The Finance function is much more than reporting of performance. Improving processes and accelerating growth as business partner as well as managing enterprise risk and creating sustainable value for all stakeholders are amongst the key priorities of a CFO and its organization.

Digital transformation has disrupted many industries and impacted operations across the entire value chain: Personalized products, shorter go-to-market cycles, outside industry competitors as well new revenue models and service offerings drive process complexity and require Finance organizations more than ever to act as a business enabler.

AI has the power to augment the playbook of Finance experts with application fields from freeing up working capital and maximizing productivity to digitizing operations as well as managing risk and compliance. Our platform of pre-configured Machine Learning systems will get you the insights needed to drive better outcomes for your organization.

Pre-configured Analytics & Machine Learning systems​

Complexity of adressing business-specifics is often underestimated while technological requirements are overestimated
Lack of understanding in business units on how to utilize Advanced Analytics and Machine Learning
Access to experts with the right set of competencies across different domains
Availability of data at sufficient quality and quantity

Performance Drivers

Working CapitalFree up capital through rapid increase of capital efficiency
Companies can leverage AI to drive capital efficiency through digesting massive amounts of aggregated data, e.g. to analyse customer behaviours, identify critical stock patterns or problematic suppliers. Continuous updates through automated data pipeline ensure sustainability of measures and consideration of current economic conditions and trends set a dynamic and responsive environment for decision-making.
Demand forecastingCost reduction from optimized production planning and minimized stock levels
Manufacturers can adjust production volume for each product to the actual demand by forecasting it and then adjusting the production planning accordingly. Also, stock levels for supply and products can be reduced in order to reduce storage costs.
Quality assuranceCost reduction from reduced manufacturing errors
Manufacturers can identify reasons for production failures by analyzing sensor data from the involved equipment. They can identify which states of the machines were responsible for the production failures in order to take actions and avoid the manufacturing errors going forward.
Process ExcellenceCost reduction through faster and more reliable business processes
Companies can identify potential issues in their business processes through process mining. Connection of transactional data is fast and easy, pre-built models identify process pain points, gives recommendations on what to improve and continuously monitors the success of your process improvements.
Risk ManagementEfficiency increase through more precise risk assessment and data-driven mitigation
Companies can predict the Probability of Default (PD) and Exposure at Default (EAD) more precisely by using Machine Learning models compared to traditional score cards. Directly tied to transactional systems, algorithms can also detect anomalies and reduce manual investigations across data-heavy verification processes.

Next Steps

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