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How to make Artificial Intelligence work for the Office of Finance

Sep. 25 2020 by Rafael Nay , Customer Success Manager - CCH Tagetik DACH

Performance Management Business Intelligence & Analytics

Machine learning and artificial intelligence are two of the most important trends in finance in terms of data quality and data collectionBased on practical use cases, a new whitepaper shows how modern finance departments can get optimal value of the new technologies. 

Access to data and knowledge is especially important for the Office of Finance. But making the right decisions requires the appropriate data platformA rapid technological change is taking placeand finance departments can benefit significantly if they adapt their approach to new technologies such as artificial intelligence (AI) and machine learning. To achieve optimum added value, however, it is also important to avoid the pitfalls associated with the use of new tech. 

In the new white paper "Machine Learning for Controllers. Use cases for Forecasting, Planning, and Simulation", experts Prof. Dr. Karsten Oehler (CCH Tagetik DACH) and Marco Van der Kooij (ForSight Consulting) deal with realistic, practice-oriented scenarios for financial controllers. The focus is on aspects such as forecasting, planning and simulation. 

Artificial intelligence and machine learning can play out their strengths in the Office of Finance in areas such as the following: 

-Data quality improvement and data collection 

By using machine learning, it is possible to significantly increase the quality of data collection. This is achieved, for example, by minimizing typical sources of error, such as duplicates and data which is not properly assigned to periods or other dimensions etc. Even seemingly consistent data from the accounting system often show these deficiencies, so that meaningful analysis is not possible without prior preparation.   

-Forecasting profit and financial positions 

An integrated and automated forecast can achieve several advantages: a better basis for planning is created and the cost of manual forecasting can be reduced. Besides that, a better understanding of success drivers can be used for simulation and activity planning – which also enables more proactive cost and revenue management.

-Analysis of planning data and other decentralized information 

Manual or oversimplified validation of bottom-up collected data often misses outliers or inconsistencies. Are distribution costs too high (or low) in relation to planned sales compared to the previous period or peers? Machine learning can be used to check contributed data using sophisticated algorithms based on a wealth of information. In the long run, this will lead to a better mutual understanding between contributor and manager about targets, expectations and limitations.

-Driver based simulation 

Machine learning and AI can provide the technological basis for creating much more accurate simulations and models. What effects do price and discount changes have on profit and liquidity? This requires an examination of the effect of the described drivers on sales. By integrating a wide range of drivers, better informed decisions can be made, and qualified actions planned. 

Using concrete cases, the new whitepaper shows finance experts how a holistic approach for data-driven decision making can be implemented. It also addresses typical problems and pain points.

To discover more download the white paper "Machine Learning for Controllers. Use cases for Forecasting, Planning, and Simulation" here.



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