings to a non-technical audience without sacrificing precision.
We also prepare a sensitivity analysis that demonstrates the robustness of the aggregate estimate to alternative modeling assumptions — anticipating the questions a court will ask about what happens to the number when key inputs are varied.
Every output is structured around its intended use — class certification, mediation, trial, or settlement negotiation — and formatted to meet expert disclosure requirements.
A statistically derived estimate of total aggregate damages across the claim population, with full cohort-level breakdowns, confidence intervals, and documented methodology suitable for Daubert review.
A probability-weighted settlement range produced by Monte Carlo simulation, showing the full distribution of aggregate outcomes rather than a single contested point estimate. Formatted for use in mediation and structured settlement discussions.
A ranked list of claims flagged by statistical anomaly detection, with supporting data for each flag and a documented methodology for the screening criteria. Designed to support targeted discovery and exclusion motions.
A statistical demonstration of class homogeneity and the predominance of common questions — supporting class certification arguments and providing a framework for allocation of any class-wide settlement fund across individual claimants.
Statistical methods become most powerful at scale — generally 200 or more claims for cohort stratification to be meaningful, and 500 or more for Monte Carlo simulation to produce tight confidence intervals. For smaller portfolios, we adjust the methodology accordingly and are transparent about what the sample size allows us to conclude with confidence.
Yes, when applied correctly. Statistical sampling is well-established in class action litigation and has been affirmed as an appropriate damages methodology in numerous federal and state courts. The key is that the sampling design, population definition, and estimation methodology must be documented, reproducible, and grounded in accepted statistical practice — all of which we build into every engagement from the start.
We work with any structured data format: Excel, CSV, SQL database exports, JSON, XML, and most insurance industry data formats including ACORD. We have experience ingesting and normalizing data from claims management systems, adjuster platforms, and legacy database environments. Raw and partially structured data is acceptable — data cleaning is part of Phase 01.
Yes. Statistical claim analysis is equally applicable to insurance coverage disputes, actuarial reserve analysis, and regulatory reporting contexts. We can build the claim valuation models needed to support coverage negotiations, reinsurance disputes, or regulator inquiries — with the same rigor as litigation-facing work, formatted for the specific audience and forum.
Whether you are approaching class certification, entering mediation, or preparing for trial, the statistical picture of your claim universe needs to be both accurate and defensible. Let us build it.