Finance
Invoice Invasion: Defending the Finance Department From Hidden Fraud Risks | PYMNTS.com
Businesses can’t grow without getting paid, and businesses won’t get paid without an invoice.
But fraudsters have taken notice, capitalizing on the fact that the invoice, whether it’s digital or paper, represents one of a company’s most attractive attack surfaces.
Against this backdrop, invoice fraud is a rapidly growing threat, with cybercriminals and internal fraudsters increasingly finding ways to manipulate the payment process for illicit gain.
Invoice and vendor fraud can take many forms, from fake invoices sent by external cybercriminals to fraudulent activities carried out by employees with access to internal systems.
And as the contemporary threat landscape digitizes, with businesses becoming more reliant on digital transactions, the risk continues to rise, especially for companies with outdated systems or weak internal controls.
Cybercriminals have become adept at exploiting weaknesses in digital payment systems. Invoice fraud often starts with a phishing attack or a compromised email account. In these cases, attackers will intercept or spoof communications between businesses, posing as a legitimate vendor or supplier. They then send altered invoices or payment instructions, redirecting funds to fraudulent accounts.
For many B2B companies, these vulnerabilities have become a significant source of financial and operational risk.
Read more: Why Business Email Compromise Scams Target Valuable B2B Relationships
Outdated Systems and Weak Internal Controls: A Recipe for Disaster
The PYMNTS Intelligence report “Automating Accounts Payable for Cost Savings” found that 34% of businesses process more than 5,000 invoices per month. At the same time, separate PYMNTS Intelligence in the report “Getting Paid: Digital Payments for Improving Cash Flow and Customer Experience” found that 75% of companies still use paper checks.
Those two statistics underscore a growing gap in the payments industry: the disconnect between accounts payable (AP) workflows and payments, which can leave businesses vulnerable to inefficiencies and fraud.
That’s because manual and paper-based processes expose companies to risks such as invoice duplication, payment fraud and vendor impersonation. Paper-based systems also make it difficult to implement stringent security controls, while fragmented tech stacks may not offer effective safeguards.
Fraudsters “will call your back-office staff who are not trained in payments fraud prevention and try to communicate false information over the phone. And these staffers, they are great, smart, hardworking people, but they do not have the tools and that is why the fraudsters are attacking them,” Ernest Rolfson, founder and CEO of Finexio, told PYMNTS in an interview posted in July.
“Fraud is the biggest and most important thing we hear from customers today in B2B payments … They want more automation, as much as possible, and they want no fraud,” Rolfson added.
Read also: Unlocking the 3 Biggest Benefits of Automating Accounts Payable
Strategies for Prevention and Risk Mitigation
Data shows the average enterprise receives half of its invoices on paper, with nearly four in 10 (38%) of payments being made manually. Against this backdrop, over a third of firms (36%) cite automating their AP function as a key priority.
Companies that rely on manual processes and systems that are prone to human error and offer limited visibility into transactions can find that they’ve inadvertently made it easier for both external and internal fraudsters to exploit them.
“The inflexibility of traditional systems and platforms have prevented lots of companies from moving forward and keeping up,” Boost Payment Solutions Chief Operating Officer Illya Shell told PYMNTS.
Many businesses, especially small- to medium-sized businesses, also operate with limited financial oversight, allowing fraudulent invoices to slip through the cracks.
But advances in digital payments technology, including automated invoicing and payment platforms with built-in fraud detection capabilities, can help reduce the risk of human error and flag suspicious transactions in real time. These systems offer greater visibility into the payment process and can quickly identify anomalies, such as changes to bank account details or unusual payment requests.
Ultimately, the human layer of defense, as emphasized by many of the risk management leaders PYMNTS has spoken to, is increasingly critical in shrinking enterprise attack surfaces — making individual education around best practices crucial for a company’s own employees.
Developing strong relationships with trusted vendors and suppliers can also help reduce the risk of fraudulent invoices. Businesses should verify vendor details before making payments and regularly review supplier contracts to ensure that services are being rendered as agreed.
