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In an earlier post in this Digitizing Finance series, we considered how CFOs can best harness the power of analytics. In this post, we explore five artificial intelligence (AI) use cases that can help create capacity while improving the quality of finance services. 

AI is everywhere and here to stay, from self-driving cars to facial recognition systems and digital assistants. Yet, its applicability and benefits to finance are not always perceptible. 

Our CFO Reimagined research found that more than half (53%) of CFOs worry that the finance function is reactive or that data and information-sharing processes are not streamlined.  

Finance teams demand automation and tools to reduce mundane transactional activities, allowing them to redirect their focus on analyzing data, providing actionable insight and truly advising the business. 

To facilitate this transition, finance organizations have adopted robotic process automation (RPA) to harvest some benefits. However, when used in isolation, the rule-based RPA capability leaves a gap when it comes to finance processes which tend to be more judgmental.  

AI can be used to close this gap and is the foundation for continuous accounting and touchless processes. It promises to boost both productivity and quality of outputs while permitting greater transparency and auditability.  

Accenture defines AI as a suite of tools and technologies that can sense, comprehend, act and learn. 

• Sense: Perceive the world by acquiring and processing images, sounds and speech. 
• Comprehend: Analyze and understand the information collected by adding meaning and insights. 
• Act: Take action in the physical world based on comprehension and understanding. 
• Learn: Improve performance (quality, consistency and accuracy) based on real world. 

Finance organizations exploring AI tools can typically invest in the five areas described below. Based on our experience, these tools can automate up to 80% of manual activities, reducing processing time and costs while improving the accuracy of data and the quality of insights. 

1. Invoice and Journal Processing 

At the forefront of many finance processes are tools that read incoming invoices or journal instructions in various formats, validate the content based on complex self-learned rules, predict the department and account to which a journal needs to be posted. Finally, these generate and upload the journal entries in the ledger without human intervention. Our work in this area indicates a reduction in manual errors and processing time by up to 80%.  

2. Reconciliation and Matching 

Addressing the paradox of the convoluted application landscape and the importance of clean data, these tools analyze input from various sources and formats to perform complex reconciliations such as intercompany, derivatives, cash, bank and other general ledger accounts. They use structured and unstructured data and learn matching rules to continuously improve the auto-matching rate. They also suggest journal entries and actions for unmatched items which can be reviewed and acted upon. We have observed a 25% improvement in productivity and an improvement in data quality of up to 15% through use of these tools.  

3. Predicting and Prescribing 

Looking backward is not enough to increase value to the organization. Predictive and prescriptive analytics tools powered by AI and machine learning allow finance to exploit data and provide actionable insights to the business. We have helped clients to automate the forecasting of cost, revenue or cash flows and have implemented mechanisms to proactively identify mitigating actions. These analytics tools free up capacity, and the quality of the insights can have material impact on business metrics such as working capital. 

4. Narrative Writing  

AI tools absorb data available and use natural language generation (NLG) to turn the data into meaningful insights, without the need for human intervention. They generate commentaries explaining profit and loss (P&L) trends and variance against budget or non-compliance items.  Narrative writing, which is three to five times faster than a human, allows finance to deliver reports at a higher frequency and customized for different audience levels.  

5. Finance Chatbots 

Chatbots aim to enhance user interaction with a cognitive conversational self-service experience to answer questions, provide guidance and orchestrate processes while assisting humans in performing their activities. Examples in finance include support in opening general ledger (GL) accounts, questions on cost policies, queries on invoices, payment statuses, or to interrogate large data sets.  

AI technologies need data to analyze, learn from and process and people to work with.  Effective AI implementations focus on bringing the appropriate data and transitioning the workforce.  

When adopting AI, we advise an agile, value-centric approach, experimenting with proof of values (PoV) before scaling up the implementation. The PoV allows financial firms to see the tool in practice in a process subset in a span of few weeks instead of months.  

If you have questions or want to learn more about AI in finance, please contact me.