Finance Decision Intelligence

Leverage cognitive capabilities that enhance predictive and prescriptive analytics to empower Finance to make augmented and automated decisions.

Decision-making in Finance

How a CFO can benefit from cognitive decision intelligence:

Decision-making – a CFO pulse check

Many Finance functions still rely on gut feeling and focus more on the past than anticipating the future

Common Finance decision-making challenges and how to tackle them

Shifting the focus beyond Finance and adopting a forward-looking approach are key success factors

Finance Decision Intelligence - how to bring it to life?

Finance Decision Intelligence - how to bring it to life

Here is the story of a large retail clothing company (K&M)

In late winter 202X K&M wanted to identify an optimal pricing for the upcoming summer season


  • Optimise the revenue and subsequently the gross margin of the summer collection


  • Predict the sales of clothing for the summer collection
  • Optimise the promotional price that enables the highest gross margin across the summer collection

Key variable:

  • Weather (long-term forecast from external provider)


Approach using Finance decision support technologies

a) Predicting and Scenario Modelling:

We predicted revenue and gross margin, modelling six different weather scenarios, using three types of data:

  • External data: weather forecast
  • Internal data: financial
  • Internal data: non-financial

b) Machine Learning & Mathematical Optimisation:

  • Step 1: we considered constraints such as inventory levels, production capacity, staff availability, and we calculated optimal revenue and gross margin for the scenarios modelled in a)
  • Step 2: we used Machine Learning as K&M progressed through the spring months to learn how the external weather data and internal financial and non-financial data have influenced the optimal revenue and gross margin
  • Step 3: we again considered constraints, this time to calculate the optimal promotion price for clothing for the rest of the season, ensuring the optimal revenue and gross margin will be reached

c) Prescriptive Analytics:

We made use of prescriptive technology to guide K&M towards the best course of action under the scenarios calculated in a) and b). Therefore, Finance helped the organisation plan resources with more precision, and with higher confidence in reaching its aim: “Optimising the revenue and subsequently the gross margin”


Deep dive into the key technologies for Finance decision support

The approach taken by the retailer in the example above is detailed on the next slide, with a visualisation of the three key technologies used:

  • Predicting and Scenario Modelling,
  • Machine Learning & Mathematical Optimisation
  • Prescriptive Analytics

Finance Decision Intelligence – key ingredients for success

A deep-dive on essential capabilities

Your Finance Decision Intelligence journey

Some practical tips for how to get started

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