Posted: November 11, 2025 | Updated: January 20, 2026 at 12:13 PM
In today’s financial landscape, artificial intelligence is revolutionizing both treasury management and payment processing. Businesses are deploying AI and machine learning tools to automate routine tasks, forecast cash needs, manage liquidity in real time, and strengthen fraud defenses. Leading solutions illustrate this trend.
Fidelity National Information Services (FIS) recently unveiled Neural Treasury, an AI/ML-powered platform for corporate treasuries that promises proactive cash forecasting, automated operations, and continuous fraud monitoring. Likewise, card networks like Mastercard have introduced advanced decisioning engines – Mastercard’s new On-Demand Decisioning (ODD) allows issuers to embed custom, AI-influenced approval rules directly into the payment network, yielding faster and more personalized authorization outcomes.
This article explores how these AI-driven innovations optimize cash flow and fraud control, and what they mean for companies and finance teams.
Corporate treasury teams traditionally juggle massive amounts of data and routine processes. They must monitor global bank balances, reconcile transactions, forecast inflows and outflows, and execute payments — all while managing foreign exchange, interest rates, and counterparty risks. Legacy systems and spreadsheets often leave treasurers reacting to events rather than planning.
AI remedies these challenges by linking disparate data sources and applying intelligent analytics. Modern platforms ingest ERP, accounting, payment, and market data to generate actionable insights, automate operations, and detect anomalies. The result is a treasury that moves from a retrospective observer to a strategic driver of cash optimization.
One prominent example is FIS’s Neural Treasury suite. Neural Treasury combines cloud software, machine learning, and even robotic process automation (RPA) to support the complete treasury workflow. It includes a specialized large language model (branded as Treasury GPT) trained on treasury data and best practices. Using these capabilities, Neural Treasury helps treasurers:
Other vendors are racing to offer similar treasury AI solutions. Banks and fintechs now sell specialized cash-forecasting tools that apply machine learning to historical transaction data.
These tools often supplement a company’s ERP or treasury management system, adding pattern recognition and statistical modeling capabilities. The industry consensus is clear: AI-driven forecasting and automation are becoming foundational features of modern treasury management.
At the core of AI-enabled treasury is better cash forecasting. Traditional forecasts – based on static models or simple trendlines – can fall short in a fast-changing economy. AI approaches improve on this by integrating internal data with external signals. An intelligent system might combine a company’s payment history with market indicators such as interest rates, foreign exchange volatility, and even commodity prices.
During an unexpected supply-chain disruption, the AI model can quickly re-run scenarios: Should upcoming payments be delayed? Is it better to hedge currency risk now? By simulating these scenarios in minutes, treasury leaders can act immediately.
These predictive capabilities translate to concrete benefits. Treasurers report that machine learning forecasts are not only faster but also more accurate, enabling companies to reduce idle cash buffers safely. Indeed, some firms find they can operate with significantly leaner cash reserves because they trust the AI’s precision. Instead of holding large safety cushions, they rely on dynamic forecasts to know precisely when cash will be needed or freed up. This boosts return on capital – companies put excess cash to work in investments rather than leaving it dormant in bank accounts.
In practice, many companies start small by applying AI to specific forecasting tasks. For instance, one common approach is to use AI for short-term prediction: analyzing the cash effects of incoming customer payments, payroll runs, or known debt obligations over the next few weeks. Others use it to forecast on a quarterly horizon, aggregating forecasted receivables, payables, and market factors. Each use of AI typically means retraining models regularly on new data, so forecasts adapt when business patterns shift (like after a price increase or the launch of a new product).
Besides forecasting, AI aids day-to-day liquidity decisions. For example, when initiating a payment, an AI assistant can recommend the best channel or currency. Suppose a supplier needs funds immediately, and the company has multiple payment options. In that case, the AI might suggest using an instant-pay network (like RTP or FedNow) instead of traditional ACH to ensure the recipient receives the funds on time. If payment is not urgent, the AI could recommend a lower-cost ACH or even splitting the payment to comply with thresholds and minimize fees. By automatically optimizing these choices, businesses save on transaction costs and avoid late-payment penalties.
Beyond forecasting, automation is another frontier where AI is making treasury smarter. Robotic Process Automation (RPA) combined with simple AI capabilities is already replacing many manual back-office tasks. Any repetitive, data-intensive task is a candidate: extracting invoice details from PDFs, matching payments to ledger entries, validating transaction metadata, or consolidating bank statements. AI-powered bots can do these faster and with fewer mistakes.
