AI Governance: Risk Managers Should Prioritize Data Integrity

A recent Risk Management Magazine article says that the single-minded emphasis many business have on selecting the best AI model is flawed, and that risk managers should prioritize data integrity over nifty algorithms. Here’s an excerpt:

AI models depend on data to deliver trusted outputs, which is why data accuracy and cleanliness are essential. Yet, as insurers and other businesses strive to adopt AI and remain competitive, they often focus on an algorithm-first approach. However, without equal attention to data, proving return on investment can become a real challenge.

When AI misfires, the problem can often be traced back to where organizational data is stored and how models can access and train on it. Inside many companies, data is scattered across different systems, limiting the context that AI models need to do their job properly. The problem is compounded for companies saddled with core legacy mainframe systems built on COBOL or other outdated programming languages. Trying to integrate AI into these rigid solutions is extremely difficult.

The article says that unclean and fragmented data can result in unexpected consequences even for businesses using modern cloud-based systems, and offers suggestions on how risk managers can help their organizations assess and reduce data-related risks.