Every property and casualty claim hinges on testimony. Not documents, though documents matter, and not data fields in a claims system, though those matter, too. What moves a claim from first notice of loss to final resolution is what people say under oath and on the record.
How well we create and manage testimony is often the difference between a good outcome and a poor one—a fairly resolved claim or an overpaid claim. This includes the insured's recorded statement, an examination under oath, the treating physician's account of causation, the accident reconstructionist's opinion, and the corporate representative's answers in a bad-faith suit.
Testimony—the oral record—is the currency we use to analyze risk and construct the narratives that support our positions. Reserves are set on it. Settlements are priced against it. Juries decide on it. When a claims organization is good at its work, it understands, dissects, connects, and redeploys testimony. When it is not, it overpays, settles cases it should have tried, and tries cases it should have settled.
If testimony is the currency, most claims organizations are managing their finances by hand, using an abacus. A deposition is taken, read once by assigned defense counsel, and summarized in a report that lands in a claim file, then effectively disappears. The knowledge inside it—how a particular plaintiff's expert testifies about future medical care, which questions or contradictions unsettle a professional expert, and how a repeat-player plaintiff firm builds its damages case—is gathered once and then thrown away. The organization paid for the transcript and the hidden data it contains. Yet almost none of that value is used beyond the single matter that produced it.
The Moneyball Parallel, One Step Further
One of us argued in a prior piece, "Moneyballing Litigation," that litigation teams still select witnesses and lawyers on gut impression, much as baseball general managers once selected players based on how they looked in a uniform rather than on objective data. That argument holds for claims, as well. The vast trove of deposition data that could inform claims decisions remains largely unmined. This article extends that assertion.
To be fair, this kind of data mining was impossible not long ago. Nobody could read across 10 years of transcripts from every case a carrier had handled, pull out every instance of a given expert opining on lumbar disc herniation, and compare those instances for consistency. The labor was prohibitive.
Today, however, we have moved from manual human review as the only option to machine-first processing and analytics as a true capability. This expands both what we can understand and what we can do with the most important currency we manage—testimony. The emerging field of testimony analytics is creating opportunities for insurers to capture both efficiencies and strategic advantages. Organizations that learn to analyze that medium at scale will price claims and risk more accurately than those that do not. Let's look at how.
Two Levels of Value: The Case and the Portfolio
Testimony analytics creates value at two levels: the individual case and the broader portfolio. Both levels produce efficiencies, but the portfolio level also unlocks strategic advantages unavailable within a single case.
The first level is the individual case. Obtaining, reviewing, and analyzing testimony consumes an extraordinary amount of billable attorney time. Yet much of that work still relies on tools and methods that have changed little in decades. AI and testimony analytics reduce that burden by helping counsel search and summarize transcripts, identify admissions, and compare testimony with medical records and other evidence. For claims organizations embracing AI, tasks that once required hours of billable time can now be completed in minutes, producing faster turnaround and lower attorney fees. Most industry attention remains focused on efficiencies at this level because the savings are both conspicuous and tangible.
The larger opportunity lies at the portfolio level: leveraging aggregate data. By treating all of an organization's testimony as a single, queryable body of knowledge, transcripts become more than case files—they become institutional memory. Across matters, they record the statements, strategies, and behaviors of insureds, company witnesses, retained experts, opposing experts, defense counsel, and adverse attorneys.
By extracting and leveraging that aggregate history, a claims organization can identify patterns that no single case reveals. It can better prepare witnesses, evaluate repeat experts, profile recurring firms and attorneys, detect anomalies and contradictions across claims, and improve outcomes across its portfolio. By mining information hidden in testimony, organizations can use previously ignored data not only to increase efficiency, but also to gain a strategic advantage through better-informed decisions and increasingly reliable predictions. In testimony, the past is often prologue.
It is now possible to turn testimony into a searchable body of institutional knowledge and use it to generate a wide range of analytics. As data is added, the value compounds: every new transcript strengthens the system, transforming testimony the organization has already paid for into a reusable data asset rather than dead weight in a file room. For a large insurer responding to a disaster, that could mean identifying recurring participants, uncovering potential fraud, avoiding improper payments, and recovering millions.
The Early-Mover Advantage Matters
Claims organizations that build a portfolio-level testimony capability will out-execute those that do not, and the gap will widen rather than close.
Managing a claim well means optimizing the creation of testimony and then using it effectively—in motion practice, settlement negotiations, or before a jury. An organization that grounds its decisions in its accumulated testimony data can make a better-informed judgment.
An organization relying on the assigned adjuster's memory and the handling attorney's instinct is guessing. On one claim, the guess might beat the model. Across 5,000 claims, it will not. As in baseball, better information yields more wins on average, and claims is a business of averages.
A second, equally important reason to act is the meaningful risk that the plaintiff bar will deploy these capabilities at scale sooner. Plaintiff firms understand the power of technology and are using it to widen the intake funnel and increase case volume. Far more investment is flowing into plaintiff-side technologies than into defense-side technologies. The hundreds of millions of dollars invested across a growing group of plaintiff-side AI platforms illustrate the scale of that effort.
Where This Leaves Claims Leadership
In the end, organizations that learn to manage and analyze testimony at scale will handle claims more efficiently, price risk more accurately, deploy better strategies, and improve outcomes across their portfolios.
"Moneyballing Litigation" imagined sealed envelopes containing hidden statistics about witnesses and attorneys. The data in those envelopes already existed; it was simply scattered across transcripts, matters, firms, and years. Testimony analytics makes it possible to open those envelopes at scale—to learn from every witness, expert, attorney, and firm an organization has encountered and apply that knowledge to every matter that follows. That is the opportunity now before claims leadership.
