Overview
You're the fraud expert who helps customers get the most out of SEON after they buy. You analyze their fraud patterns, help tune detection rules to reduce false positives, train their fraud teams on using the platform effectively, and recommend configuration changes based on what's working. You're part consultant, part analyst, part trainer. Your success is measured by customer satisfaction, fraud detection improvement, and whether customers renew/expand.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Technical Customer Success / Consultant |
| Sales Motion | Post-sale value delivery and optimization |
| Deal Complexity | Consultative - analyzing data and making recommendations |
| Sales Cycle | N/A - working with existing customers |
| Deal Size | N/A - supporting customers across all segments |
| Quota (est.) | Measured on customer health scores, CSAT, optimization metrics |
Company Context
Stage: Likely Series B/C (having dedicated fraud consulting roles shows maturity)
Size: Unknown but growing customer base
Growth: Fraud Consultant role suggests they have enough customers needing ongoing optimization support
Market Position: Challenger focused on customer success and technical depth
GTM Reality
Customer Allocation: You likely support 15-25 active customers at various stages of maturity
High-Touch Customers: 5-8 enterprise customers with weekly/bi-weekly touchpoints
Medium-Touch: 10-15 mid-market customers with monthly check-ins
Engagement Model: Mix of scheduled optimization reviews and reactive support when fraud patterns change
Competitive Landscape
Why This Role Exists: Fraud prevention tools are only as good as their configuration. Many customers buy but don't optimize, leading to poor results and churn. You prevent that.
Value Delivery:
- Reducing false positives (so good customers don't get blocked)
- Improving fraud catch rates
- Reducing manual review workload
- Training teams to be self-sufficient
What You'll Actually Do
Time Breakdown
Data Analysis (30%) | Customer Calls (30%) | Training (20%) | Documentation (20%)
Key Activities
- Fraud data analysis: You're pulling reports from SEON to analyze their fraud patterns. You're looking at false positive rates, fraud catch rates, which rules are triggering most, and where legitimate transactions are getting blocked. You're identifying patterns they're missing.
- Optimization sessions: You're on calls with their fraud team recommending specific rule changes, velocity thresholds, or ML model tuning. "Your false positive rate on Europe transactions is 8%, which is high. Let's adjust the device fingerprint scoring for that region."
- Training fraud analysts: You're running training sessions for their team on how to use SEON's interface, investigate flagged transactions, and interpret fraud scores. Many fraud analysts are new to AI-based tools and need hand-holding.
- Quarterly reviews: You're preparing business reviews showing their fraud metrics over time, benchmarking them against similar customers, and recommending next steps. This feeds into the AM's renewal conversations.
- Custom configuration work: For larger customers, you're building custom fraud rules based on their specific business logic, fraud patterns, or industry requirements.
- Firefighting fraud waves: When a customer gets hit with a new fraud attack pattern (credential stuffing, card testing, promo abuse), you're jumping in to help configure emergency rules and analyze the attack.
The Honest Reality
What's Hard
- You inherit customers from sales - some were sold unrealistic expectations or configured poorly during onboarding
- Fraud patterns constantly change, so configurations that worked last month may not work today
- Customers often don't have clean data or don't track their fraud metrics well, making your analysis messy
- You're balancing competing goals: catching more fraud vs. not blocking good customers (always a tradeoff)
- Getting customers to actually implement your recommendations - many nod along but don't make changes
- Austin-based but supporting global customers means odd-hour calls sometimes
- You need to become an expert in their business model and fraud risks quickly
What Success Looks Like
- Customers see measurable improvement in fraud catch rates and false positive reduction
- High CSAT scores and positive feedback in your account health reviews
- Customers become self-sufficient - they stop needing you as much for basic optimization
- Your book of customers has high retention and expansion rates
- You spot emerging fraud trends across your portfolio and share insights internally
Who You're Working With
Primary Contacts:
- Fraud Analysts (day-to-day users reviewing transactions)
- Fraud Managers (decision makers on rule changes)
- Risk/Operations Directors (care about business impact and ROI)
What They Care About:
- Reducing fraud losses without killing conversion
- Making their fraud team's job easier (less manual review)
- Understanding why certain transactions are flagged
- Keeping up with new fraud attack patterns
- Getting to self-sufficiency with the tool
Requirements
- 2-5 years in fraud prevention, risk analysis, or payments
- Hands-on experience with fraud detection tools, rules engines, or data analysis
- Comfortable analyzing data and explaining findings to non-technical people
- Understanding of common fraud types (payment fraud, account takeover, promo abuse, etc.)
- Customer-facing experience - you're consulting and training, not just analyzing
- Strong communication skills - you're translating technical concepts for fraud teams
- Self-directed - you manage your own book of customers and prioritize your time
- Bonus: Experience with machine learning fraud models, SQL, or Python for data analysis