Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality – McKinsey
Digital transformation is not a technology problem, but a business imperative, and insurers need to begin to look at their data as a business asset and not simply the domain of the IT department
To remain competitive, traditional banks must be able to make intelligent decisions on how best to serve their customers – and the crux of intelligent decision-making is quality data.
For someone that’s been preaching data governance and data quality for more than fifteen years, its been fascinating to see how these two topics have been gaining traction in the last few years. A few week’s back I touched on the difference between data governance and data quality – governance is about “what” and “who”,…
But to business, having customer data consolidated into a master data management application often leaves a bad taste.
What makes the difference, for any AI project, is the quality and quantity of data that is available to feed the model.
Concerns about the quality of data are inhibiting the adoption of AI in South Africa and world wide – access the report