The Real Cost of Failed Payments

Failed payments cause over half a trillion dollars in losses every year, and that’s just the measurable impact. The scale of the problem is staggering. In 2020 alone, failed payments, those rejected due to incorrect data, account issues, or technical failures, cost the global economy $118.5 billion in fees, labor, and lost business. But that's only part of the story. Add in false declines, legitimate transactions incorrectly flagged as fraud, and ecommerce merchants alone lose an estimated $443 billion annually. Together, these payment failures represent over half a trillion dollars in lost economic activity every year.

These aren't just numbers on a balance sheet. They represent lost revenue, frustrated customers, operational strain, and systems pushed beyond their breaking point. And increasingly, the question isn't just if a payment will fail, it's when, and whether your system is ready for it.

Two Types of Payment Failures

Before we dive into costs, it's important to understand that "payment failure" actually encompasses two distinct problems:

Failed Payments: These are transactions rejected by banks or intermediaries in the payment flow due to technical or data issues, incorrect account numbers, outdated beneficiary details, insufficient funds, expired cards, or system failures. These are infrastructure and data quality problems.

False Declines: These are legitimate transactions incorrectly flagged as fraudulent by fraud prevention systems and rejected before they even reach the payment rails. A customer tries to buy something with a valid card, but the merchant's fraud filters mistake them for a fraudster. These are fraud detection problems.

Both cost businesses and customers billions. Both erode trust. And both require different solutions. Throughout this article, we'll examine how these failures happen, from internal system design flaws to external infrastructure breakdowns, and what can be done to prevent them.

Failed Payments: Estimated Cost for Businesses

The direct cost of a failed payment averages $12.10 per transaction. That includes bank fees, manual intervention, customer service time, and processing corrections. But the real damage runs deeper.

Operational drain: More than 70% of financial institutions report frustration with their failure rates. Each failed payment creates extra work, reviewing transactions, contacting customers, reconciling accounts. On average, banks spend around $360,000 annually on failed payments, while non-bank corporates spend $200,000–$220,000.

Revenue leakage: Depending on the industry, failed payments can result in 8–11% revenue leakage. For subscription businesses, payment failures cause 20–40% of quarterly subscriber churn. These aren't customers who decided to cancel, they're customers lost because the payment infrastructure couldn't hold.

Customer attrition: According to LexisNexis research, 60% of organizations have lost customers due to failed payments. Once a payment fails, 33% of customers won't try again. They'll abandon the cart, cancel the subscription, or find a competitor who can complete the transaction.

In some operational models, investigating a single failed payment can cost as much as $97 per item, a staggering amount when failures scale into the hundreds or thousands.

Failed Payments: Estimated Cost for Consumers

Customers don't just lose a transaction when a payment fails, they lose time, trust, and sometimes money.

Direct fees: Depending on the payment method and region, failed payments can trigger dishonor fees or overdraft charges. In some cases, combined fees can reach up to $70 when factoring in penalties from multiple parties.

Lost convenience: Customers waste time re-entering payment details, contacting support, or finding alternative payment methods. In a world where seamless transactions are the baseline expectation, friction becomes a dealbreaker.

Service disruption: For subscription-based services, a failed payment often means immediate service interruption. A streaming service pauses. A SaaS tool locks. Critical software goes dark, right when the customer needs it most.

Erosion of trust: Repeated payment failures create doubt. If a customer can't trust that their payment will go through, they'll question the business behind it. That loss of confidence is hard to recover.

Internal Threats That Cause Failed Payments

Internal threats to payment systems originate from design decisions, architectural choices, inadequate capacity planning, and operational practices within an organization's control. These failures often stem from assumptions about normal operating conditions that prove inadequate under real-world stress.

1. Backend Overloads and Poor Capacity Planning

Backend overload events typically stem from architectures designed around mean traffic profiles rather than peak demand envelopes. When systems are optimized for average concurrency instead of predictable surge periods, resource contention escalates rapidly, exhausting thread pools, saturating I/O channels, and triggering cascading latency across dependent services.

Events such as payroll cycles, batch settlements, and regulatory reporting windows produce highly concentrated access patterns that are fully foreseeable. Without proactive capacity provisioning, autoscaling thresholds, and load-distribution strategies aligned to these known peaks, service degradation becomes a recurring and systemic failure mode.

