Can you deploy alongside our existing fraud stack?

Yes. Common pattern: your existing vendor makes the auth decision; our graph runs in parallel and flags rings, ATO patterns, and synthetic identities the vendor missed for offline review. Over time, high confidence graph decisions get promoted into the live auth path.

How do you handle explainability for adverse action letters?

Every decline includes an audit trail: which features triggered which threshold, which graph paths were involved, which model version made the call. For adverse action eligible declines we emit ECOA compliant reason codes that trace to specific graph features.

What ROI have you seen on graph based fraud detection?

Common benchmarks: 30 to 50% reduction in fraud losses on the categories the graph specifically targets (rings, ATO, synthetic identity), and 10 to 20% reduction in false positive declines because the graph provides context traditional models miss. Payback is typically 6 to 12 months at fintech scale.

How long does a fraud pilot take?

8 to 12 weeks. Weeks 1 to 3: data audit, feature engineering, historical backtest. Weeks 4 to 8: production graph build + real time scoring path. Weeks 9 to 12: shadow mode alongside your existing system, then cutover with your fraud ops team.

AI & Machine Learning

AI Fraud Detection for Fintech

Knowledge graph fraud detection that catches rings traditional ML models miss. Sub 100ms decisioning at authorization time.

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What is AI fraud detection?

The Answer

AI fraud detection for fintechs and payment platforms combines knowledge graphs with machine learning to detect fraud rings in real time. We build graph patterns to spot relationships (accounts, cards, devices, IPs) the fraudsters share, score them in sub-100ms so every authorization sees the full picture, and reduce false positives so legitimate customers don't get blocked.

What you get

Three outcomes we commit to before we start.

+40% true positive lift on fraud rings

Traditional ML fraud models score transactions in isolation. Graph based detection scores the entity network (merchant, device, IP, phone, account age) and their relationships to known bad actors. Rings that hide from row level models get caught.

Sub 100ms decisioning at auth time

Graph queries are pre cached for the hot 20% of entities. p95 latency lands under 60ms: inside authorization SLAs for card networks, ACH, and instant payment rails.

Explainable declines

Every decline surfaces the exact suspicious relationship path (device → merchant → known fraud actor within N hops). Your fraud analysts stop guessing; your customers get useful decline reasons.

The Guaranteed Production Pilot

Fixed scope · Written target

A production AI Fraud Detection system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working AI Fraud Detection deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

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