Product lead · LBG engagement
A cloud based KYC and AML platform built on IBM Watson.
Compliance work at machine speed, without losing the audit trail.
Compliance at Machine Speed
Know your customer and anti-money-laundering checks are slow, manual, and expensive, and getting them wrong can be costly from a regulatory standpoint. SafetyNet set out to use machine learning to do work that teams of analysts were doing by hand.
Built on Watson
We built the platform on IBM Watson and supporting AI and ML tooling, accessed through REST APIs. That meant designing around what the models could actually do, not what a demo suggested, and being clear about where a human still had to make the call.
Designing for the Auditor
In regulated environments, the output is only half the product. Every decision has to be explainable and every check has to leave a record. We designed the workflows so compliance teams could see why the system flagged something, not just that it did.
Cloud Native from the Start
The platform was built cloud-based and API first so it could integrate with the systems financial institutions already run, rather than asking them to move their data somewhere new.
Highlighted Initiatives
- Led product for a cloud based KYC and AML platform. Owned direction for a compliance product serving financial services.
- Built on IBM Watson. Applied machine learning to identity verification and transaction monitoring workflows.
- Designed API first. Integrations built to fit institutions’ existing systems rather than replacing them.
- Kept the audit trail intact. Designed workflows so compliance teams could see the reasoning behind every flag.
- Balanced automation with human judgment. Defined clearly where the model decides and where a person does.
- Applied AI where it earned its place. Machine learning used to remove manual work, not as a feature in itself.
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