Real-World Example: UK Banks’ Month-End Capacity Failures (May 2025)

Between February and May 2025, major UK banks—Lloyds, Nationwide, TSB, HSBC—experienced repeated outages at month-end, following a consistent pattern at the end of each month. Over 1.2 million users were impacted across these incidents. Customers were unable to log in, complete payments, or access salary deposits during critical periods.

The failures occurred during predictable traffic spikes associated with salary processing cycles. The infrastructure lacked sufficient capacity to handle the concentrated load generated when large volumes of users simultaneously attempted to access their accounts following payroll processing.

Root Cause 

Systems designed for average traffic loads without adequate provisioning for known, recurring peak demand periods will experience regular service degradation. Predictable events such as batch processing cycles, salary disbursements, and tax deadlines require infrastructure capacity planning that accounts for these consistent load patterns.

2. API Rate Limiting Failures and Protocol Violations

API-level instability often emerges when participants overload shared infrastructure by ignoring protocol constraints or retry guidelines. Distributed payment systems depend on strict rate limits, idempotency rules, and orderly request flows to maintain stability across thousands of interconnected nodes. When even one participant bypasses these controls, intentionally or through faulty implementation, the resulting surge in calls can saturate shared endpoints, overwhelm central switches, and cascade into system-wide outages. These failures are rarely accidental; they usually reflect gaps in protocol enforcement and inadequate guardrails against non-compliant client behavior.

Real-World Example: UPI Nationwide Disruption (April 2025)

On April 12, 2025, UPI payments in India experienced significant disruption. Transaction success rates dropped to approximately 50% across major platforms including Google Pay, PhonePe, Paytm, and banks such as HDFC and SBI.

The root cause was attributed to some PSP banks repeatedly calling the NPCI servers with Check Transaction API requests, bypassing the established 90-second rate limit. This behavior resulted in server congestion and a nationwide disruption lasting approximately two hours. This represented the third outage within a 30-day period.

NPCI's response included acknowledging the issue, directing banks to cease the repeated calls, and implementing a fix within 15 minutes. Service was restored by 4:40 PM. The disruption prevented consumers from completing essential purchases and created transaction delays for merchants, generating thousands of customer complaints.

Root Cause  

Protocol specifications and rate limits serve critical functions in maintaining system stability. Systems require enforcement mechanisms to reject or throttle non-compliant behavior, prevent excessive retry attempts, and maintain service availability when individual participants violate established protocols. Without proper enforcement, a single misbehaving client can degrade or disable services for all users.

3. Centralized Architecture Without Fault Isolation

Centralized systems become fragile when they lack well-defined isolation boundaries and containment controls. In tightly coupled architectures, a failure in one node can ripple through the entire network, overwhelming shared dependencies and disabling unrelated services. Without segmentation, redundancy, or automated failover paths, localized faults escalate into statewide or even nationwide outages. These incidents highlight the importance of designing distributed systems that degrade gracefully rather than collapsing entirely when a single component becomes unavailable.

Real-World Example: North Carolina DMV Network Failure (July 2025)

On July 16, 2025, a single network switch failure in North Carolina's DMV network resulted in all 112 offices losing credit card payment capabilities. The outage affected in-person transactions, online payments, and kiosk-based services. The system operated on a cash-only basis until late July 17.

The incident demonstrated the vulnerability of centralized systems that lack proper fault isolation mechanisms. The failure of a single component propagated throughout the entire network, preventing any payment processing capability statewide. No failover mechanisms activated, and no graceful degradation occurred.

Root Cause  

Architectures where a single component failure can cascade to complete system failure represent significant operational risk. Resilient system design incorporates fault isolation such that component failures are contained and do not propagate. Effective architectures implement automatic retry mechanisms, component restart capabilities, and maintain service for unaffected portions of the system even when individual components fail.

4. Maintenance Windows Without Real Redundancy

Maintenance-related outages often occur when core systems lack true redundancy or parallel processing paths that allow updates without halting transaction flows. In high-availability financial networks, planned downtime is just as dangerous as unplanned failure when the architecture cannot support rolling upgrades, traffic draining, or live failover during maintenance cycles. If a platform requires a full shutdown to apply updates, every dependent institution inherits the outage, regardless of whether the event is scheduled or unexpected.

