Intelligent Finance: Empowering Finance Leadership with Predictive Insights and Automation

Where should the finance leadership in an organisation be spending more time? Ideally, higher up the value chain in the decision-making process. This did come across as a link node at the just concluded round table with finance leaders in Bengaluru. From cautious explorers to believers in a tech-led transformation and to even those keen to learn from experiences of others, there was little doubt that there were organisational and societal gains to be accrued when CFOs opted to focus on decision-making armed with predictive insights in an era of automation and artificial intelligence (AI). The round table did deepen the perception that AI was ‘here to stay’ as against ‘the scratching the surface’ perception about the technology tools just a couple of years ago. 

From the perspective of the CFOs, there were enough and more reasons to leverage AI - be it in the ability gained with right pricing and get to win a contract in a bidding transaction or be in a better position to predict cash flows or gain a better grasp on the asset-liability mis-match or even in decisions based on insights gathered from various financial stress testing scenarios. 

The focus therefore was on how and where to leverage it all. Any AI model will succeed only when there was enough data (and good quality one at that) to train a model. While AI could help and even if the issues around data quality and integrity were sorted, it was still crucial that the report that gets generated thereafter out to have correct data flow, which to some was still a challenge. Those in the business of supply chain sought it was really about gaining greater insights with data analytics and with connected network. There was also the aspect of scale and size of business operations. Typically, sub-scale operations had added challenges of missing out on the institutional knowledge that bigger entities could fall back on. 

While many could clearly saw the regulatory environment encouraging automation in financial reporting, some found more room for improvement in the R2R (industry jargon for Record-to-Report), an important finance and accounting process that took raw financial data from various business transactions and transferred them all into accurate and actionable financial reports. It was felt that in the adoption of AI and staying aligned to the path to intelligent finance also came with its own challenges. It being still more of a people-driven exercise than technology-driven. Most seemed upbeat about the need for digital transformation where people, processes and technology came together. While the way forward offered solutions in the form of AI-powered assistants for say payments or agents in other areas related to finance, there was also need for specific aspects and more urgently pressing one to some like the ability to know ‘unbilled revenue’ on a daily basis.    

Harnessing AI for Strategic Finance Leadership

Chief Financial Officers are no longer shying away from adopting AI processes in their decision-making. That’s a key shift in stance noted at a recent roundtable at the Finance Leadership Dialogue, organised by Financialexpress.com in association with Oracle, in Bengaluru. The CFO’s role is shifting from simple oversight and number crunching to strategic finance leadership. CFOs, today, dedicate nearly 80% of their time to strategic decision-making.

The role of AI is moving from a niche technology CFOs were experimenting with to a fundamental “way of life” within the finance function. At the roundtable, CFOs were clear about AI's role and the challenges it poses. While most have adopted AI-driven processes, such as robotic process automation and agentic AI, challenges stem from interoperability with legacy systems and the quality of the available data. A certain level of customisation is needed to get these systems to communicate effectively.

However, participants at the roundtable were unanimous about how AI now helps with data sampling, enabling 100% sampling for more accurate results, which helps build trust and give finance professionals real-time insights. A key area of impact is in forecasting and scenario planning. Traditional forecasting is being redefined, moving beyond historical analysis. AI can now power various scenario playout in critical areas like Mergers and Acquisitions (M&A) and Capital Expenditure (CapEx) analysis, helping to forecast cash flows and pinpoint the expected Return on Investment (ROI).

As one participant summed up, AI gathers data from hindsight to give insight and enable foresight. This helps finance professionals with better pricing decisions or resource allocation. Early proofs of concept (POCs) are crucial to validate AI's effectiveness on historical data before scaling up. However, what many participants noted was that to implement AI – it’s not just a technological challenge – it’s a mindset change that’s needed.

While AI excels at pointing out "red flags" and providing structured data, the human interface and intelligence remain vital. Finance leaders need to trust the data provided by AI and use it to make informed decisions. The Return on Investment on AI in finance is

multifaceted. While financial gains are evident by using AI processes, equally important are time savings and adoptability. For customer-facing sectors like banking, customer satisfaction is a critical ROI measure.

Empowering Finance leadership with predictive insights and automation

Then, where should the finance leadership in an organisation be spending more time? Ideally, higher up the value chain in the decision-making process. This did come across as a link node at the roundtable in Bengaluru. From cautious explorers to believers in a tech-led transformation and to even those keen to learn from the experiences of others, there was little doubt that there were organisational and societal gains to be accrued when CFOs opted to focus on decision-making armed with predictive insights in an era of automation and artificial intelligence (AI). The round table did deepen the perception that AI was ‘here to stay’ as against ‘the scratching the surface’ view about these technology tools just a couple of years ago. 

From the perspective of the CFOs, there were enough and more reasons to leverage AI - Be it in the ability gained with right pricing and get to win a contract in a bidding transaction or be in a better position to predict cash flows or gain a better grasp on the asset-liability mis-match or even in decisions based on insights gathered from various financial stress testing scenarios. 

The focus, therefore, was on how and where to leverage it all. Any AI model will succeed only when there was enough data (and good quality one at that) to train a model. While AI could help, and even if the issues around data quality and integrity were sorted, it was still crucial that the report that gets generated thereafter has correct data flow, which, to some, was still a challenge. Those in the business of supply chain sought that it was really about gaining greater insights with data analytics and with a connected network. There was also the aspect of scale and size of business operations. Typically, sub-scale operations had added challenges of missing out on the institutional knowledge that bigger entities could fall back on. 

While many could see the regulatory environment encouraging automation in financial reporting, some did found more room for improvement in the R2R (industry jargon for Record-to-Report), an important finance and accounting process that took raw financial data from various business transactions and transferred them all into accurate and actionable financial reports.

It was felt that in the adoption of AI and staying aligned to the path to intelligent finance also came with its own challenges. It is still more of a people-driven exercise than a technology-driven one. Most seemed upbeat about the need for digital transformation, where people, processes and technology came together. While the way forward offered solutions in the form of AI-powered assistants for say payments or agents in other areas related to finance, there was also a need felt for specific aspects and more urgently pressing ones, such as the ability to know ‘unbilled revenue’ on a daily basis.