# Tamas Kadar

Tamas Kadar writes about fraud prevention and risk strategy at SEON, including how companies can detect coordinated fraud and manage risks from AI shopping agents.

- Profile: https://b2binfluencer.marketing/creators/tamas-kadar/
- LinkedIn: https://www.linkedin.com/in/tomkadar
- LinkedIn followers: 29,467
- Median of last 9 posts: 69 reactions, 4 comments (checked 2026-11-02)
- Topics: artificial-intelligence, leadership, startup, agentic-commerce
- Based on 8 dated posts, latest 2026-10-07

## Summary

Most useful for practitioner content and webinars on fraud detection patterns and verification for AI shopping agents.

- Best for: B2B fraud-prevention, risk, AML or fintech brands wanting expert explainers, case studies or webinars.
- Not for: Consumer lifestyle, fashion or short-form video campaigns aimed at a broad general-shopping audience.
- Check first: Ask for audience breakdown and comparable session results; only a LinkedIn follower count is supplied.

### Takeaways

- A 200-account sign-up ring was caught through four signals after device ID and IP linking failed.
- The same battery level across 5,000 accounts helped expose a device farm.
- AI shopping agents need verify-once, score-every-step treatment, adding friction only when needed.

## Main ideas

- **Any single fraud check can be passed, so detection depends on many signals read together**: Kadar's most consistent position is that fraudsters adapt to whatever single signal a team watches, and that identifying them requires several signals interpreted together. Across case posts, the pattern is the same: accounts look unrelated on the obvious identifiers, then several smaller anomalies line up and reveal one operation behind them. The device or bot's own characteristics, not its stated identity, carry the proof.
- **AI shopping agents need verify-once, score-every-step treatment rather than a block-or-allow decision**: Kadar frames agentic commerce as a live commercial split that merchants are resolving inconsistently. Blocking agents also blocks the customers behind them; admitting agents opens the door to fraudsters travelling with them. The third path Kadar advocates ties an agentic session to a known customer, issues a token that cannot be reused or stolen, and then keeps scoring trust at every step, adding friction only when good and bad cannot be told apart.
- **Fraud teams sit on data that can grow revenue and should use friction selectively**: Kadar argues that the signals used to predict who will steal can also predict who will spend, so risk teams are revenue engines rather than blockers. The mechanism they describe is dynamic friction: make things easier for high-value customers and harder for suspected fraudsters, rather than applying the same steps to everyone. The same continuous-scoring idea carries into the agentic commerce argument, where friction is added only when the system cannot distinguish good from bad.
- **Fraud has moved to places identity checks do not look**: Kadar repeatedly returns to fraud that arrives after, or around, a clean identity check. Accounts opened by real people with their own documents pass KYC and are then handed over; deepfakes now clear biometric checks that once caught them; authorized push payment fraud involves no break-in at all because the victim sends the money. A related tension Kadar reports is that products ship faster than risk teams can write rules for them.
- **Building the company deliberately is part of the public argument**: Outside fraud typologies, Kadar writes about how SEON is built and staffed, treating hiring and internal events as deliberate choices rather than background. The company posts carry the same structured, list-driven style as the fraud posts, and the hackathon is presented as product research rather than only team culture.

## Work to read

- [How a 200-account sign-up ring was caught from four signals](https://www.linkedin.com/posts/tomkadar_how-do-you-catch-200-scammers-abusing-sign-up-activity-7521829584980537344-Rrw3)
- [What a 5,000-account marketplace fraud ring reveals about AI-run device farms](https://www.linkedin.com/posts/tomkadar_sites-like-this-let-fraudsters-run-fake-accounts-activity-7508769610998026240-KYRb)
- [From a university crypto exchange to 1,100 fraud signals](https://www.linkedin.com/posts/tomkadar_in-2016-bence-jendruszak-and-i-ran-a-crypto-activity-7521466353174654976-9g9v)
- [The three options for merchants when AI shopping agents arrive](https://www.linkedin.com/posts/tomkadar_metas-muse-and-openais-dots-can-now-shop-activity-7513479638271262720-7JFE)
- [Money20/20: AI agent liability, risk shipping late, and mule accounts](https://www.linkedin.com/posts/tomkadar_3-reflections-from-money2020-1-the-industry-activity-7520741497865175063-bGLw)
- [Why fraud teams can become revenue engines](https://www.linkedin.com/posts/tomkadar_how-do-you-turn-your-fraud-team-into-a-revenue-activity-7518915471124094976-P2Qf)

## Brand relationships

- **SEON** (employment)
- **Y Combinator** (client)

## Partnerships

The reviewed material contains no published sponsorship policy, media kit, rate card, or partnership inquiry route for Tamas Kadar. What it shows instead is how he currently appears in public: panel discussions alongside fraud, identity, and payments vendors; guest conversations on industry podcasts; SEON-hosted roundtables and hackathons; and customer case studies. Those appearances promote SEON's fraud-prevention products rather than presenting a defined creator partnership package, so a buyer cannot yet plan deliverables, pricing, usage rights, exclusivity, or turnaround from the evidence reviewed. Treat the absence as an open research gap rather than as a statement that no sponsorships are ever accepted.

## Alternatives

- Roberto Machado: Choose this alternative when the webinar or co-authored piece is aimed at ecommerce and marketing leaders rather than fraud teams, and the question is how selling through AI agents changes the merchant's own stack. The posts worked through the operational consequences of letting Meta's Muse sell for a Shopify store: the order still lands in Shopify, so a connected email platform can still trigger post-purchase flows, while website analytics pixels do not fire during the agent checkout. A companion post covered connecting ChatGPT ads to HubSpot and Shopify and OpenAI testing conversational ads. That is the enabling-side view: how a merchant makes agent commerce work commercially and keeps follow-up working.
- Dan Willoughby: Choose this alternative when the session should train merchants to test and prepare their own product data for shopping agents rather than explain fraud controls. The posts supply a ready-made exercise — ask one of the current personal agents to buy your own product and watch what it reads — and the argument that an agent works only from what a brand has put where software can read it, filling gaps with the average when product pages, reviews and claims are thin. A second post brought a benchmark of LLM agents running a simulated store for a year, where the best earned 27.3% of what human beginners did, and adding a memory-and-skills harness lifted results. That supports a session about encoding a company's specific knowledge where an agent can act on it.
- Stephen Moock: Choose this alternative when the audience is platform, marketplace and commerce-product teams rather than fraud teams, and the session should cover how a portfolio of merchants gets agent-ready across several agents. The posts explained a single-integration model that makes an entire catalogue discoverable to a range of AI agents, with checkout, payments and fraud protection bundled, and listed platform and brand early adopters by name. A second post read OpenAI's pullback of checkout inside ChatGPT as evidence that discovery matters more than a native buy button and that open, agent-agnostic infrastructure wins long term.

---
AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/