By 2025, Gartner predicts that generative artificial intelligence (GenAI) will be a workforce partner for 90% of companies globally. Yet, research shows that most corporate finance teams lag behind in adoption, hindered by privacy, security, and regulatory challenges.

AI works best as an enabler, not a replacement. Human expertise is essential for interpreting insights and making strategic decisions. This guide empowers you to trust AI and thrive in an AI-driven era, focusing on the intersection of data strategy, AI, and corporate finance.

eBook summary

AI technology is advancing at an unprecedented pace, requiring finance leaders to adapt proactively. This guide covers.

• The journey from rules-based automation to GenAI.

• Why a robust data strategy is essential for success.

• How AI is transforming cash management, forecasting, and fraud detection.

• Why integrating AI is now a necessity for treasury and finance leaders navigating digital disruption.

The evolution of AI: From robotic to human intelligence

Before exploring use cases of AI in treasury management and finance, it’s helpful to understand how the technology has evolved, from simple rules-based automation to today’s advanced generative AI.

The earliest forms of AI in finance focused on automating repetitive tasks using clear rules. Over time, advancements like machine learning and predictive analytics enabled systems to recognize patterns, make data-driven predictions, and support complex decision-making. Today, generative AI combines these capabilities with large language models, enabling conversational technology that can synthesize information and deliver strategic insights.

For CFOs and treasury leaders, this evolution means AI is now capable of answering complex, business-critical questions, streamlining processes, and supporting more proactive decision-making across the organization.

There is no AI strategy without a data strategy

AI’s effectiveness depends on data quality, reliability, and integration. A strong data strategy is essential to unlock value from AI in cash management, forecasting, fraud detection, and investment optimization. Leaders must focus on:

• Ensuring accessible, high-quality data

• Clear ownership and compliance

• Secure integration between systems

Without these foundations, AI’s potential remains untapped.

Once a data strategy has been established, the next step is connecting all of your data sources to a single source of truth. By ensuring seamless integration and communication between banks, ERPs, applications, and data trading platforms, you provide the fuel AI uses to leverage intelligence capabilities effectively.

Leveraging AI for liquidity performance management has enabled organizations to achieve remarkable outcomes:

_{Source: Kyriba Value Engineering Analysis of 341 Corporations}

• $1.04M

  average net interest benefit from 47%+ reduction of idle cash

• $55M

  average free cash flow per $1B revenue from Supply Chain Finance program

• 87%

  reduction in overall risk impact with BI-enabled exposure management

Bridging the trust gap in AI

While AI holds great promise, the recent Kyriba Global CFO AI Survey revealed that 76% of financial leaders see security and privacy risks as top concerns. According to Bob Stark, Global Head of Enablement at Kyriba, there are ways to combat these concerns that could ease CFOs’ discomfort with integrating AI.

“Every CFO that we talk to, they say the same thing. ‘It needs to be our data. [We] need to understand how it works, and we need to ensure that the outputs are our own and only our own, and that they can work within [our] own organization’s policy.’”

Revolutionizing treasury management and finance with the power of AI: 3 applications being used today

AI is already making an impact across treasury functions:

• Cash management

• Cash forecasting

• Payments fraud detection

Agentic AI takes this further. According to IDC’s InfoBrief, agentic AI is the next frontier in treasury: it enables teams to achieve new levels of operational efficiency, gain strategic insights, and free up time for higher-value work without sacrificing control or compliance. Agentic AI combines autonomy with explainability and transparency, ensuring treasury leaders remain in command even as AI takes on more complex tasks.

Cash management

Managing cash flow is complex, but AI-powered tools are transforming the process. Today, AI enables: 

• Transaction categorization: Automatically classifies transactions for better insight and resource allocation. 

• Cash positioning: Predicts future balances and liquidity needs by analyzing historical and market data. 

• Data collection: Streamlines gathering and analysis from multiple sources.

• Hedging optimization: Uses generative AI to suggest optimal hedging strategies based on exposures and goals. 

By processing large volumes of data and generating actionable insights, AI empowers treasury teams to optimize cash management now and in the future.

Cash forecasting

Traditional cash forecasting is slow and error-prone, relying on manual data entry and outdated spreadsheets. This keeps treasury teams a step behind—especially as market volatility rises.

AI streamlines forecasting by automating data consolidation and using predictive analytics to deliver faster, more accurate, and more dynamic forecasts. Key benefits include:

• Predictive analytics: AI analyzes historical and real-time data to improve forecast accuracy, even in new scenarios where little data exists.

• Market forecasting: AI sifts through large volumes of market data to anticipate movements in prices, rates, and indices.

• Supply chain optimization: AI identifies bottlenecks and predicts demand shifts, helping companies optimize operations and reduce costs.

By harnessing AI, organizations gain more reliable forecasts and can respond to change with greater agility and confidence.

Payments fraud detection

AI-powered fraud detection enables real-time identification and prevention of suspicious activities, reducing financial losses and safeguarding trust.

• Real-time monitoring: AI screens payments and detects outliers by analyzing transaction history and user behavior.

• Cross-border vigilance: AI flags anomalies in currency, timing, or geography that signal potential fraud.

• Risk scoring: AI assigns risk scores to transactions, helping teams focus on the most critical threats.

• Predictive analytics: AI anticipates fraud trends, enabling proactive intervention.

With robust, explainable AI and disciplined data practices, treasury teams can accelerate transactions while minimizing fraud risk and ensuring compliance. As Gavaghan explains, “AI success is built on strong foundations. Secure architecture, clear data permissions, and human oversight are critical for adoption in finance. When treasury teams implement AI solutions with rigorous governance and transparency, they not only reduce risk but also gain confidence to leverage AI’s full potential.”

Looking ahead: Building trust, skills, and strategic value with AI

AI is poised to fundamentally reshape the finance function in the coming years. As Gavaghan emphasizes, the future will favor those who can properly use and understand AI to augment human insights, rather than using it as a substitute. By combining technology and strategy, treasurers will be able to foresee current and future risks and opportunities, allowing them to gain a competitive advantage.

CFOs see AI literacy and technology skills as crucial for the next generation of finance leaders, with 59% highlighting their importance. Developing new skills, such as prompt engineering, data storytelling, and understanding explainable AI, is now essential for finance teams to fully leverage AI.

Unlock the potential of AI in treasury management

In today’s environment of rapid digital change and market volatility, CFOs and treasurers play a critical role in maintaining financial stability. AI is fast becoming an essential tool for optimizing operations, managing risk, and enabling informed, agile decisions.

Many treasury professionals are trained to act with a risk mitigation mindset, which shapes how AI is viewed and adopted. As Felix Grevy, Vice-President, API and Connectivity at Kyriba observes, “A strong command over AI and technology, and the ability to implement responsibly, will be essential to keep up with the rapidly evolving technological and economic environment. In order to stay competitive, organisations must prioritise AI-driven skills across the workforce, including within the C-suite.”

While challenges around security, accuracy, and compliance remain, most CFOs are optimistic about AI’s potential. Building trust requires transparency, upskilling, and responsible partnerships.

AI’s influence will only increase—reshaping finance through hyper-personalization, advanced fraud detection, and predictive risk management. Success demands robust data governance, explainability, and continuous learning.

For finance leaders, now is the time to build a trusted AI strategy. By treating AI as a co-pilot, organizations can unlock greater efficiency, innovation, and resilience.