Aidena

— AI STACK RECOMMENDATION

Real-time AI Fraud Detection for Fintech

Scalable fraud detection stack combining real-time anomaly detection, model serving, and observability for fintech transactions with sub-second latency and cost efficiency.

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Finance

Real-time AI Fraud Detection for Fintech

Scalable fraud detection stack combining real-time anomaly detection, model serving, and observability for fintech transactions with sub-second latency and cost efficiency.

high confidence

Core Stack ℹ︎

AWS Bedrock

Primary

Managed foundation models for real-time fraud pattern analysis and anomaly scoring without managing inference infrastructure. Scales automatically for transaction volume spikes.

$0.50-2/1M tokens

Baseten

Primary

Production-grade ML model serving with auto-scaling GPU support and sub-100ms latency for custom fraud detection models. Pay-per-inference pricing ideal for variable transaction loads.

$0.10-0.50/hour

Airbyte

Primary

Real-time data integration from payment processors, wallets, and transaction sources into data warehouse for training and feature engineering. 300+ connectors enable rapid fintech integrations.

$0/month (self-hosted) or $100-500/month (cloud)

Complete the Stack ℹ︎

Datadog LLM Observability

Alternative

Enterprise monitoring for fraud detection model latency, cost per transaction, and error rates. Correlates LLM-based anomaly scoring with infrastructure metrics for root cause analysis.

$299-999/month

Elasticsearch Vector Search

Alternative

Hybrid search combining vector embeddings of transaction patterns with full-text fraud rules. Fast retrieval of similar historical fraud cases for real-time context.

$0/month (self-hosted) or $100-500/month (cloud)

Getting started

  1. 1Set up Airbyte pipelines to ingest real-time transaction data from payment APIs into a data warehouse (Snowflake/BigQuery).
  2. 2Train custom fraud detection model using historical transaction data and deploy to Baseten for sub-100ms inference.
  3. 3Create AWS Bedrock integration for LLM-based anomaly explanation and risk scoring on flagged transactions.
  4. 4Implement Elasticsearch vector index of transaction embeddings for similarity-based fraud pattern matching.
  5. 5Configure Datadog monitoring to track model latency, false positive rates, and cost per transaction.
  6. 6Build API layer that orchestrates Baseten model calls with Bedrock explanations for real-time fraud decisions.
  7. 7Set up alerting for anomalies exceeding fraud thresholds with automatic transaction blocking.
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