Technically Legal – A Legal Technology and Innovation Podcast
From Water Pistols to Tanks: Why Data Science Is the Gold Standard to Counter Class Action Fraud (Donald Beshada, CEO, Covalynt)
Technically Legal – A Legal Technology and Innovation Podcast
•March 5, 2026•56 min
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Technically Legal – A Legal Technology and Innovation Podcast•Mar 5, 2026
Why It Matters
As class‑action settlements grow in size and complexity, distinguishing legitimate claimants from fraudulent ones is critical to ensuring fair compensation and protecting settlement funds. Covalent’s data‑driven approach demonstrates how legal tech can enhance accuracy, reduce costs, and restore trust in mass‑litigation processes, making the episode especially relevant for lawyers, firms, and companies navigating modern consumer litigation.
Key Takeaways
- •Data science detects fraudulent class action claims efficiently.
- •Traditional notice methods rely on outdated print-impression formulas.
- •Digital targeting boosts claimant response rates from 1% to 8‑9%.
- •Covalent offers analytics platform for settlement administration and fraud prevention.
- •Defense and plaintiff lawyers need distinct skill sets.
Pulse Analysis
The Technically Legal episode spotlights how Covalent’s data‑science engine is reshaping class‑action litigation. Former litigator Donald Bishada explains that fraudulent claimants—bots, coupon sites, and fragmented digital identities—have turned settlement distribution into a multi‑billion‑dollar problem. By applying machine‑learning models to claimant data, Covalent can flag suspicious submissions, streamline verification, and protect defendants from overpaying. This analytical approach contrasts sharply with the “water‑pistol” tactics lawyers once used, positioning data analytics as the new gold standard for fraud detection in mass‑tort settlements.
Bishada also traces the evolution of class‑action notice practices. Historically, courts required reach metrics based on print‑media impressions—People Magazine, USA Today, and similar outlets—using a 70‑80% coverage threshold derived from 1980s circulation data. Today, digital advertising and targeted social‑media campaigns deliver far higher engagement, lifting claimant response rates from a typical 1% to 8‑9%. Covalent leverages these modern channels, providing courts with more accurate reach analyses and reducing reliance on outdated formulas. The shift not only improves notice effectiveness but also cuts costs for administrators and defendants alike.
For the broader legal industry, the conversation underscores two key trends: the necessity of specialized skill sets on both plaintiff and defense sides, and the accelerating adoption of legal‑tech platforms. Defense lawyers benefit from analytical rigor, while plaintiff teams need rapid, “gunslinger” tactics to mobilize claimants. Covalent’s litigation data platform bridges this divide, offering real‑time insights, predictive claim rates, and automated settlement administration. As courts and firms recognize the value of data‑driven strategies, firms that integrate such technology will gain competitive advantage, reduce fraud exposure, and streamline class‑action processes for all stakeholders.
Episode Description
Donald Beshada, former litigator turned legal tech entrepreneur and CEO of Covalynt shares his journey from big-law employment litigation to the forefront of using data science in litgiation. Specifically, to address systemic fraud in class action settlements.
The conversation explores the evolution of claims administration—from the traditional "People Magazine" notice era to the current digital landscape dominated by targeted advertising and sophisticated fraud bots. Donald explains how his company uses data science and identity resolution to bring "scientific rigor" to ensure class action settlements reach legitimate claimants while filtering out fraudulent activity.
Key Takeaways:
The Shift in Fraud: How class action fraud evolved from "couponing" websites to sophisticated, non-US-based bot attacks.
Defensible Clarity: Why "gut feelings" about fraud don't hold up in court, and the necessity of providing an evidentiary framework for disqualifying claims.
Data Science vs. Traditional Settlement Administration: A look at how the Apple Antitrust case served as an inflection point, proving that old-school matching methods are no longer sufficient for class certification or ascertainability.
The Future of Notice: Moving toward a world where data science can connect retail purchases directly to individuals, potentially eliminating the need for expensive, broad-market advertising.
Show Notes
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