Real-World Example: SEPA Transfer Suspension During TARGET2 Maintenance (April 2025)

Between April 18 and 21, 2025, SEPA bank transfers across the eurozone were offline for four days due to scheduled maintenance of the TARGET2 system. Despite being a planned event, the suspension created a complete halt in payment flows during the Easter weekend period.

The maintenance window prevented rent payments, supplier invoices, and routine transfers from processing. Customer service operations experienced high volumes of inquiries and complaints throughout the suspension period.

Root Cause  

High-availability payment systems require architecture that permits system evolution and maintenance without complete service interruption. When financial transactions must flow continuously across borders, platforms, and time zones, four-day service suspensions create significant operational disruption. Modern system architecture should support rolling updates, hot-swappable components, and maintenance procedures that maintain service availability during upgrade cycles.

5. Hardware Failures and Backup Systems That Don't Actually Work

Critical infrastructure failures often stem from redundancy that exists only on paper, not in operational reality. High-availability systems depend on hardware fault detection, automated failover, and redundant components that activate instantly when primary systems degrade. If failover paths are misconfigured, untested under real load, or incapable of taking over at production scale, a single hardware fault can escalate into a full-system outage. These breakdowns reveal gaps not in hardware itself, but in the orchestration and reliability of the mechanisms designed to protect against it.

Real-World Example: TARGET2 Hardware Failure and Failed Failover (February 2025)

On February 27, 2025, the European Central Bank's TARGET2 payment system—the backbone of eurozone interbank transfers—experienced a service outage lasting nearly 7 hours. The system, which processes approximately €2 trillion daily, was unable to complete transactions including salaries, social security payments, and financial market settlements.

A critical hardware component failed. The backup systems failed to activate as designed. The fault detection mechanism did not identify the issue quickly enough, and the failover process did not execute successfully. During the outage, government welfare payments were delayed, employee salaries were not distributed on schedule, and financial markets lost access to settlement infrastructure.

Root Cause  

Failover systems must function reliably in production environments, not just in testing scenarios. For payment systems processing trillions in daily volume, effective resilience requires not only redundant infrastructure but also reliable fault detection, automatic failover activation, and seamless service recovery. The true measure of system resilience is whether recovery occurs transparently without requiring manual intervention or creating visible service disruption.

6. Poor Data Quality and Manual Validation

Payment failures frequently originate from poor data quality compounded by manual processing. When systems rely on human intervention for validation, errors in account numbers, beneficiary details, or transaction metadata can propagate undetected. Manual steps not only increase the probability of mistakes but also create bottlenecks that delay transaction flow and reduce overall throughput. High-volume payment networks require automated data verification and straight-through processing (STP) to prevent these errors from reaching production infrastructure.

Real-World Example: Payment Failures Due to Data Quality and Manual Validation

According to LexisNexis research, incorrect beneficiary bank details—including names, addresses, and account numbers—represent one of the primary causes of payment failures. A significant portion of payment data continues to undergo manual validation processes, which substantially increases error probability.

Payment systems lacking robust straight-through processing (STP) capabilities require human intervention at multiple stages. Manual processes introduce data entry errors, validation delays, and processing bottlenecks that automated systems with real-time validation could prevent.

Root Cause  

Payment workflows dependent on manual validation introduce systematic risk at each intervention point. Automated validation systems, real-time data verification, and data integrity controls can identify and prevent errors before transactions reach the payment infrastructure, significantly reducing failure rates attributable to data quality issues.

External Threats That Cause Failed Payments

External threats originate from infrastructure dependencies outside an organization's direct control, including third-party service providers, telecommunications networks, power infrastructure, and malicious actors. These dependencies create systemic vulnerabilities that can propagate payment failures across multiple organizations simultaneously.

1. Third-Party Infrastructure Failures

Failures in third-party infrastructure are a common source of payment system disruptions. Modern financial networks depend on multiple interconnected systems, each operated by different organizations. When one partner experiences an outage or internal malfunction, transaction flows can stall, confirmation messages may fail to propagate, and users can see inconsistent or delayed results. These issues highlight the inherent risk of inter-system dependencies, where even a well-functioning primary platform cannot fully mitigate the impact of external failures.

Real-World Example: Zelle Disruption Due to Fiserv Infrastructure Failure (May 2025)

On May 2, 2025, Zelle users across multiple U.S. banks—including Bank of America, Capital One, Truist, and Navy Federal—experienced disruptions in sending and receiving money transfers. Investigation traced the root cause to an internal infrastructure issue at Fiserv, one of Zelle's key infrastructure partners.

