What challenges did Cockroach Labs face before AI implementation?

Detect evolving financial fraud in real time with low latency and low false positives. Scale vector similarity search over billions of historical fraudulent transaction embeddings.

What was the impact of the AI solution?

The article shows a limited vector scan replacing a full table scan for similarity search, indicating lower latency. It describes real-time fraud monitoring dashboards and immediate transaction validation flows. It states that the design helps reduce false positives and detect fraud within milliseconds.

Source

CockroachDB: real-time fraud detection pipeline using AWS Bedrock, SageMaker, and Lambda (embeddings + decisioning)

Use case typeFraud detectionUpdated Jun 13, 2026

Cockroach Labs describes a real-time financial fraud detection pipeline built around CockroachDB and AWS services. The article explains how historical transaction data is used to train anomaly and fraud models in Amazon SageMaker, and how Amazon Bedrock is used to generate embeddings for fraud-related transaction data. Live transactions are ingested through Amazon Kinesis Data Streams and AWS Lambda, which applies rule-based filtering, calls SageMaker model endpoints, computes similarity against historical fraudulent vectors, and writes results into CockroachDB for low-latency decisioning and monitoring.

Organization: Cockroach Labs
Industry: Finance
Location: United States
Published: June 2026

Reported outcomes

Strategic outcomes

Risk & complianceReduced false fraud positivesSpeed & agilityEnabled real-time fraud detectionScale & capacityScaled similarity search over billions of vectorsNew product / capabilityBuilt real-time fraud decisioning pipeline

Primary read

Use case focus

Showing 3 of 4

1Fraud Detection
2Real-time Decisioning
3Anomaly Detection

Detect evolving financial fraud in real time with low latency and low false positives.
Scale vector similarity search over billions of historical fraudulent transaction embeddings.

Train anomaly and fraud models on historical transactions with Amazon SageMaker, including Random Cut Forest and XGBoost.
Use Amazon Bedrock to vectorize fraud-related transaction data into embeddings.
Ingest streaming transactions through Amazon Kinesis Data Streams and AWS Lambda.
Apply rule-based prefilters, similarity search, and trigger-driven decisioning against CockroachDB vector indexes.
Store fraudulent transaction vectors and monitoring results in CockroachDB for fast lookup and visualization.

Technologies

Amazon SageMaker Amazon Bedrock AWS Lambda Amazon Kinesis Data Streams Amazon S3 CockroachDB vector indexing

The article shows a limited vector scan replacing a full table scan for similarity search, indicating lower latency.
It describes real-time fraud monitoring dashboards and immediate transaction validation flows.
It states that the design helps reduce false positives and detect fraud within milliseconds.

Architecture

Historical transaction datasets are loaded from Amazon S3 into Amazon SageMaker for training. Fraud and anomaly scores are generated with SageMaker models, while Amazon Bedrock creates embeddings for historical fraud data. AWS Lambda consumes Amazon Kinesis Data Streams to process live transactions, apply rules, and trigger similarity search and alerts. CockroachDB stores transactions and fraudulent vectors, and its vector index is used to reduce similarity-search scan scope and latency.

Sources & evidence1

Groundedness: 4/5Type: Blog PostPublished: Jun 4, 2026Publisher: Cockroach LabsEvidence: VendorConfidence: Medium

Primary source

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