Anomaly Detection in Accounting Transactions
**Problem**: The client, governed by the Financial Conduct Authority, is obligated to conduct monthly audits of its accounting and ERP systems to identify financial misconduct and unusual transactions. These exceptions must be reported at a monthly board meeting, as required by the regulatory body. Given the company processes hundreds of thousands of transactions each year, manual audits are impractical. Additionally, defining the nature of misconduct or anomalies is challenging, as false alarms could lead to distrust or complacency among users.
**Solution**: We performed a literature review and developed a machine learning algorithm for anomaly detection, inspired by an architecture previously proposed by PWC accountants. This involved creating a deep 'variational autoencoder' that utilized nominal transactions from the accounting system, which is a readily available source of high-quality data. The model was trained to identify anomalies with adjustable sensitivity. Extensive testing ensured that the default sensitivity level was set correctly. As the data was sensitive, it could not be transferred outside the company's premises. We established secure data pipelines to extract accounting data daily and optimized the model to process all data on-site. Results were communicated to managers only when exceptions occurred, and a log of activities was maintained on the server and sent to a backup location weekly for auditing purposes.
**Outcome**: The model promptly flagged a few unusual transactions that management had not previously noticed. Investigating these with staff revealed interesting stories but no malicious intent. This fostered trust among management and impressed staff, showcasing the model's capability to uncover hidden issues within the business. Management now has documented activities to assure the FCA that they are taking fraud and compliance seriously.