Artificial Intelligence Banking Guest Posts

How Can Finance Companies Improve their Operations using AI ML?

How Can Finance Companies Improve their Operations using AI ML?
Artificial Intelligence (AI) for FP&A – What’s Holding Finance Back?

As the promise of Artificial Intelligence (AI) within corporate performance management (CPM) moves from fiction to fact, many FP&A teams are asking the same basic question: What does AI and Machine Learning (ML) mean for me? 

Global Fintech Interview with Alwin Schauer, Chief Revenue Officer at Basware

Before jumping into any new journey, it’s critical to chart a course to anticipate what’s in store on the road ahead. For a topic as exciting and overhyped as AI, any new journey must begin by considering the key factors that have traditionally held Finance back from AI adoption.

Market Appetite for AI and ML

According to the 2021 Dresner Advisory Wisdom of Crowds® Data Science and Machine Learning Market Study, 60% of organizations are using or actively exploring ML. On the surface, the progression over the last several years underscores the AI hype and excitement for the potential of AI for FP&A, but when looking deeper at the study, only 20% of Finance organizations are currently using AI and ML, and Finance actually lags in most functions, despite the hype around it.

So, What’s Holding FP&A Back?

With so much buzz, yet such little adoption, let’s examine the key barriers holding FP&A and Operations teams back from mainstream adoption of AI and ML solutions.

Lack of Expertise in AI ML

If FP&A teams believe that they are required to learn AI and ML modeling techniques on their own, chances are FP&A teams will have trouble deploying AI across enterprise planning processes. Enterprise planning processes, such a rolling forecasting or demand planning, could consist of hundreds or thousands of unique forecasts. Furthermore, for organizations with existing AI investments, FP&A teams generally lack the dedicated business analysts and data science engineers required to build ML models.  Without dedicated expertise or resources, FP&A’s ability to take advantage of AI and ML is severely limited.  

Lack of Scale 

For organizations with existing data science teams, creating repeatable and systematized ML processes and infrastructure across the enterprise can be expensive. Most ML capabilities within today’s CPM tools are only capable of producing a single forecast (target) at a time. 

Why does that matter? It is FP&A’s role to lead and drive effective planning processes across lines of business and functions such as HR, Sales and Operations. If FP&A cannot enable the organization to forecast at scale, FP&A is not executing as a strategic business partner. AI solutions for FP&A that cannot scale will remain a barrier to mass adoption. 

Lack of Business Intuition & Transparency 

Traditional ML modeling processes within CPM tools are often a “black box” to FP&A teams. If ML modes are completed without material business “intuition” and offer users only limited visibility or traceability into model inputs and results, the limitations can result in a lack of trust or confidence in those results. As a strategic business partner, FP&A must instill confidence in forecasting processes. While leveraging AI and ML is likely to increase forecast accuracy, if P&L owners cannot assess the drivers that comprise their forecasts, P&L leaders will never own their forecasts. Additionally, if P&L owners do not own their forecasts, forecasting processes break down and fail altogether…which means FP&A has failed too.

Fragmented & Disconnected Processes 

Current AI and ML solutions within CPM typically require data movement and reconciliation between fragmented systems and Finance processes, increasing the technical debt and limiting the ability for advanced analytics to scale across the organization.    

The fragmented software and technical processes needed for connected planning create added complexity and administrative burden for FP&A teams, including constantly moving and reconciling data, monitoring data latency and managing security between fragmented products or models. Collectively, this dilutes the ability of strategic FP&A teams to focus on driving performance and supporting critical decision-making.  

Recommended Fintech News: Taps Meron Colbeci as New Chief Product Officer

As AI and ML for FP&A enter the mainstream, organizations will undoubtedly have several choices to consider. On one spectrum, solution vendors for AI are offering everything from AI infrastructure solutions, to data science toolkits and complete AI platforms to create and deploy ML models. While these are powerful tools addressing varying use cases, these tools are not designed for FP&A teams.  CPM vendors are also investing in AI capabilities to support extended planning & analysis (XP&A) processes, such as demand planning and sales planning. Some are offering integrations to third-party AI and ML tools, while others are building AI and ML-based forecasting right into their platforms with automation capabilities that can make advanced forecasting more accessible to FP&A teams.

What’s the lesson in all this? Don’t let AI hype cloud the evaluation process. Start with a clear understanding of “what” business outcomes your FP&A team is trying to achieve, identify “who” is using the solution and then “how” the solution is unified into existing planning processes.

With answers to these questions in mind, use the evaluation process to “get under the hood” and learn whether the solution will in fact alleviate the organization of the key barriers that are holding FP&A back from moving beyond the hype.    

[To share your insights with us, please write to]

Related posts

New Banking Study Reveals Growth Benefits of Personalization, Innovation and Trust in Customer Experience

Fintech News Desk

Survey Says 95% of Bank Employees Prefer Managing Their Schedules with Mobile App

Fintech News Desk

Bank Muscat and Newgen Software Win Award for the Best Lending Implementation in the Middle East

Fintech News Desk