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Machine Learning and AI systems are most commonly used in the finance business function in order to forecast future expenditure for budget allocation and prediction. Fast Fourier transformations are able to generate stable approximations of even the most complex seasonality patterns while recurrent neural networks capture non-linear asymmetric cyclic patterns. However, with the advancement of sophisticated algorithms and computational power, anomaly detection systems are able to traverse internal financial databases, proactively searching for fraudulent or abnormal financial behaviour – automatically notifying the financial department when abnormalities are discovered.

Machine Learning algorithms can be useful in using patterns in data sets to highlight potential areas of discrepancy and double-check human work. Machine-learning technologies can sort through high volumes of data from financial reports at an exponentially faster pace than humans, and then turn that data over to human eyes, which can subsequently investigate the story behind the numbers and evaluate whether certain patterns or anomalies may be cause for concern.

Another process that can allow finance teams to benefit from collaboration between humans and intelligent machines is fraud reduction and cybersecurity. As finance departments are embracing digital solutions for storing and managing financial data – either on-premise or in the cloud – cybersecurity is becoming a top concern for CFOs who must ensure that access to that information is monitored and regulated. Again, machines have the ability to churn through massive sets of data regarding access to and transactions upon those data sets, identifying abnormal patterns or unique access behaviours.