The failure manifested as transactions appearing to initiate successfully from the user perspective, while confirmation messages failed to propagate between backend systems. This disruption in the confirmation loop prevented transaction completion without providing clear status information to users. Application interfaces remained operational, but actual transaction settlement was impaired. Service restoration occurred later the same day, though customers experienced stuck transfers, missing payment confirmations, and fund receipt delays.

Root Cause  

Payment system failures frequently occur at integration points between systems—neither fully within the primary system's control nor entirely the responsibility of third-party vendors. These inter-system dependencies create complexity that becomes visible during failure events. When communication layers between systems fail, the disruption propagates regardless of which organization technically owns the failing component, affecting all participants in the payment chain.

2. Network and Connectivity Failures

Network and connectivity failures can propagate quickly through payment ecosystems due to shared upstream dependencies. Payment systems often assume stable infrastructure and rely on continuous connectivity for transaction processing. When routing issues, switching failures, or acquirer-level disruptions occur, even fully operational endpoints cannot complete transactions. These failures are particularly impactful for small businesses and mobile-dependent services, as the lack of redundancy or fallback mechanisms can turn brief network outages into significant revenue losses.

Real-World Example: Mobile and Card Payment Disruptions in France and the Nordic Region (June–July 2025)

On June 16, 2025, mobile service disruptions affected major French cities including Paris, Lyon, and Marseille. Users lost access to mobile data, voice calls, and SMS services for several hours due to a routing issue within SFR's network infrastructure.

While many users experienced only inconvenience, small businesses operating card readers dependent on mobile data connectivity faced complete loss of payment processing capability. Food trucks, cafés, and kiosks had no alternative payment infrastructure and either reverted to cash transactions or lost sales entirely during the lunch service period. Estimated gross sales losses ranged from €5M to €15M.

On July 20, 2025, card payment failures occurred across Denmark and portions of the Nordic region. The disruption affected supermarkets, public transport hubs, and service providers. Systems experienced silent failures—transactions did not complete, but no error messages or system crashes provided clear indication of the problem.

The outage pattern was not isolated to specific retailers or terminal brands, suggesting failure at the payment infrastructure level. The simultaneous nature of the disruption across multiple organizations indicated shared upstream dependencies, potentially at the network layer, switching infrastructure, or acquirer processing level. Such upstream failures typically cannot be resolved through local intervention.

Root Cause  

Many payment systems operate under the assumption of stable network connectivity without implementing fallback mechanisms for network failures. When connectivity infrastructure fails, systems must either gracefully degrade service or provide clear failure indicators rather than silent transaction failures that create user confusion and operational uncertainty.

3. Power Outages

Power outages reveal the physical infrastructure dependencies that underpin all digital payment systems. Even highly redundant networks and cloud-based platforms rely on electricity to operate ATMs, payment terminals, and transaction processing nodes. When the power supply is disrupted, upstream and downstream services alike fail, often simultaneously, affecting millions of users and creating cascading economic impacts. Designing for resilience requires anticipating these dependencies and implementing measures to maintain critical functionality during grid failures.

Real-World Example: Iberian Peninsula Power Outage Impacting Payment Systems (April 2025)

On April 28, 2025, a large-scale electrical blackout affected regions of Spain and Portugal. The power loss disabled not only residential and commercial lighting but also critical financial infrastructure including ATMs, payment terminals, and retail transaction networks. Payment processing was limited to cash transactions for the duration of the outage.

The blackout originated from instability in the high-voltage electrical grid, exacerbated by extreme weather conditions and stress on the broader European energy network. Point-of-sale terminals became non-functional, ATM networks went offline, and businesses had no capability to process digital transactions.

Spain's primary business association estimated the outage reduced GDP by approximately €1.6 billion, with total economic impact projected between €2 to €5 billion. Multiple industries required multi-day recovery periods. The meat sector alone reported €190 million in losses from product spoilage. The disruption affected approximately 60 million people across affected regions, with outages lasting up to 10 hours in many areas.

Root Cause  

Payment infrastructure is built with implicit assumptions about power availability. However, electrical grids, data centers, and telecommunications networks all experience periodic failures. During power loss events, even advanced digital payment systems become non-functional. Payment system resilience must account for infrastructure dependencies including power availability, requiring either alternative power sources, offline transaction capabilities, or explicit contingency procedures for power loss scenarios.

