Risk and Compliance

Financial systems generate thousands of transactions per second. Each one potentially triggers compliance checks, fraud detection, risk calculations. But the systems that evaluate them can't keep up in real time.

Batch delays. Transactions happen now; compliance runs tonight. By the time suspicious patterns surface, the money has moved, the damage is done. Real-time detection means simple rules. Sophisticated analysis means waiting.

Pipeline complexity. Event streams, message queues, custom processors, dead letter handling. The infrastructure to move transactions through compliance checks becomes its own system to maintain, debug, and explain to auditors.

Hard-coded rules. Fraud signatures, AML patterns, risk thresholds buried in microservice code. When regulations change, you're not updating a rule. You're scheduling a release, coordinating deployments, hoping nothing breaks.

The speed/sophistication trade-off. Real-time systems use simple pattern matching. Sophisticated analysis runs in batch. You're forced to choose, and either way you're exposed.

Inferal evaluates transactions as they happen against your full rule set. Not simplified patterns. Not sampled subsets. The complete logic, running continuously.

• Transaction sequence matches known fraud signature → freeze account and alert investigations
• Customer activity crosses risk threshold → trigger enhanced due diligence workflow
• Trade would violate position limit → block execution and notify compliance
• Payment routing matches sanctions pattern → escalate before settlement

Rules evaluate against live transaction streams. When patterns match or limits are crossed, relevant systems activate immediately with full context. No overnight batch. No simplified approximation.

Regulators don't want to see your code. They want to see your logic. And they want to know it was applied.

Logic you can show. Rules are readable declarations, not behavior buried in microservices. When examiners ask "how do you detect X," you show them the rule, not a code review.

Decisions, not reconstructions. Every evaluation produces a record: which rules applied, what data they saw, what action resulted. Audit trails by design, not forensic reconstruction.

Change without release cycles. Regulations evolve. Sanctions lists update. Risk appetites shift. Update the rule, not the deployment pipeline. The new logic applies immediately.

Consistency across products. Same rules evaluate transactions whether they come from mobile, web, API, or batch file. The logic is centralized. The application is uniform.

Agent action trails. When agents make decisions on your behalf – executing trades, approving transactions, escalating cases – every action is traced to the rule that authorized it. AI governance stops being theoretical.

Static rules catch known patterns. But fraud evolves. Risk profiles shift. And when agents execute trades or approve transactions, you need rules that evaluate their decisions in real-time – not after the fact.

What if your compliance system could adapt?

Pattern refinement. Investigators flag false positives. The feedback tightens rules, reducing noise without missing signals.

Emerging signatures. New fraud patterns detected in one product line automatically inform monitoring across others.

Risk recalibration. Customer behavior over time adjusts their risk profile. Thresholds become personalized, not one-size-fits-all.

Compliance isn't a checkpoint. It's continuous understanding.

The trade-off was never inherent. It was architectural. Batch processing was a constraint, not a feature. Complex pipelines were a workaround, not a solution.

When rules evaluate continuously against live data, you don't choose between catching fraud fast and catching fraud well. You do both.