For organizations embracing finance automation, the guiding principle is simple: “Start
Small, Scale Fast.” This approach ensures incremental, manageable changes, allowing
finance teams to adapt effectively and achieve quick wins.When assessing the scope of automation and available solutions, CFOs are focusing on several key factors:
Gen AI in financial controllership – seizing opportunities, navigating risks
- 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.
- 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.
- 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.
- 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.
- Data privacy and security are major concerns for finance teams, as AI models require a substantial amount of data input.
- Finance decisions require transparency and accountability, so AI models must be trained to justify their predictions for stakeholders to trust them.
- Training AI models is challenging, as the data used for training must be unbiased and fair in order to produce accurate results.
Kaushik Venkatraman is Co-founder and Head - Products at Consark.
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