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Case study

Talisai

Explainable AI Risk Platform

AI Risk Management AML Compliance Product Development
Top 10 Enterprise Security Magazine: Automation Security Providers 2020
3 Product modules on shared infrastructure
2 Deployment models: SaaS for standard clients, on-premises for data sovereignty requirements
The challenge

The gap between AI analytical capability and operational usability in regulated environments is a transparency problem.

AI adoption in regulated industries stalls at the same point every time: the model produces a score and nobody can explain why. A compliance officer cannot act on an AML alert they cannot justify to a regulator. A risk manager cannot defend a people-risk decision in a legal context.

The gap between AI analytical capability and its operational usability in regulated environments is a transparency problem. That was the founding insight behind Talisai. They needed explainability as a core architectural principle, not an audit log bolted on afterwards.

What we built

Talisai's full SaaS platform, built from scratch, across three product modules.

AML for Crypto Exchanges: real-time transaction monitoring with risk scoring and full rationale. Compliance officers see not just the alert but the specific risk indicators that triggered it, each weighted and traceable. Configurable risk indicator libraries that evolve with regulatory requirements without requiring a rebuild.

People At Potential Risk (PAPR): out-of-the-box risk factor monitoring for customers, business partners, and employees, with company- and industry-specific configurable factors. Hybrid deterministic and ML models: the explainability of rules combined with the pattern-recognition of learned models.

Supply Chain Vendor Risk: vendor security and privacy compliance monitoring against ISO 27x, SOC2, and HIPAA, with anomaly detection and evidence chains for audit.

Technical approach

Three architectural decisions that made explainability possible.

Hybrid model architecture
Neither pure rules nor pure ML was sufficient. Rules are explainable but rigid; ML learns patterns but is opaque. The platform combines both: deterministic scoring for the explainable baseline, ML for patterns the rules don't catch. Each component of the final score is traceable to its source.
Evidence chains as a product feature
Every risk score generates a traceable evidence record: specific inputs, model weights, and rule triggers, queryable for regulatory response without manual investigation. Designed into the data model from day one, not retrofitted.
SaaS or on-premises deployment
For clients with data sovereignty requirements, the platform deploys on-premises with no data leaving the customer environment. The deployment model was a product decision, not an afterthought.
Outcome

A production platform recognised in the market from launch.

Talisai went to market with a production SaaS platform recognised by Enterprise Security Magazine as a Top 10 Provider of Automation Security for 2020. The platform serves financial services, healthcare, and enterprise risk management clients requiring accountable, auditable AI.

Building AI for a regulated industry?

Explainability is not optional in financial services. We built it as a core architectural principle for Talisai.

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