4. Cyberattacks and Ransomware

Cyberattacks and ransomware expose systemic vulnerabilities in payment and claims processing infrastructure. When critical systems are compromised, transaction flows halt, billing and reimbursement processes stop, and dependent organizations experience severe operational and financial disruption. Extended outages in payment processing can propagate liquidity crises, particularly impacting smaller entities that cannot sustain operations without continuous cash flow. Designing resilient systems requires layered cybersecurity defenses, rapid detection, and recovery protocols that minimize operational downtime even in the event of an attack.

Real-World Example: Change Healthcare Ransomware Attack (February 2024)

In late February 2024, a ransomware attack compromised Change Healthcare's systems, affecting the payment and claims processing infrastructure serving a substantial portion of the U.S. healthcare system.

Claims processing, billing systems, and payment infrastructure remained offline for an extended period measured in weeks rather than hours or days. Many healthcare providers lost the ability to receive reimbursements, with some requiring emergency loans to maintain operations during the disruption. Recovery efforts continued into March and beyond. Nearly one year after the incident, UnitedHealth confirmed that 190 million individuals—representing more than half of the U.S. population—were affected by the breach.

The financial impact on healthcare providers was severe. Physicians depleted personal financial reserves, pharmacies secured bridge financing, and small practices faced potential closure not due to lack of demand but due to complete cessation of payment flows.

Root Cause  

When payment processing infrastructure experiences outages lasting hours, the impact constitutes an operational inconvenience. When payment systems remain non-functional for weeks, the disruption escalates to an existential threat for dependent businesses. Extended payment infrastructure failures create cascading liquidity crises throughout affected industries, particularly for smaller organizations lacking sufficient capital reserves to sustain operations during prolonged payment processing disruptions.

5. Software Updates and Security Tool Failures

Failures in foundational software updates and security tools can propagate widely across dependent systems. When critical IT infrastructure relies on a single security platform, defects in updates can cascade through administrative, billing, and operational workflows. Even without external attacks, such failures can halt business-critical processes, disrupt revenue flows, and impair supply chain operations. Resilient systems require careful testing, staged rollouts, and isolation mechanisms to prevent a single update from impacting global infrastructure.

Real-World Example: CrowdStrike Update-Induced System Failures (July 2025)

On July 19, 2025, a defective CrowdStrike software update triggered widespread IT system failures across global infrastructure. The incident affected over 750 U.S. hospitals, which lost access to digital services including billing systems, scheduling platforms, and patient intake processes. Recovery times ranged from several hours to multiple days depending on the facility.

The faulty update caused mass device failures across organizations. Healthcare facilities with CrowdStrike deployed across their infrastructure experienced comprehensive system freezes affecting not only security functions but also administrative and revenue management tools.

The disruption extended beyond healthcare to global logistics operations. Shipping companies, air cargo operators, and supply chain coordination systems depend on IT infrastructure to manage deliveries, track shipments, generate invoices, and process payments. On July 19, significant portions of this infrastructure became non-functional.

Fleet management systems, cargo booking platforms, and automated invoice generation tied to shipping milestone completion all depend on underlying IT infrastructure layers. When this foundational layer failed, downstream workflows ceased functioning. For numerous logistics operations, applications remained technically running but business processes broke down, resulting in halted deliveries, suspended billing triggers, and frozen inter-account fund transfers.

Root Cause  

This incident originated not from external attackers but from a failure within widely deployed security infrastructure itself. Security tools are typically granted broad system access and deep integration, creating potential for catastrophic failure propagation when updates contain defects. The incident raises questions about assumption validity regarding third-party security tool reliability, the risks of monoculture in security tool deployment, and accountability structures when failures originate from protection mechanisms rather than threats.

6. Upstream Payment Processor Failures

Failures at upstream payment processors can create cascading outages that affect all downstream participants. Even robust merchant and banking systems cannot compensate when the core processing infrastructure of a payment network malfunctions. Such failures impact high-volume transaction flows, particularly for small businesses that rely heavily on card payments and lack alternative mechanisms. Effective payment system design requires contingency planning, redundancy, and mitigation strategies to handle processor-level failures without halting operations.