Looking ahead, as businesses invest in advanced technologies, strengthen internal policies and educate their employees on fraud risks, the future intersection of both payments automation and fraud prevention looks bright.
“There are a lot of changes happening across a lot of outdated or antiquated industries. We’re in a good space right now to see a lot of change,” Priority Head of Commercial Court Toomey told PYMNTS. “It’s ironic that one of the areas for most companies that is the most outdated are their financial tools, when just a small investment from that same team can go a long way in improving efficiency and also cost savings.”
In a world of interconnected financial markets, policymakers and regulators face the complex task of identifying and addressing risks before they escalate into crises. The 2008-09 global crisis and recent episodes of market dysfunction highlight the need for early warning tools to detect vulnerabilities in real time. However, predicting financial market stress remains challenging, as traditional econometric models often fail to capture the complex, nonlinear dynamics and interconnectedness of modern financial systems.
Recent advances in artificial intelligence (AI) provide new tools to address these challenges. AI methods excel at analysing high-dimensional datasets and uncovering hidden patterns. While they are widely applied in asset pricing (Kelly et al. 2024), they are increasingly used for financial stability monitoring (Fouliard et al. 2021, du Plessis and Fritsche 2025). However, the ‘black box’ nature of AI models has limited their ability to generate actionable policy insights.
This article highlights recent research (Aldasoro et al. 2025, Aquilina et al. 2025) that advances the deployment of AI tools to anticipate financial market stress. These studies demonstrate the potential of AI to forecast market stress and dysfunction, offering both methodological innovations and actionable insights for policymakers by addressing the black-box issue.
The challenge of anticipating financial market stress
Financial market stress can take many forms, including liquidity shortages, price dislocations, and breakdowns in arbitrage relationships. Events such as the 1998 LTCM crisis, the 2008-09 global crisis, and the 2020 ‘dash for cash’ highlight the systemic risks posed by market dysfunction. These disruptions often begin in specific market segments, such as foreign exchange or money markets, but can quickly spread throughout the financial system, threatening its stability. Increasingly, stress has also shifted from traditional banks to non-bank financial intermediaries, reflecting the evolving nature of financial intermediation.
Traditional early warning systems, which were primarily designed to predict full-blown crises, have had mixed success. These models often suffer from high false positive rates and struggle to account for the nonlinear interactions and feedback loops that amplify shocks during periods of stress.
Machine learning (ML) offers a promising alternative, particularly for generating early warning signals. Unlike traditional models, ML algorithms can process vast datasets, identify complex relationships, and adapt to changing market conditions. The studies discussed here demonstrate the potential of these tools to anticipate market stress and provide policymakers with timely warnings.
Using machine learning to model the tail behaviour of financial market conditions
Aldasoro et al. (2025) present a novel framework for predicting financial market stress using machine learning. The study begins by constructing market condition indicators (MCIs) for three key US markets critical to financial stability: Treasury, foreign exchange, and money markets. These indicators (illustrated in Figure 1) capture dislocations in liquidity, volatility, and arbitrage conditions.
Figure 1 Market condition indices for US Treasury, foreign exchange, and money markets
Notes: This figure shows the five-day moving average of market condition indices for the US Treasury, money, and foreign exchange (FX) markets (upper, middle, and lower panels respectively). The sample period is from 01/01/2003 to 31/05/2024.
The paper employs random forest models, a popular tree-based machine learning algorithm, to forecast the full distribution of future market conditions. This approach uses multiple decision trees and averages their predictions, reducing the risk of overfitting. The results are noteworthy: random forest models outperform traditional time-series approaches, particularly in predicting tail risks over longer time horizons (up to 12 months). This is especially evident in forecasting foreign exchange market conditions (Figure 2).
Figure 2 Forecast accuracy of random forest and autoregressive models
Notes: This figure compares quantile losses between the random forest and autoregressive models based on out-of-sample predictions across forecast horizons. Negative values indicate better performance of the random forest model.
