This has held particularly true across B2B transactions, where the terms, counterparties, transaction size and payment mechanism each come with their own protective castle walls or deep moat.
This architecture, born in an era of paper checks and early online banking, traditionally prioritized control above all else. If fraudsters were to be kept out, B2B customers would have to put up with long waits, friction-heavy approvals, and an experience that often made legitimate users feel like intruders in their own system.
At the same time, the fortress model of B2B security was never designed for the realities of digital-first commerce. Corporate treasurers and chief financial officers know the pain points, including the authentication process for a wire transfer; batch reviews that delay supplier payments; and mismatches between fraud detection protocols and the actual cadence of business operations.
Fraudsters, meanwhile, have evolved, exploiting the rigidity of these systems. Social engineering, synthetic identities and increasingly sophisticated malware now bypass rules-based defenses. The PYMNTS Intelligence report “Rising Risk: Confronting Modern AP Fraud Threats” found that 9 in 10 businesses in the United States were targeted by cyberfraud last year, and 86% lost money.
Against this backdrop, a new theory of payments security is emerging that flips the script. Instead of creating barriers that slow down every participant equally, firms are constructing security architectures that permeate the transaction environment.
Advertisement: Scroll to Continue
This invisible fence theory of defense is less about locks and more about fields of deterrence, guided by artificial intelligence that operates in the background.
Read also: AI Leapfrogs, Not Incremental Upgrades, Are New Back-Office Approach
B2B Security Matures From Fortresses to Force Fields
Trust has always been the linchpin of B2B relationships. Payment delays don’t just strain cash flow; they erode the credibility that underpins long-term partnerships. In the fortress era, companies were forced to choose between security and efficiency. Stall tactics like multilayered passwords, clunky multifactor authentication, painstaking manual reviews and a labyrinth of compliance checks kept payments safe, for the most part, but also slowed them down.
Invisible fences represent a newer model better suited to the digital age of B2B commerce. At the core of the invisible fence are machine learning algorithms trained to spot subtle deviations. In B2B contexts, these might include a supplier’s bank account suddenly shifting to a new country, a routine invoice appearing at a strange hour, or a buyer whose payment history suggests stable behavior suddenly authorizing a high-risk transfer.
Every data point, from keystroke cadence to device fingerprint to historical supplier behavior, becomes part of a machine learning model. The system doesn’t just ask users to prove who they are with ever-more elaborate rituals. Instead, it builds a probabilistic picture of legitimacy, flagging anomalies before they potentially escalate into fraud.
At the same time, as supplier networks expand, the number of counterparties multiplies, and with them, the risk of fraud. A traditional fortress approach might grind under the weight of complexity.
PYMNTS Intelligence’s August edition of the 2025 Certainty Project found that 97% of mid-market firms reported at least one case of a social engineering attack or fraud in the last year. The most common tactic was a fake invoice scam, experienced by 47% of firms.
Invisible fences thrive on scale. The more data they ingest, the better their AI-driven insights become. In this sense, the new model is not just a defensive measure but an enabler of growth.
See also: AI, Cyber Risk and Payments Monetization Put Treasury at the Center of Finance
Designing for the Future of Security
Looking ahead to the future of B2B security, the metaphor of the invisible fence can itself be instructive.
Invisible fences do not prevent entry altogether; they shape behavior by making deviations costly or impossible. In the same way, AI-driven systems must focus less on building perfect barriers and more on shaping probabilities by tilting the odds toward legitimate commerce and keeping fraud on the margins.
By aggregating data across ecosystems, for example, these security system models may allow firms to assess counterparty credibility instantly. A small supplier in Southeast Asia could lack decades of brand equity, but if its payment history is validated across a trusted network, it can access global buyers more quickly.
Still, for all its promise, the invisible fence approach is not without raising potential new challenges. AI-driven systems can produce false positives if models are poorly trained, leading to the very friction they aim to eliminate. Overreliance on algorithmic decision-making may obscure accountability when disputes arise. Additionally, as attackers themselves adopt AI, the cat-and-mouse game of fraud detection will only intensify.
The stakes are high, but as fraudsters become more sophisticated and as the velocity of global commerce accelerates, the old fortress walls may be showing their cracks.
Register for the upcoming B2B PYMNTS 2025 event, “B2B.AI: The Architecture of Intelligent Money Movement,” taking place Oct. 6-31.
For all PYMNTS B2B and AI coverage, subscribe to the daily B2B and AI Newsletters.
