How to maximize the value of Artificial Intelligence for the Finance area?

Many solutions on the market allow the Finance area to perform powerful calculations (some of which require the help of Data Scientists and highly specialized IT professionals) that apply the logic of AI or ML. But these solutions are somewhat limiting because the AI ​​produces a result, but does not offer the context to understand how this prediction came about. This approach is one of the causes that explain the hesitation of the Office of Finance with respect to the use of Ai Underwriting.

Few can’t rely on blind automation, but they must always understand the “why” behind a result. This is true even if the prediction made is calculated in an extremely accurate and sophisticated way. The Finance area, in fact, must understand that the AI ​​and ML functionalities must go beyond the forecast to explain why this forecast is likely to occur. With this knowledge, informed “what-if” analyzes can be conducted and, consequently, business decisions can be determined.

How does the Office of Finance use technology to improve financial processes?

To deliver value to the Finance area, Predictive Analytics technology needs to be explainable. Machines must support the team in understanding:

  • What : the expected outcome
  • Why : The drivers that determine the expected outcome
  • How : How to change the expected outcome

Predictive Analysis must offer a result, but also information regarding the drivers that influenced the forecast . This is what allows forecasts to become actionable decisions, made consciously. Without an explanation, the forecast is one-dimensional.

Furthermore, it is important that the CPM software made available to the Finance area are intuitive and immediately usable, without requiring the intervention of IT specialists or Data Scientists.

The secret of adopting Artificial Intelligence and Machine Learning lies in ensuring a smooth transition, with an assisted and sustainable path . You start with what you have now, then add new information incrementally, including operational data and external factors. There is no need to immediately and totally revise your way of working.