AI Fraud Detection for a Neobank Startup
Graph + behavioral fraud scoring that cut false declines while catching account-takeover rings for an early-stage neobank.
Key Details
| Challenge | Rules-only fraud checks blocked good users and still missed coordinated account-takeover attempts. |
|---|---|
| Solution | A real-time fraud scorer combining device, velocity and graph features with an investigator console. |
| Technologies | Python, Kafka, Redis, PostgreSQL, AWS |
Technologies used
Python
Apache Kafka
Redis
PostgreSQL
AWS
Docker
Client background
A neobank startup shipping cards and P2P transfers hit a wall: static rules either annoyed legitimate users with declines or let mule rings through. Fraud ops lived in Slack threads with no shared case model.
Key challenges
- False declines hurt activation and support costs for a brand still earning trust.
- Coordinated rings shared devices and funding patterns rules could not see.
- No real-time feature path — batch jobs were too slow for card authorizations.
- Investigators lacked a prioritized queue with evidence packs.
What we built
- Streaming features for device fingerprint, velocity and peer-graph risk.
- Online fraud score service with kill-switches and reason codes.
- Investigator console with case notes, linked entities and disposition outcomes.
- Feedback loop so confirmed fraud retrains weekly challenger models.
Project team: 6 engineers across AI/ML, backend and product engineering — delivery over 14 weeks for a startup team that needed to ship, not slide decks.
How we delivered
01
Baseline
Measured false decline vs. fraud loss and mapped the hottest attack patterns.
02
Features
Stood up streaming feature jobs with latency budgets for auth time.
03
Score
Shipped v1 scorer with human review for mid-risk band.
04
Operate
Trained fraud ops and locked SLAs for model and rule changes.
Business impact
-35%False declines
+2.1×Fraud catch rate
<80msScore latency