There has been a lot of conversation lately about how machine learning (ML) is paving the way for artificial intelligence (AI) methodology in financial compliance, addressing the key differences between AI and ML, and outlining why today’s financial compliance applications require a hybrid approach that combines ML and human decision-making attributes.
For all the talk around AI and ML in financial compliance, the conversation really is a non-starter if we don’t first address data quality. Data quality is the foundation on which all automated, intelligent applications are built. Without quality data, there’s no basis for intelligent applications in financial compliance to even begin to understand what constitutes good (compliant) or bad (non-compliant) behavior.