Real-World Example: UK Small Business Card Payment Disruptions (September 2024)

On September 12, 2024, card payment processing failures affected thousands of UK small businesses. The disruption manifested as hours of failed transactions at retail shops, cafés, and wholesale establishments that depend on card payment acceptance for operations.

The failures originated from issues at third-party payment processors, rendering numerous point-of-sale terminals non-functional. Small businesses lacking fallback payment infrastructure were unable to conduct business operations, particularly those that have largely discontinued cash handling.

For businesses operating on narrow profit margins, several hours of payment system downtime translated to complete loss of a day's revenue. Aggregate estimates suggest UK retail and hospitality sectors lost approximately £1.6 billion to payment outages during this period, representing thousands of individual business disruptions.

In 2018, Visa experienced a payment processing outage affecting the UK and European regions. Card transactions failed to process or stalled during authorization. Customers initially attributed failures to individual cards, issuing banks, or merchant terminals, but investigation traced the root cause to Visa's core processing infrastructure.

When failures occur at the payment processor level rather than at merchant or bank systems, all downstream participants experience service disruption regardless of their individual system health. Despite modern infrastructure, geographic redundancy, and global routing capabilities, concentrated processing architectures create single points of failure where issues at one central node can degrade service across large geographic regions.

Root Cause  

Payment systems require contingency planning for scenarios where primary payment processors experience failures. Organizations should evaluate whether they maintain viable alternatives when their primary processing infrastructure encounters issues, and whether over-reliance on single payment pathways creates unacceptable operational risk even in nominally distributed and scalable system architectures.

7. The Human Factor: Systemic Payment Delays

Payment system failures are not always sudden outages—they can manifest as gradual, systemic delays that strain cash flow across industries. Extended settlement times, processing backlogs, and technical debt accumulate to create widespread operational stress. When multiple participants experience delayed payments simultaneously, the impact propagates through supply chains, forcing businesses to allocate resources to cash collection instead of core operations. Understanding and mitigating these delays requires monitoring, process automation, and systemic improvements to prevent operational dysfunction from becoming normalized.

Real-World Example: Freight Payment Delays and Systemic Cash Flow Strain (2025)

Payment system failures do not always manifest as immediate service outages. Some disruptions occur gradually as payment processing timelines extend from days to weeks, creating cash flow constraints and forcing businesses to pursue outstanding receivables that previously settled reliably.

In 2025, freight transportation operations continue functioning operationally, but corresponding payment settlements have experienced significant delays. Approximately 60% of freight service providers now report waiting periods exceeding 60 days for invoice payment settlement. Multiple factors including cost pressures, tariff implementations, and backend system failures have compounded these delays. Many freight companies face liquidity challenges despite maintaining full order volumes.

Back-office systems responsible for invoice processing, payment approvals, and fund disbursement are experiencing strain from multiple sources including cost reduction initiatives, accumulated technical debt, and processing slowdowns that have become normalized rather than exceptional. Smaller carriers are declining business opportunities because payment settlement delays exceed their working capital capacity. Operations teams allocate more resources to payment collection activities than to core freight movement operations.

Root Cause  

When 60% of an industry segment reports payment settlement periods exceeding 60 days, the issue transcends individual business practices and indicates structural systemic dysfunction. When businesses maintaining supply chain infrastructure must secure external financing to manage cash flow gaps created by payment delays, the underlying payment infrastructure bottlenecks have become critical operational constraints affecting the broader economic system.

Long-Term Business Risks

Failed payments don't just hurt today's revenue, they threaten tomorrow's growth.

Undermined Scalability

As a business grows, the number of failed payments increases. If left unaddressed, this can hinder a company's ability to scale effectively and create an unsustainable financial weakness. What works at 1,000 transactions per day might collapse at 10,000.

Investor Concerns

For subscription businesses seeking investment, high involuntary churn due to payment failures signals operational immaturity and a flawed business model. Investors look at retention curves. When churn spikes from payment failures, it raises questions about infrastructure readiness and long-term viability.

Decrease in Revenue and Business Instability

Revenue leakage compounds over time. An 8% loss from failed payments might seem manageable in month one. But over a year? Over three years? It becomes the difference between profitability and struggle.

Regulatory Scrutiny and Mandatory Standards

After a string of outages across major banks in Singapore in early 2025, the Monetary Authority of Singapore (MAS) stepped in. It called on banks to strengthen their IT resilience—not just uptime, but how they detect, isolate, and recover from failures.

