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BP-022Risk & Compliance

AI Fraud & Anomaly Detection

Catch suspicious transactions and unusual activity in real time — with explainable flags.

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Project range
$9,000–20,000
AWS running cost
$100–450/mo
Time to deploy
4–7 weeks
Best-fit industries
E-commerce, Financial services

Executive summary

A streaming detection system that scores transactions and account activity as they happen, combining rules, statistical anomaly detection, and AI reasoning to flag likely fraud while minimizing false positives. Each alert comes with a plain-language explanation and the evidence behind it, so analysts act quickly and customers aren't wrongly blocked.

Business problem

Fraud is caught too late — after a chargeback or loss — while overly blunt rules decline good customers and erode revenue and trust. Small teams can't manually review every transaction, and static rules can't keep pace with evolving fraud patterns.

Architecture

AWS services

Amazon Kinesis

Messaging
  • — Ingest transaction and activity events in real time
  • — Buffer bursts for smooth processing

AWS Lambda

Compute
  • — Score events against rules and anomaly models
  • — Manage case creation and state

Rules + anomaly model

Compute
  • — Velocity, threshold, and statistical-outlier detection
  • — Configurable risk scoring

Amazon Bedrock

AI / ML
  • — Explain why an event was flagged
  • — Assist analyst investigation with context

Amazon DynamoDB

Database
  • — Scores, case state, and entity risk profiles

Amazon S3

Storage
  • — Immutable audit history for disputes and model tuning

Amazon SNS

Messaging
  • — Alert analysts on high-risk activity

Amazon CloudWatch

Observability
  • — Logs, metrics, and cost alarms

Data flow

  1. 1

    Transactions and account events stream into Kinesis in real time.

  2. 2

    Lambda scores each event with rules and an anomaly model; high-risk cases are created in DynamoDB.

  3. 3

    Bedrock generates a plain-language explanation and gathers context for the analyst.

  4. 4

    SNS routes high-risk alerts to a review queue; low-risk events pass through without friction.

  5. 5

    Every decision is preserved immutably in S3 for disputes, audits, and model improvement.

Security considerations

  • Sensitive transaction data encrypted at rest and in transit; tokenized where possible.
  • Least-privilege IAM; segregation between scoring and case resolution.
  • Explainable flags with evidence — no unexplained blocks.
  • Immutable audit trail supports chargeback and regulatory defense.

Cost considerations

  • Kinesis throughput and Bedrock explanations are the main variable costs.
  • AI reasoning is applied to flagged events only, not every transaction.
  • DynamoDB and S3 are inexpensive with lifecycle tiering.

Scalability

  • Stream-based and serverless; scales to high event volumes.
  • New rules and signals deploy without downtime.
  • Risk thresholds tunable per segment to balance loss and friction.

Deployment roadmap

Phase 1 — Signals & thresholds

Weeks 1–2
  • — Define fraud patterns, signals, and risk appetite
  • — Provision streaming and AWS foundation

Phase 2 — Build & backtest

Weeks 3–5
  • — Build scoring pipeline and case management
  • — Backtest against historical fraud and good traffic

Phase 3 — Launch & tune

Weeks 6–7
  • — Launch in shadow then enforcing mode
  • — Tune to minimize false positives

Future enhancements

  • Adaptive models that learn from analyst decisions.
  • Network/graph analysis to detect coordinated fraud rings.
  • Step-up verification instead of hard declines.
  • Loss and false-positive reporting into the executive dashboard.