Improve Audit Risk Assessment with Custom AI Models

Most audit teams rely on the features within their existing audit software like Caseware or CCH ProSystem fx. These tools are excellent for documentation and workflow management, but their analytical capabilities are often limited to basic, rule-based filters. An auditor can flag all transactions over $10,000, but the system cannot identify a pattern of fifteen separate transactions for $9,950, a classic indicator of structuring.

To compensate, auditors export data to Excel or Power BI for deeper analysis. This creates a painful, manual process for every engagement. Consider an auditor assessing revenue recognition for a client using Stripe. They spend over 10 hours exporting CSVs, building pivot tables, and manually checking for unusual discount or refund patterns. This work is non-repeatable, prone to human error, and only ever analyzes a small sample of the total transaction volume. The core audit software cannot connect to the client's Stripe account to perform this analysis continuously.

The structural problem is that audit suites are designed as systems of record, not as live data analysis platforms. Their data models are rigid and disconnected from the client's operational financial systems. Generic BI tools are powerful but are not integrated into the audit workpapers, forcing auditors to copy-paste findings and break their workflow. This gap between client data and audit analysis is where risk is missed and where auditors waste their most valuable time.

The first step is a discovery process to map the specific financial risks relevant to your client base, such as revenue recognition, inventory capitalization, or expense classification. We would audit the available data sources, whether through API access to QuickBooks, direct Plaid connections to bank accounts, or data feeds from payment processors like Stripe. This defines a set of 5-10 high-value anomaly patterns the system will be built to detect.

We would build the technical solution as a set of AWS Lambda functions written in Python. These functions securely connect to client data sources on a nightly schedule, pulling transaction data for analysis. Using libraries like pandas for data transformation and scikit-learn for modeling, we would deploy an isolation forest model to identify statistical outliers. The results, including a risk score and an explanation, are written to a Supabase PostgreSQL database. A simple FastAPI endpoint is then exposed, allowing your existing audit software to pull a list of high-risk transactions for any client.

The delivered system provides your auditors with a dashboard showing the highest-risk transactions across their client portfolio each morning. They can review the flagged items, investigate, and export the findings into a format that loads directly into their workpapers. The entire analysis for a client with 250,000 annual transactions completes in under 5 minutes. The system runs in your firm's own AWS account, with hosting costs typically under $50 per month. You receive the full source code and a detailed runbook.