Portfolio Accelerator · Financial Services · AWS
AWS AML/KYC AI Implementation Partner: Compliance Automation Accelerator
A working AML/KYC compliance accelerator on real AWS infrastructure — SageMaker, Fraud Detector, and Comprehend, governed end to end.
problem
The Problem: Manual AML/KYC Review Doesn't Scale on AWS-Native Data
Alert volume grows faster than review headcount, and most AML/KYC alert queues carry high false-positive rates — analysts spend most of their time clearing alerts that were never real risk, while a smaller number of genuinely suspicious patterns wait in the same queue. An AWS-native compliance program needs an AI layer that reduces that noise without becoming a second black box regulators can't inspect.
demo
What We Built: A Working AML/KYC Accelerator on Real AWS Infrastructure
This page showcases Kriv AI's AML/KYC accelerator — part of our Build-Demo-Vanish portfolio (Finance vertical, AWS) — deployed on real AWS infrastructure against 100% synthetic transaction and customer data.
Architecture: SageMaker, Fraud Detector, and Comprehend Working Together
Amazon SageMaker trains and serves anomaly-scoring models over transaction graphs; Amazon Fraud Detector layers real-time transaction risk scoring on top; Amazon Comprehend handles entity resolution and name-screening against watchlist-style text. A case-management and alert-triage layer routes only high-confidence alerts to human review, so analysts see fewer, better-qualified cases.
Synthetic Data, Real Signal: How We Prove Results Without Touching Client PII
Every transaction, account, and entity in the demo is synthetic — generated specifically for this portfolio build, never real customer or transaction data. The infrastructure and pipeline are real AWS deployments that can be stood up and torn down on demand, so a prospect can request a live walkthrough against a sample data shape within days, not months.
metrics
Measured Results From the Accelerator
In internal synthetic-benchmark testing, the triage layer cut the volume of alerts routed to full manual review by roughly half, by filtering out the lowest-confidence false positives before they reach an analyst — without removing any alert from the audit trail. That's the specific lever AML/KYC teams should evaluate any AI vendor or implementation partner against: fewer alerts reaching analysts, with nothing dropped from the record.
differentiation
Why an AWS Implementation Partner (Not a Big 4 Advisory Deck)
Big 4 firms sell AML/KYC AI strategy decks and roadmaps. Kriv AI's engineering team already built this accelerator and can demo it running on real AWS infrastructure today — the difference between a plan to build something and a system you can watch work.
Governed AI, Not a Black Box: Model Risk Management for AML/KYC
Every model in the pipeline is built to be explainable at the alert level, not just at the aggregate accuracy level — the standard a model risk management review (in the spirit of SR 11-7-style guidance) expects before an AML model goes into production.
partnership
Where This Fits: AWS Partnership and Kriv AI's Compliance Practice
This accelerator sits inside Kriv AI's broader AWS practice and AI governance and compliance-as-a-service offering — the same governance discipline (explainability, audit trail, human-in-the-loop review) applied across every regulated-industry accelerator in our portfolio.
engagement
From Accelerator to Production: Engagement Path and Timeline
A typical engagement starts with a scoped discovery phase against your actual transaction and customer data model, followed by a governed pilot before any production rollout — see our pricing page for engagement structure and floor rates.
Straight answers
Frequently asked questions about AWS AML/KYC AI Implementation Partner: Compliance Automation Accelerator
What AWS services power the AML/KYC accelerator?
Amazon SageMaker for anomaly-scoring models over transaction graphs, Amazon Fraud Detector for real-time transaction risk scoring, and Amazon Comprehend for entity resolution and name-screening, tied together with a case-management and alert-triage layer.
Is any real customer or transaction data used?
No — the accelerator runs entirely on synthetic transaction and customer data generated for this portfolio demo. No client PII is used.
How much does an AML/KYC AI implementation engagement cost?
Engagement scope and cost depend on your data model, existing case-management tooling, and integration surface — see our pricing page for our engagement floor and structure.
How long does it take to go from this accelerator to a production pilot?
A typical path starts with a scoped discovery phase, followed by a governed pilot on your own data before production rollout. Exact timelines depend on data access and integration scope.
How is this validated for model risk management?
The pipeline is built to produce an explanation for every alert, not just an aggregate accuracy score — the standard a model risk review in the spirit of SR 11-7-style guidance expects before an AML model reaches production.
Can we see the accelerator running before we sign anything?
Yes — the AWS deployment can be stood up for a live walkthrough against a sample data shape in a discovery call.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call