To address the black-box issue, the study uses Shapley value analysis to explain the main factors driving market stress predictions. The analysis reveals that macroeconomic expectations and uncertainty, particularly around monetary policy, are significant contributors to market vulnerability. Liquidity conditions and the state of the global financial cycle also play critical roles. This approach not only improves predictive accuracy but also provides actionable insights for policymakers, enabling them to respond proactively to the build-up of vulnerabilities.
Combining machine learning with large language models
Aquilina et al. (2025) take a different approach by integrating numerical data with textual information using large language models (LLMs). The study focuses on deviations from triangular arbitrage parity (TAP) in the euro-yen currency pair, a key indicator of dysfunction in the foreign exchange market. By combining recurrent neural networks (RNNs) with LLMs, the authors develop a two-stage framework to forecast market stress and identify its underlying drivers.
The recurrent neural network detects periods of heightened triangular arbitrage parity deviations up to 60 working days in advance, effectively predicting market dysfunctions that may occur within a one-month window. Out-of-sample testing on 3.5 years of data demonstrates the model’s practical value. For example, the model identified elevated risks before the March 2023 banking turmoil, despite being trained only on data up to the end of 2020 (Figure 3). However, it did not predict the market anomaly caused by the onset of COVID-19, as the event’s origins were external to the financial system.
Figure 3 Predictive accuracy of market dysfunction episodes
Notes: True data: 20-day average of the daily euro-yen triangular arbitrage parity difference with the US dollar as the vehicle currency, calculated on a minute-by-minute basis. The vertical red dashed line represents the end of the training period, end-2020; everything to the right of this line is considered pseudo out-of-sample.
To address the black-box challenge, Aquilina et al. (2025) develop a new architecture for recurrent neural network models that dynamically assigns weights to input variables. This allows the model to identify which indicators are most important for predicting future market conditions at any given time. These weights can then be fed into an LLM to search financial news and commentary for contextual information, helping to uncover potential triggers of market stress.
For instance, during the March 2023 banking turmoil, the model flagged elevated risks in euro liquidity and cross-currency arbitrage. Guided by these signals, the LLM identified news articles discussing tightening dollar funding conditions and rising geopolitical tensions. This targeted approach transforms opaque statistical forecasts into narrative explanations that policymakers can understand and act upon.
Policy implications and conclusions
While much more research into these issues is needed, these approaches show the promise of leveraging AI tools for financial stability monitoring and analysis.
- First, our work has shown that machine learning models are useful in forecasting future conditions of various markets.
- Second, the integration of numerical and textual data through machine learning and large language models provides a richer understanding of market dynamics. Policymakers can use these tools to monitor emerging risks in real time, combining quantitative forecasts with qualitative insights from financial news and commentary.
- Finally, the interpretability of machine learning models is critical for their adoption in policy settings. Techniques like Shapley value analysis and variable-specific weighting not only improve the transparency of forecasts but also provide actionable information about the drivers of market stress.
Overall, these approaches represent a significant step forward in leveraging AI to detect vulnerabilities in financial markets. By combining different methods, the studies offer novel tools for forecasting market stress and understanding its underlying drivers. However, these methods are not without limitations, such as the risk of overfitting and the need for substantial computational resources. Policymakers and regulators should invest in the necessary data and infrastructure to fully harness the potential of these tools.
References
Aldasoro, I, P Hördahl and S Zhu (2022), “Under pressure: market conditions and stress”, BIS Quarterly Review (19): 31–45.
Aldasoro, I, P Hördahl, A Schrimpf and X S Zhu (2025), “Predicting Financial Market Stress with Machine Learning”, BIS Working Papers No. 1250.
Aquilina, M, D Araujo, G Gelos, T Park and F Pérez-Cruz (2025), “Harnessing Artificial Intelligence for Monitoring Financial Markets”, BIS Working Papers No. 1291.