Key measures now include:

• Stricter thresholds for availability

• Faster response plans for critical systems

• More frequent resilience audits and infrastructure reviews

MAS didn't wait for another outage to act. They treated recurrence as a design flaw—not just an operational issue. After a point, a disruption isn't just about the bug. It's about how many misses a system gets before trust wears thin.

How to Mitigate the Costs

Reducing failed payments isn't just about fixing bugs. It's about designing systems that expect failure and recover gracefully.

Implement Smart Retry Logic

Use automated retry systems that don't just retry immediately, but use a schedule (e.g., a retry after a few days, timed to likely paydays). Tailor the retry logic to the kind of decline (soft vs hard declines). Limit the number of retries to avoid excessive transaction fees or irritating customers.

Use Proactive Communication ("Dunning")

Send friendly reminders before a payment is due or before a card expires. After a failed payment, send 2–4 follow-up messages (via email, SMS, or app) spaced over several days to ask the customer to update their payment method or retry. Make sure your messages are mobile-friendly and include a direct link to update payment details.

Offer Multiple Payment Methods

Don't rely solely on credit/debit cards. Support digital wallets (Apple Pay, Google Pay), bank transfers (SEPA, ACH), or even Buy Now Pay Later (BNPL) if it makes sense. Allow customers to set a backup payment method that will be used when the primary fails.

Use Card-Update Services

Many payment processors support "account updater" services that automatically refresh expired or replaced card details, reducing declines due to outdated data. Remind customers proactively (e.g., 30 days before card expiry) so they can manually update if needed.

Analyze Declines and Use Data-Driven Strategies

Track why payments are failing. Different decline codes (from banks) tell different stories. Use data to segment customers (e.g., by geography, payment behavior) and apply different retry or communication strategies. Consider routing payments dynamically: if a payment fails with one processor or gateway, retry via another.

Optimize the Checkout / Billing Experience

Simplify the checkout flow: minimize form fields, validate input in real time, optimize for mobile. Make updating payment data very easy: self-service billing portals where customers can change cards, download invoices, or update subscriptions. Communicate clearly: what happens if payment fails, what they need to do, how to update.

Use Predictive or Machine-Learning-Based Recovery Tools

Some tools (or payment providers) use ML to predict when a retry is likely to succeed, optimizing retry timing. Using such tools can increase recovery rates and reduce wasted retries / transaction fees.

Prevent Fraud / Unnecessary Declines

Use fraud detection tools (e.g., risk-based authentication, 3D Secure) to prevent fraud, but do it in a way that doesn't overly block valid customers. Ensure all transaction data is correct (address, zip code, etc.)—sending good metadata to banks reduces false declines.

For false declines specifically:

• Don't rely solely on automated rules. Catch-all rules like "decline all billing/shipping mismatches" will block legitimate customers who ship gifts, travel frequently, work from offices, or live abroad.

• Use behavior analytics. Fraudsters rush to checkout; legitimate customers browse and compare. These behavioral signals can enrich your decisioning.

• Embrace machine learning. AI-powered fraud systems can analyze thousands of data points—IP, device, purchase history, behavioral patterns—to make more nuanced decisions than rigid rule-based systems.

• Implement EMV 3-D Secure. This protocol shares 130+ data elements with issuers, providing 10x more context than older systems, resulting in better authentication with fewer false declines.

• Limit blocklists carefully. Consumer behaviors change constantly. Express shipping used to signal fraud; now it's common for last-minute holiday orders.

Remember: false declines were expected to generate a $157B loss for US merchants alone in 2023, and globally were set to exceed $443B—far outweighing actual fraud losses. The cost of blocking good customers often exceeds the cost of the fraud you're trying to prevent.

Build for Fault Isolation and Graceful Degradation

Design systems where failure doesn't spread. One piece breaks, but the rest holds. Processes fail without taking the whole system down. Broken components restart automatically. And the lack of shared state keeps issues from spreading.

This is the model that holds up in environments where outages aren't rare—they're expected. What makes the difference is how well the system absorbs the hit and keeps moving.

Design for Real Redundancy

Real redundancy isn't just having a backup—it's making sure the backup actually works when called upon. Test failover systems regularly. Ensure that hardware failures trigger automatic recovery. Don't wait for production to find out your backup doesn't work.

