Finance and Accounting

How AI transforms scenario analysis in corporate finance

Scenario analysis is a vital tool for finance leaders, helping them anticipate and prepare for possible future outcomes for their enterprises. Artificial Intelligence (AI) for financial analysis is revolutionizing this process through advanced Machine Learning (ML) algorithms and automated data processing. AI-powered scenario analysis tools can process vast amounts of multimodal data to identify patterns human analysts might miss and generate more sophisticated scenarios in a fraction of the time.

The landscape for business across a wide swathe of sectors is being disrupted by several forces–changes in the regulatory landscape, the promise of AI-driven technology disruptions, and political headwinds in various geographies buffeting the marketplace, to name a few.

What remains unchanged is the driving imperative of enterprises the world over – the need to build and sell goods and services responsibly and profitably. Profitably is a key word here, with the finance team shouldering the increasing load of that term like modern-day Atlases.

For finance leaders–whether Chief Financial Officers (CFOs), Controllers, Heads of Finance as well as their risk management counterparts such as Chief Risk Officers (CROs)–scenario analysis is a critical tool to shoulder this load: to anticipate and prepare for various future outcomes. As the name implies, scenario analysis involves evaluating multiple potential scenarios that could impact a business's financial performance and developing relevant and feasible strategies to address them.

A traditionally tedious exercise: For finance leaders, scenario analysis is more than just a planning exercise—it's a survival tool. Consider a manufacturing CFO who must evaluate the impact of raw material price fluctuations on profit margins. Traditional scenario analysis can help them determine optimal inventory levels and pricing strategies across different cost scenarios. The scenarios can gamify supply chain disruptions, geopolitical impacts on raw material pricing, as well as the impact of regulatory announcements. Similarly, a retail finance leader uses scenario planning to assess the financial implications of expanding into new markets, considering variables like real estate costs, local competition, and consumer spending patterns.

Currently, many finance teams rely on combinations of complex spreadsheet models and manual data analysis to conduct scenario planning. Enterprise leaders may have incorporated analysis and modeling software into their review workflow as well. Typically, all these traditional approaches involve gathering historical data, creating financial models, and manually adjusting variables to generate different scenarios. Whatever the tools used, finance analysts spend innumerable hours updating spreadsheets, cross-referencing data sources, and reformulating projections when market conditions change.

Enter AI: Artificial Intelligence(AI) in finance is revolutionizing the field through advanced Machine Learning (ML) algorithms and automated data processing. AI-powered scenario analysis tools can now simultaneously process vast amounts of structured and unstructured data, identify patterns human analysts might miss, and generate more sophisticated scenarios in a fraction of the time.

The cherry on the cake? Such AI strategy helps CxOs not only acquire powerful financial insights. Deploying AI for decision making also facilitates better risk measurement and stress testing of the organization’s capabilities and financial workflows.

Business leaders are actively evaluating AI-powered scenario planning and moving into POCs and beyond for this capability:

• According to the 2024 EY/IIF global survey of bank Chief Risk Officers, enhancing risk measurement, stress testing, and scenario analysis capabilities is the top priority for 52% of respondents when it comes to improving their financial risk management. Additionally, 58% of CROs identified scenario analysis and stress testing as crucial tools for managing climate-related risks.
• The market size of AI in fintech (including generative AI tools) is estimated to grow to US$ 50 billion by 2029.

Not without challenges: However, CFOs face challenges when integrating AI into their scenario analysis workflows. The quality and consistency of the input data remain significant hurdles—AI systems require clean, standardized data to function effectively. Many organizations struggle with fragmented data sources and inconsistent formatting. Security concerns also loom large, particularly when handling sensitive financial data. Additionally, there's the challenge of change management: finance teams need proper training to effectively use AI tools, and some staff members may resist adopting new technologies. CFOs must also ensure that AI-generated scenarios align with human judgment and business context, as purely algorithm-based decisions may miss crucial qualitative factors.

AI in accounting and finance is rapidly reshaping scenario analysis for business leaders, offering unprecedented speed and insight generation capabilities. While challenges exist, the potential benefits in terms of time savings, accuracy, and strategic decision-making make AI integration a worthwhile investment for forward-thinking finance organizations. The key lies in thoughtful implementation that balances technological capabilities with human expertise.

Infosys BPM offers leading-edge services in finance and accounting to support the F&A function end-to-end. With 14,000 F&A professionals spread globally, serving over 100 clients across several industry verticals from 23 delivery centers across the globe, Infosys BPM can offer expert services in cutting edge scenario analysis for your business.