For instance, instead of a treasurer manually downloading bank statements each morning, an automated system can pull all account statements into one interface. Then an AI engine automatically aligns each line item with company records. Exceptions (unmatched items) are flagged, while the rest reconcile instantly. This not only speeds up month-end closing but also reduces human error from copying numbers or reconciling thousands of transactions by eye.
Another simple example is reducing reliance on legacy spreadsheet macros. Companies often have Excel templates with macros to import data and generate reports. New RPA tools can replace these with standardized data pipelines. Once the data connection is set, AI/automation handles the ingestion and formatting. The finance team can then focus on interpreting the results rather than tinkering with data plumbing.
While treasury teams are optimizing cash and operations, banks and payment networks are also applying AI to the flow of payments themselves. A key area is real-time transaction decisioning. Traditionally, when a customer swipes a card or initiates an online payment, the authorization request is subject to a series of checks. These checks used to rely on static rules (like “block purchases over $10,000”). Now, AI and machine learning are increasingly embedded into this process to make smarter, context-aware decisions instantly.
A prime example is Mastercard’s On-Demand Decisioning (ODD), launched in 2025. ODD lets card issuers insert their own logic – potentially AI-enhanced – into the authorization flow on Mastercard’s network. In practice, this means the bank that issued your card can define more nuanced rules, and Mastercard’s system applies them as the transaction is processed.
An issuer might prioritize approvals for its premium customers: if a high-value client attempts to pay the monthly mortgage, the issuer’s custom rule could ensure approval is granted immediately. If the card had been reissued recently (a common cause of declines), the issuer could also set a rule to reauthorize the transaction automatically rather than decline it.
Because On-Demand Decisioning runs within Mastercard’s own network, issuers gain instant control without having to re-route transactions through separate systems. In effect, it streamlines the authorization process by handling a greater portion of the decision at the network level. Early feedback indicates that banks using ODD see smoother service for essential customers and fewer unnecessary declines, all without extra operational overhead.
Mastercard’s move reflects a broader trend: card schemes and processors are tapping AI to balance security, user experience, and profitability. Visa offers analogous services (like Visa Advanced Authorization) that crunch hundreds of transaction attributes per purchase. These AI-driven engines might consider the cardholder’s past behavior, merchant risk profiles, device information, and more — all in milliseconds.
The goal is always the same: approve more legitimate transactions (boosting revenue and customer satisfaction) while cutting out fraud before it happens. In some cases, the networks’ risk models can prevent tens of billions of dollars in fraud each year by learning from global data patterns.
Beyond cards, instant payment networks (like real-time ACH or peer-to-peer rails) are also integrating AI. Any time a transaction moves between accounts, AI can analyze it on the fly. For instance, if a corporate customer initiates an instant wire transfer out of business hours to a new beneficiary, the system can trigger a risk check based on company history and global intelligence feeds. This “instant analytics” approach means fraud can be spotted even as payments clear within seconds.
A recurring theme is that AI both drives efficiency and serves as a powerful fraud-prevention tool. Fraudsters are using increasingly sophisticated methods (even their own AI) to breach companies’ defenses. AI counter-measures are emerging everywhere in response.
In the payments realm, machine learning models continuously monitor transaction flows. They detect anomalies that would escape simple rules. A sudden change in the location of card purchases or the frequency of card purchases can trigger an alert. These models improve over time as they learn standard patterns for each customer or vendor. As a result, merchants and consumers face fewer false declines, but actual fraud attempts are halted more quickly.
Within corporate finance, AI also mitigates internal and B2B fraud. One primary target is business email compromise (BEC), in which attackers impersonate company executives or suppliers to trick payment staff into wiring funds to bogus accounts. Advanced systems now cross-check vendor details whenever an account change is requested. If an email or instruction looks suspicious (e.g., it comes from a slightly off-domain name or the bank account is offshore), the AI flags it. It might even simulate verification steps—for instance, automatically calling the original vendor’s known contact number—to confirm any change in payment instructions.
The impact can be huge. The U.S. Department of the Treasury and Federal agencies report that AI and machine learning have recently helped prevent and recover billions of dollars in fraudulent government payments. Although corporate treasuries are smaller than federal budgets, the lesson is the same: AI’s ability to process vast data quickly can drastically reduce losses. Companies implementing these tools often discover that the first or second week after deployment, they catch attempts they would have missed before.
