Use AI to Ensure Financial Regulation Compliance in Audits

Most accounting teams rely on their ERP, like NetSuite or QuickBooks, for compliance. These systems are good for recording transactions but are not designed for continuous monitoring. Their reports are static snapshots, not real-time feeds for an anomaly detection model. Getting a complete, granular dataset for analysis often requires stringing together multiple paginated API calls, which is slow and brittle.

Audit management tools like AuditBoard or Workiva improve workflow but do not solve the underlying data problem. They help manage documentation and controls but rely on manually exported data. This creates a high risk of versioning errors and means that by the time data is analyzed, it is already hours or days old. The core issue remains: detection happens long after the event.

Consider a 50-person SaaS company preparing for a SOC 2 audit. The controller pulls reports from Stripe, NetSuite, and their bank into Excel. An auditor requests evidence for 50 sampled revenue transactions. The team then spends two days manually screenshotting and linking records to prove each transaction's path. If a single manual journal entry from three months ago was mis-categorized, it triggers a wider, more painful audit request, costing 20-30 hours of unplanned work.

The structural problem is that off-the-shelf tools are architected for human-speed workflows, not machine-speed analysis. They lack the append-only, immutable data structures needed to create a cryptographically verifiable audit trail. True AI-driven compliance requires a system designed from the ground up to ingest, link, and analyze financial data from multiple sources in real time, something generic ERPs are not built to do.

The first step is a data systems audit. Syntora maps every source: Plaid for bank transactions, Stripe for revenue, Ramp for expenses, and your ERP for the general ledger. We work with your team to translate specific controls, like revenue recognition policies under ASC 606, into concrete, machine-testable rules. You receive a technical specification that maps each compliance control to the exact data points and logic required to monitor it.

Syntora would then build a central data pipeline using AWS Lambda functions that pulls data from these sources into a Supabase PostgreSQL database. This unified ledger serves as the immutable source of truth. For anomaly detection, an Isolation Forest model would be trained in Python using scikit-learn on your historical data to learn normal transaction patterns. The model is then deployed as a serverless FastAPI endpoint for efficient, low-cost inference.

The delivered system provides a simple dashboard and automated Slack alerts. When the model flags a high-risk transaction, an alert is sent instantly with a direct link to a report showing the complete data lineage. Your team can investigate and remediate issues in minutes. You receive the complete source code in your company's GitHub, a runbook for operations, and full ownership of the system.