Gen AI in financial controllership – seizing opportunities, navigating risks

Gen AI is an exciting opportunity for companies to explore especially in the world of finance and accounts. From automating routine tasks to providing deep analytical capabilities, Gen AI is poised to revolutionize how organizations manage their financial operations.
There are plenty of use cases to adopt these models some of which I have attempted to list below:
  1. The finance close workstream – Gen AI models can be used to generate task lists for finance close processes. This can be highly beneficial for small to mid-market companies seeking to streamline their finance close function. Gen AI can generate task lists which are specific to industries and sectors, thereby enhancing the relevance of check points which is what any finance controller would like to have in place.

  2. Flux management – Mid to large companies place huge emphasis on flux management as it involves extensive analysis of unit metrics to assess business performance. Gen AI can assist with initial data analysis, suggesting potential reasons for variance that users can review and contribute to. Over time, the accuracy of these explanations will improve, providing real-time insights to stakeholders (CXOs) for making rapid and effective business decisions.

  3. Transaction anomalies – Often, controllers and auditors need to closely monitor accounting transactions for any anomalies. These anomalies can include one-time journal entries or unusual transactions with related parties. The parameters for detecting these anomalies are quite specific. AI models equipped with these parameters can analyze all accounting records such as general ledgers, sub-ledgers, and control accounts to identify potential anomalies. The AI can also provide possible explanations or exceptions for these transactions. Controllers can then review this analysis, allowing them to focus on managing exceptions more thoroughly, which can otherwise be quite time-consuming.

  4. Financial reporting – The CFOs and controllers dedicate a significant amount of time to preparing financial reports, with a large portion of that time spent on gathering and organizing data. AI models, after being trained, can handle the laborious tasks involved in data collection and organization, while also generating insights, enabling controllers to concentrate on enhancing the quality of information and analysis.

The use cases mentioned above serve as examples based on our conversations with customers. While these use cases are exciting, there are also significant risks that need to be considered before adopting them.

  1. Data privacy and security are major concerns for finance teams, as AI models require a substantial amount of data input.
  2. Finance decisions require transparency and accountability, so AI models must be trained to justify their predictions for stakeholders to trust them.
  3. Training AI models is challenging, as the data used for training must be unbiased and fair in order to produce accurate results.

These are the top three concerns that customers face when considering the adoption of Gen AI models in their day-to-day finance operations. Therefore, a well-informed approach is necessary to balance the promising possibilities with the inherent risks of evaluating Gen AI models.

Kaushik Venkatraman is Co-founder and Head - Products at Consark.

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