Overview
You sell SEON's fraud prevention and AML compliance platform to companies losing money to fraud - think e-commerce sites, fintechs, online marketplaces, gaming companies. You're working mid-market to enterprise deals where you're helping fraud/risk teams replace manual review processes or outdated rules-based systems. Most of your time goes to demos, explaining how device fingerprinting and behavioral analytics work, and building business cases around fraud loss reduction.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Full-cycle AE (likely demo to close) |
| Sales Motion | Balanced - some inbound from marketing, some outbound prospecting |
| Deal Complexity | Consultative - technical product requiring education |
| Sales Cycle | 2-4 months for mid-market, 4-6 months for enterprise |
| Deal Size | $30K-150K ACV (estimated based on fraud prevention market) |
| Quota (est.) | $500K-800K/year |
Company Context
Stage: Likely Series B/C based on hiring velocity and multi-geo expansion
Size: Unknown but expanding rapidly (hiring across 4+ locations)
Growth: Active hiring in sales, engineering, and support across Austin, Budapest, London, and LATAM - suggests strong product-market fit
Market Position: Challenger in crowded fraud prevention space competing against Sift, Forter, Riskified, and legacy systems
GTM Reality
Pipeline Sources:
- 40% Inbound - Companies actively searching for fraud solutions after getting hit with chargebacks or noticing fraud patterns
- 40% Outbound - Cold outreach to fraud/risk/payments teams at companies in high-fraud verticals
- 20% Referrals/Partners - Payment processors and existing customers making intros
SDR/AE Structure: Likely have SDRs setting some meetings, but you're also doing your own prospecting
SE Support: Sales Engineer team for technical demos and POC support (they're hiring SEs, so they exist)
Competitive Landscape
Main Competitors: Sift, Forter, Riskified, Signifyd (plus legacy rules engines and in-house systems)
How They Differentiate: Likely positioning on AI/ML capabilities, real-time decisioning, and device fingerprinting depth
Common Objections:
- "We already have fraud rules in place"
- "Our fraud rates are acceptable" (until you show them what they're missing)
- "Too expensive for our volume"
- "Integration sounds complex"
Win Themes: ROI based on fraud loss reduction, false positive reduction (fewer good customers declined), faster integration than competitors
What You'll Actually Do
Time Breakdown
Active Deals (40%) | Prospecting (30%) | Demos/Discovery (20%) | Internal (10%)
Key Activities
- Discovery calls with fraud/risk teams: You're asking about their current fraud rates, false positive rates, what fraud vectors they're seeing (account takeover, payment fraud, promo abuse). Most don't have clean answers.
- Product demos: Walking through how device fingerprinting works, showing the fraud scoring UI, explaining velocity rules and machine learning models. Expect lots of technical questions about data privacy and GDPR compliance.
- Building business cases: You need to quantify their fraud losses and show ROI. This means digging into their chargeback data, manual review costs, and false decline rates. Many don't track this well.
- Managing POCs: Working with the SE team to get a proof-of-concept running. This involves API integration work and usually takes 2-4 weeks. Many POCs get delayed by their engineering team's bandwidth.
The Honest Reality
What's Hard
- Fraud isn't always a priority until it becomes a crisis. You'll have deals that go cold for months until they get hit with a fraud wave.
- Technical buying process - you need buy-in from fraud team, engineering, legal (data privacy concerns), and procurement. Lots of stakeholders to coordinate.
- ROI is clear in theory but messy in practice - their fraud data is often incomplete, so building the business case requires assumptions
- You're competing against "do nothing" - many companies accept fraud as a cost of doing business
- Integration timelines slip constantly because you're dependent on their engineering team
What Success Looks Like
- Closing 8-12 deals per year at $50K-100K ACV
- Building a pipeline of 3-4x your quota (lots of deals stall)
- Getting good at identifying companies with acute fraud pain vs those just exploring
- Learning to speak both business language (ROI, fraud rates) and technical language (API integration, false positive tuning)
Who You're Selling To
Primary Buyers:
- Head of Fraud/Risk (main champion - they feel the pain daily)
- Director/VP of Payments or Operations (budget holder)
- Engineering leads (integration stakeholder)
- Legal/Compliance (data privacy sign-off)
What They Care About:
- Reducing fraud losses without killing conversion (don't want to block good customers)
- Manual review workload - they're drowning in reviewing edge cases
- Speed to value - how fast can this be live?
- Data privacy compliance - especially GDPR in EU markets
- Total cost vs. fraud saved - clear ROI story
Requirements
- 2-4 years selling B2B SaaS, ideally in fraud/risk, fintech, payments, or security
- Comfortable with technical products - you need to explain APIs, webhooks, and data flows
- Experience managing consultative deals with multiple stakeholders
- Used to building ROI models and business cases
- Self-starter mentality - you'll be prospecting and closing simultaneously
- Willingness to learn fraud prevention domain (chargebacks, ATO, card testing, etc.)