Using AI to Detect Errors & Falsification 

The ability to detect data errors and falsification is an essential part of every regulated industry, from finance to healthcare. In addition to the very real safety and financial impacts, these issues destroy trust between key shareholders and can lead to the collapse of the business itself.  

Audit trails have long been a key resource to finding and mitigating these issues, but adding the processing power of new Artificial Intelligence (AI) tools enables organizations to find errors faster (sometimes even in real time) and address them sooner. Here’s how: 

Spotting illogical patterns: AI models can automatically scan audit logs and related data to identify red flags, such as: 

• Unusual access sequences 

• Large amount of data downloads 

• Repeated overwriting in the same field 

• Duplicated data entries 

Catching nonsensical data: AI can recognize unusual, inconsistent, or illogical data that technically fits the format but raises a warning. Examples include: 

• Entries with conflicting values 

• Gaps/ spikes in normally steady data 

• Outliers and duplicated record seconds apart 

Learning from human review: AI systems can improve over time. The feedback from the auditors can be fed back into the model, resulting in: 

• More accuracy with less variability 

• Faster identification for future cases 

The combination of AI and audit trails provides a powerful defense against both accidental data entry issues and intentional falsification. Instead of manually reacting to discovered errors, organizations can now detect issues as they happen, helping ensure integrity, compliance, and trust in every record.