Artificial intelligence (AI) has been around for decades, but the release of ChatGPT at the end of 2022 marked a global revolution. it is, Fastest adopted product in history We have brought AI into the daily lives of consumers and businesses alike. Generative AI (GenAI) tools like ChatGPT and Perplexity are changing the way the world accesses, consumes, and understands data, including financial and financial data.
The world of business-to-business (B2B) payments is increasingly recognizing the potential of AI or machine learning (ML) to deliver tangible value. AI is already being deployed in areas such as exception handling, transaction monitoring, and fraud detection to improve operational efficiency, quickly detect suspicious behavior, and improve the customer experience.
However, it’s important to cut through the hype. AI is not the solution to every problem. Finance teams need to be realistic when implementing AI tools into the products and services they use.
Current state of AI
A practical approach requires, for example, a deep understanding of AI’s current limitations. Despite its rapid development, GenAI, a core component of agent AI, can still cause hallucinations and distort results. Even traditional AI can produce erroneous outputs due to data bias or incorrect assumptions. Therefore, deploying agent AI for tasks such as making high-value payments can pose significant risks at this early stage of commercial applications. Human expertise and involvement will continue to be essential for critical decision-making.
Second, the wave of “AI-first” technologies has created the assumption that AI is the key to removing manual labor from everyday tasks. In fact, there is a danger of over-engineering when introducing AI to simple tasks. For basic tasks like checking account balances, AI adds unnecessary complexity and friction to the user experience.
Also, the market is not yet ready for agent AI. Currently, in the absence of a widely accepted and standardized agent payment protocol, providing banking credentials to an AI agent could violate banking agreements because only authorized signatories must make payments. The regulatory landscape is still evolving to address this issue, in addition to more general concerns about data privacy, transparency, accountability, anti-bias, and risk management when introducing AI-powered autonomous decision-making into financial services.
In most cases, even pioneering AI-enabled organizations tend to rely on a symbiotic relationship between employees and AI assistants, rather than deploying fully autonomous AI agents to do all the paying for them. Finance teams should therefore focus on practical applications of AI that enhance human decision-making, rather than eliminating human intervention.
practical application
AI plays a key role in optimizing payments and cash management for businesses. It is ideal for tasks that require analysis of large datasets, are repetitive, rule-based, occur frequently, and have definable outcomes and limited need for human judgment. This technology powers several key financial areas, including fraud detection, cash forecasting, payment optimization, and investing excess cash.
Fraud detection
Fraud detection is perhaps the most proven and widely adopted use case for AI/ML in financial services. The technology has the ability to analyze large volumes of transactions in real time, recognize complex patterns, and continuously adapt to new fraud techniques, freeing fraud analysts from the manual burden of time-consuming investigations. The business benefits include early fraud intervention, reduced financial loss, regulatory compliance, and improved customer outcomes through fewer false positives.
cash forecast
AI is especially useful for enhancing certain business processes, such as cash forecasting and reporting, as it can account for complex variables and adapt to changing conditions in real time.
In the context of cash optimization, cash forecasting is a fundamental pillar on which future capital transfer decisions are based, supporting strategic foresight. AI-powered predictive models can analyze vast amounts of historical and real-time financial data, identify complex patterns, and more accurately predict future cash flows compared to traditional methods.
Improved payment flow
Payment optimization also lends itself well to AI applications. B2B payments can be complex to operate for organizations that handle multiple banks, multiple geographies, and multiple currencies, especially when considering bank- and railroad-specific cut-off times, fees, and other costs.
Treasurers can receive AI-generated recommendations on the most efficient and cost-effective routes to process payments. For example, instead of sending a single payment through CHAPS, you can split a large payment into multiple Faster Payments Service transactions.
For a business with payments in 20 countries, 30 banks and over 1,000 accounts, AI-generated payment flow recommendations can significantly reduce costs and late fees, send and receive payments faster, and help finance teams better plan their payments operations.
surplus cash
CFOs can leverage AI to get the most out of their company’s remaining cash. From a CFO’s perspective, investing idle cash to achieve better yields is a strategic necessity to grow the business. However, this is a complex issue with many considerations, including access to investment vehicles, market dynamics, regulations, costs and risks.
In addition to automating the first two steps of the investment process (gathering information and analyzing options), AI can also be used to support the decision-making step by providing recommendations. At this critical point, humans must intervene to select the best solution and complete the process.
This is a great example of human-AI interaction and highlights the fact that AI should be used to facilitate, not override, key decision makers in B2B payments.
The path to AI success
While the world isn’t quite ready for autonomous agents to perform critical tasks, AI, ML, and GenAI are transforming the way finance teams access and use information. Today, this technology is powering B2B payments and finance workflows, helping teams get clear answers to relatively complex questions and enable better and faster decision-making.
In the current situation, human involvement and intervention in important decisions remains paramount. Rather than making decisions autonomously, AI should provide options for humans to consider.
Key best practices for implementing AI in B2B payments include identifying the areas where AI can provide the most immediate value, creating a framework and key performance indicators to evaluate AI use cases, and defining business objectives such as increasing efficiency, reducing fraud, and improving cash forecasting. Organizations should also assess their current technology stack and workforce readiness.
The final, and perhaps most important, element to ensure a successful AI implementation is high-quality data. Treasury and treasury teams must ensure that the data underpinning AI models is clean, complete, and consistent across all financial systems to ensure that AI in payments and fintech delivers trusted results. A robust data governance framework is important, including implementing standardized data entry protocols and regular audits.
As with all technological innovations, market dynamics and timing are key factors for success. Given the current backdrop of ISO20022, which signals an increase in the quantity and quality of banking data, there is no better time to implement AI more broadly in corporate banking.
As a market leader in corporate finance data, AccessPay is well-positioned to deliver high-quality, real-time, ISO20022-compliant data at a time when finance leaders recognize that data is the key to unlocking the true value of AI.
Invite your CFO and finance executives to discuss your AI readiness with AccessPay.