Du Plessis, E and U Fritsche (2025), “New forecasting methods for an old problem: Predicting 147 years of systemic financial crises”, Journal of Forecasting 44 (1): 3-40.
Fouliard, J, M Howell, H Rey and V Stavrakeva (2021), “Answering the queen: Machine learning and financial crises”, NBER Working Paper 28302.
Huang, W, A Ranaldo, A Schrimpf and F Somogyi (2025), “Constrained liquidity provision in currency markets”, Journal of Financial Economics 167: 104028.
Kelly, B, S Malamud and K Zhou (2024), “The Virtue of Complexity in Return Prediction”, Journal of Finance 79: 459-503.
Pasquariello, P (2014), “Financial Market Dislocations”, Review of Financial Studies 27(6): 1868–1914.
US-based bank holding company First Financial Bancorp has completed its previously announced acquisition of Westfield Bancorp and its subsidiary, Westfield Bank.
In June, First Financial agreed to acquire Westfield Bancorp from Ohio Farmers Insurance Company in a cash-and-stock deal valued at $325m.
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
Find out more
With the addition of Westfield Bank, First Financial’s total assets now stand at $20.6bn, strengthening its presence in the Midwest region of the US.
The acquisition is said to expand First Financial’s commercial banking and wealth management services in Northeast Ohio.
Westfield Bank’s retail locations and related services will now operate as part of First Financial’s network.
These branches will retain their current branding until the completion of conversion process, which is expected to occur in March 2026.
The conversion will merge the two banks’ products, processes and operating systems.
Westfield Bank clients will continue to receive services through existing channels, and will receive information about account conversions in the coming months.
First Financial president and CEO Archie Brown said: “This is an exciting step in the growth of First Financial, as the addition of Westfield Bank opens new possibilities for growth and profitability for us in an attractive market.
“We can now bring our wide range of solutions in consumer, commercial, specialty lending and wealth management to new clients, while expanding our geographic footprint for our current clients.
“The First Financial team is thrilled to welcome the Westfield Bank team members to the First Financial family.”
The transaction follows First Financial’s recent expansion activities in the Midwest.
In 2023, the company established a commercial lending presence in Northeast Ohio.
Earlier this year, First Financial announced BankFinancial, the parent company of BankFinancial, National Association, in Chicago, Illinois.
Furthermore, the company has also established a commercial banking presence in Grand Rapids, Michigan.
Sign up for our daily news round-up!
Give your business an edge with our leading industry insights.
Boston, MA7 minutes ago
Where can I watch the Celtics tonight? Celtics vs. Wizards tip off at 7:30 p.m.
Denver, CO17 minutes ago
Greeley police searching for missing Denver woman 2 months after disappearance
Seattle, WA27 minutes ago
Seattle approves Prop. 2, giving small businesses a tax break
San Diego, CA37 minutes ago
San Diegans in need invited to enjoy free Thanksgiving meal
Milwaukee, WI47 minutes ago
Milwaukee Tool recalling over 90,000 chainsaws due to safety risk
-
Milwaukee, WI1 week agoLongtime anchor Shannon Sims is leaving Milwaukee’s WTMJ-TV (Channel 4)
-
News1 week agoWith food stamps set to dry up Nov. 1, SNAP recipients say they fear what’s next
-
Alabama1 week agoHow did former Alabama basketball star Mark Sears do in NBA debut with Milwaukee Bucks?
-
Culture1 week agoVideo: Tyler Mitchell Breaks Down Three Photos From His New Book
-
News1 week ago1 dead, 6 injured in shooting at Lincoln University homecoming festivities
-
Culture7 days agoVideo: Dissecting Three Stephen King Adaptations
-
Seattle, WA3 days agoESPN scoop adds another intriguing name to Seahawks chatter before NFL trade deadline
-
Seattle, WA1 week agoFOX 13’s Aaron Levine wins back-to-back Jeopardy! episodes