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When finance gets a brain upgrade with AI

Artificial intelligence is reshaping the financial world, with 71% of companies now integrating it into processes that promise smarter, faster decisions.

  • Marina Mouka
  • December 20, 2024
  • 4 minutes

Artificial intelligence (AI) is no longer a peripheral tool for finance teams; it is now a central force reshaping how organisations operate.

According to KPMG’s latest Global AI in Finance Report, 71% of companies are already using AI in their financial processes. What’s striking is the gap between those leading the charge and others still finding their feet.

The report categorises organisations into three maturity levels: Beginners, Implementers, and Leaders. Of these, Leaders are driving innovation and seeing their AI investments deliver exceptional returns.

The Leaders—24% of surveyed organisations—are reaping rewards by embedding AI into diverse use cases. These range from adaptive pricing and predictive analytics to real-time fraud detection. More than half of these Leaders report that their AI initiatives exceed ROI expectations.

By comparison, only a quarter of Beginners achieve similar outcomes. The difference lies in the breadth and depth of AI integration. Leaders invest significantly more, not just in technology but in the talent and governance structures that ensure its success.

Barriers and blind spots

Despite its transformative potential, AI adoption in finance is far from straightforward. The report identifies several critical barriers. Chief among them are data security concerns, cited by over half of respondents, and a lack of skilled talent to implement and manage AI systems. Integration with legacy systems—often complex and expensive—also poses a significant challenge.

Biggest barriers to adoption of AI. Source: KPMG

Generative AI (Gen AI), while promising, introduces additional risks. Transparency and accountability are recurring concerns, particularly with AI’s “black box” nature. Executives worry about the potential for bias, misinformation, and intellectual property issues when deploying Gen AI for tasks like financial reporting.

Yet, the report notes that over 95% of Leaders plan to integrate Gen AI into their processes within three years. The push for transparency, alongside robust governance frameworks, will be critical in overcoming these challenges.

Driving ROI and efficiency

AI’s ability to deliver measurable benefits is well-documented. For Leaders, the return on investment is not merely financial; it’s also about operational efficiency and strategic decision-making.

The report highlights five key clusters of benefits. These include:

  • Improved Data Accuracy: AI enhances data accuracy by automatically detecting and correcting errors, which leads to more reliable decision-making.
  • Faster Decision-Making: Predictive analytics enable quicker and more informed strategic decisions.
  • Cost Reductions: Automating repetitive tasks results in significant operational savings.
  • Operational Efficiency: AI streamlines processes, freeing teams to focus on higher-value activities.
  • Talent Retention: By reducing mundane tasks, AI fosters job satisfaction and attracts skilled professionals.

Top AI use cases piloted or implemented in finance. Source: KPMG

AI’s role in talent retention is emerging as a significant factor. For instance, Gen AI is transforming financial reporting by generating dynamic narratives and scenario analyses. While only 39% of organisations outside the Leaders category have adopted it, the momentum is building. In areas like treasury management and fraud detection, AI applications are proving invaluable.

Organisations piloting these technologies report significant time savings and better compliance with regulatory requirements.

The human factor

What sets Leaders apart isn’t just their technological investments but their commitment to the human side of AI integration. Upskilling staff and creating AI Centres of Excellence are common practices among leading organisations. These initiatives ensure that finance teams are not just users of AI but active participants in its evolution.

Governance is another differentiator. Leaders are twice as likely as their peers to have implemented comprehensive frameworks to manage AI risks and ensure ethical use. Many also seek third-party assurance to validate their AI systems, building trust with stakeholders.