Key fraud prevention techniques powered by AI include:
All told, AI brings a proactive stance to fraud control. Instead of waiting for human investigation after a suspicious transaction, these tools work in parallel with operations teams, typically preventing fraud before any damage is done.
For businesses, the combined effect of AI in treasury and payments is profound. Organizations gain greater visibility and control over cash. They can optimize working capital by pinpointing exactly when and where money will be needed. Faster, AI-driven decisions also mean improved customer and partner experiences: suppliers get paid reliably, and customers enjoy smoother payment processing.
Meanwhile, the company’s risk exposure shrinks as fraud losses drop. In one industry report, banks and treasurers noted that AI-enabled risk tools significantly reduced false declines, preserving revenue that would have been lost under stricter manual rules.
These advantages translate into financial results. More accurate cash forecasts might allow a company to reduce lines of credit or negotiate better terms with lenders: automated processes and fewer fraud incidents lower operating expenses and insurance premiums. Perhaps most importantly, treasury staff can focus on strategic planning — analyzing capital structure, financing opportunities, and market risks — instead of mundane chores. This empowers the finance function to become a true business partner, rather than just a back-office function.
However, successful adoption requires planning. AI tools are only as good as the data they use. Businesses must invest in data integration and quality. This often means establishing real-time links between the ERP system, bank accounts, and market data feeds. It also means cleaning historical records so the AI models aren’t learning from flawed information. Companies may need to upgrade their treasury management systems (TMSs) or banking interfaces to leverage AI capabilities fully.
Change management is also crucial. Treasury teams should start with clear use cases: perhaps piloting AI-powered cash forecasting on one segment of the business, or deploying an AI fraud monitor for high-value transactions. Early quick wins build confidence. Leadership should ensure treasury and IT collaborate; many successful implementations assign “AI champions” to guide end users and refine models based on feedback. Staff training is essential too: as systems take over routine tasks, treasurers need to develop skills in data analysis and interpreting AI-driven insights.
Governance cannot be overlooked. Companies should set up oversight for these new systems, just as they would for any critical financial process. This means monitoring AI decisions, regularly testing models, and documenting how automated rules are set. It may involve risk teams reviewing AI models for biases (for example, ensuring credit decisions remain compliant with policy). Regulatory requirements are evolving to cover AI in finance, so organizations should stay abreast of guidelines from bodies such as banking regulators and international standards bodies (for example, the EU’s Digital Operational Resilience Act).
A practical way to move forward is often through partnership. Many businesses begin by working with their bank or a fintech vendor. For instance, banks now offer AI-enhanced treasury services (like cash forecasting tools on their platforms), so a corporate treasurer can experiment without building everything in-house. Similarly, card issuers using network tools such as Mastercard’s ODD or Visa’s risk services can tap into AI capabilities as part of their card programs, leveraging the expertise of those networks.
AI-powered treasury and payments systems are enabling more innovative cash management and stronger fraud control. Companies that adopt these technologies find their treasury departments acting more like nerve centers, guiding strategic financial moves. Automated forecasts help in planning investments; continuous monitoring keeps an eye on unauthorized activity; and customizable decision engines keep payments flowing smoothly.
While implementation takes effort — upgrading data infrastructure and guiding teams through change — the payoff is significant. Businesses that embrace AI in finance are better equipped to navigate uncertainty, respond quickly to opportunities or threats, and protect their bottom line.
The era of AI-enhanced finance is here. As machine learning and intelligent automation become standard tools, treasurers and finance leaders have the opportunity to transform their roles. By leveraging these innovations, companies can achieve quicker, data-driven decisions that optimize cash flow, minimize risk, and ultimately provide a competitive edge in a fast-paced economy.
AI-powered treasury management uses machine learning and automation to forecast cash flows, optimize liquidity, and detect fraud in real time. It replaces manual spreadsheets with intelligent, data-driven tools that improve decision-making and efficiency.
AI analyzes historical data, market signals, and real-time transactions to predict future cash needs than traditional methods more accurately. This helps companies reduce idle cash and plan investments or debt repayment proactively.
Yes. AI models continuously monitor transactions, flag suspicious behavior, and adapt as fraud patterns evolve. Tools like FIS Neural Treasury and Mastercard’s decision engines catch anomalies before money leaves the account.
ODD allows card issuers to apply real-time, AI-based rules directly within the Mastercard network. It enables faster, more personalized approvals while reducing false declines and fraud.
Companies gain more accurate forecasts, lower operating costs, faster payments, reduced fraud losses, and better visibility of global cash. Treasury teams can shift from manual work to strategic financial planning.
