We believe in the analysis of 100% of transactions to detect anomalies and not in the sampling of data. We also believe that sensible business rules based controls can help, to comply with existing audit recommendations. Vitally important is that these methods are combined with advanced analytics and data science.
You don’t want to choose one approach or the other, but instead a blended approach.
Use of Machine Learning
It much improves the effectiveness of anti-fraud controls by identifying outliers and anomalies by combining rule-based techniques with robust algorithms. This enhances accuracy based on what they see. Machine learning uses modeling and makes data-driven predictions about a given situation. Machine learning is one way for the system to feed what it learns back into the anomaly detection engine. The more exposure to data they have the smarter they become. This is important because it alleviates the manual rules maintenance and decision-making that has proven slow and ineffective in the previous generation of financial anomaly detection.
By doing this, the investigator have access to not only a data scientist in a box but a virtual forensic auditor that brings potential misstatements to your attention. At the same time the auditor or internal auditor can select from a number of well accepted fraud scenarios (control points). They have the financial statement reviewed based on his/her risk-assessment and comply with company policy and audit standards.