Reduce Manual Accounting Compliance Checks by 30%

Accounting teams often start with the built-in features of their accounting software, like QuickBooks Online. QBO's rules can flag transactions over a set dollar amount, but they lack context. The system cannot distinguish a legitimate $5,000 payment to a known supplier from a suspicious $4,900 payment to a brand new vendor registered last week.

This forces teams into tedious manual reviews in Excel. Consider a 15-person accounting department auditing a month's worth of expenses. They export 10,000 transactions and a junior accountant spends a full day with VLOOKUPs and pivot tables, spot-checking for duplicate invoices and unapproved vendors. This process is brittle; a changed column name in an export breaks the entire workflow. More importantly, it is ineffective. A series of ten $950 payments to the same new vendor will fly under a $1,000 review threshold, completely missed by manual sampling.

The structural problem is that general-purpose accounting software is designed for recording, not analysis. Its data model is rigid and cannot easily incorporate external signals like vendor history or transaction timing to build a true risk profile. Off-the-shelf audit tools like AuditBoard are designed for managing the workflow of an audit, not performing deep, automated transaction analysis for smaller teams. They are too expensive and complex for a 20-person team that needs a technical solution, not more process management.

The first step is a data audit. Syntora connects to your existing general ledger via API and analyzes the last 12-24 months of transaction data. Using Python libraries like pandas, we profile your data to identify the specific patterns of normal versus high-risk activity in your business. You receive a report that outlines these patterns and provides a clear plan for what the AI system will look for.

Syntora built its own accounting system on a PostgreSQL double-entry ledger. For your compliance system, we would build a dedicated data pipeline that feeds your transactions into a similar structure. A FastAPI service acts as the processing engine. Simple, deterministic checks are handled by a Python-based rules engine. For subtler risks, an isolation forest model identifies anomalous transactions in under 200ms per transaction. This entire system runs on AWS Lambda, processing 100,000 transactions for less than $50 per month in hosting fees.

The delivered system is a simple dashboard that shows only the flagged transactions, each with a risk score and a plain-English explanation of why it was flagged, generated by the Claude API. Instead of sampling 10,000 records, your team reviews a prioritized list of 200 high-risk items. The monthly audit process shrinks from a full day to under two hours. The system sends critical alerts directly to a dedicated Slack channel, ensuring nothing gets missed.