Case Many organizations work to ensure the quality of advice their advisors give. Especially in the financial industry the consequences of non-compliant advice can be significant. Traditionally quality managers would examine a random selection of all given advices to determine compliancy of those advices. In many cases AI models are able to predict the probablity of an advice being compliant. This opens up opportunities to make advice quality management more effective or more efficient.
Solution Advice is typically documented in the form of text. The text either contains a compliant advice or a non-compliant advice. Quality management classifies a subset of these advices using those labels. This data can be used to create and train a text analytics AI model. With this model we can now predict whether an advice is probably compliant or probably non-compliant.
Impact There are several ways this AI model can be applied to improve the business process. A) Instead of randomly selecting advices for quality management we can now have the AI model make a probalistic selection containing higher amounts of non-compliant advices. B) All of the advices can easily be checked instead of a subset. C) If the AI model reaches a high accuracy non-complaint advices can be filtered out for re-work by senior staff. D) If the AI model reaches even higher accuracy realtime direct feedback can be given to advisors