Plan for Upstream Dependencies

Understand the shared pieces in the middle of all your systems. Know how they fail. Know how to catch issues before they cascade. And when fallback isn't built in, have contingency plans for who carries the weight on the ground.

Manage Administrative Costs Through Automation

Automate as much of the recovery / collections process as possible to avoid manual overhead. Validate account details early (especially for direct debit) to reduce rejections. Invest in straight-through processing (STP) to minimize human intervention points.

Negotiate With Payment Providers / Use Multiple Providers

Work with payment processors that give you detailed decline codes, good reporting, and tools for retry / dunning management. Consider splitting volume across multiple payment service providers (PSPs) to improve success rates and have fallback options.

Conclusion

The documented cases examined in this article—ranging from a single network switch failure in North Carolina to a ransomware attack disrupting healthcare payments for weeks—reveal a consistent pattern in payment system vulnerabilities. These real-world examples were brought to life by Gabi Bota, CEO and co-founder of Crafting Software, through his personal newsletter, Edge Notes: Payment Outages.

The fundamental question for payment infrastructure is whether systems can execute reliable automated recovery when component failures occur, or whether they require manual intervention and extended downtime. Payment failures are not anomalous edge cases but rather regular occurrences that payment systems must be designed to handle. System resilience is determined by architectural decisions made during design and implementation: whether individual component failures cascade to complete system failures, or whether systems maintain operational capacity through automated recovery mechanisms.

The distinction between resilient and fragile payment systems lies in their design assumptions and recovery capabilities rather than in optimistic projections about component reliability. In an economic context where failed payments cost the global economy $118.5 billion annually and false declines generate an additional $443 billion in losses, payment system design choices have measurable economic consequences. Effective payment infrastructure requires engineering approaches based on realistic failure models rather than assumptions of continuous component availability.

FAQ: Failed Payments Prevention and Solutions

1. How do we determine the right capacity buffer for handling predictable payment spikes like month-end payroll?

Capacity planning for payment systems should account for peak load scenarios, not average traffic. Best practices suggest provisioning for 3-5x average load to handle predictable events like salary cycles, tax deadlines, and holiday shopping periods. Implement load testing that simulates real-world traffic patterns including concurrent user sessions, batch processing jobs, and API call volumes. Use historical data to identify your specific peak periods and add 20-30% headroom beyond observed maximums to accommodate growth.

2. What specific rate limiting strategies prevent API abuse without impacting legitimate high-volume users?

Implement tiered rate limiting with different thresholds for different client categories. Use token bucket or leaky bucket algorithms that allow brief bursts while preventing sustained abuse. Set progressive backoff policies that increase wait times for repeat violators. Include circuit breakers that automatically disconnect clients violating rate limits for escalating time periods (e.g., 1 minute, 5 minutes, 15 minutes). Provide real-time rate limit status in API responses so legitimate clients can self-regulate before hitting limits.

3. How can we architect payment systems to isolate failures and prevent single-point cascades?

Deploy service mesh architectures that compartmentalize components with bulkheads, preventing failures from propagating. Implement the Strangler Fig pattern to gradually replace monolithic systems with microservices that fail independently. Use message queues to decouple synchronous dependencies—if one service fails, messages queue rather than requests failing immediately. Deploy circuit breakers that detect failing downstream services and stop sending requests until recovery. Maintain stateless service design so any instance can handle any request, allowing automatic failover without session loss.

4. Our business operates across multiple banks and payment processors—how would a payment orchestration platform have prevented the Zelle/Fiserv outage from affecting our operations?

A payment orchestration engine like the one Crafting Software builds provides intelligent failover routing when it detects infrastructure issues at any payment service provider. During incidents like the May 2025 Fiserv outage that affected Zelle, the platform automatically: (1) monitors real-time health of all connected payment providers through continuous transaction success rate tracking and response time analysis, (2) detects degradation within seconds and immediately routes new transactions through alternative payment rails—if Zelle/Fiserv fails, transactions automatically shift to direct bank transfers, wire services, or other P2P networks, (3) implements smart retry logic that queues failed transactions and resubmits them through working channels rather than losing them entirely, and (4) provides real-time observability dashboards showing which providers are experiencing issues and how transaction volume is being redistributed. The orchestration layer abstracts away individual provider dependencies, treating all payment methods as interchangeable resources and automatically selecting the most reliable path for each transaction. Partners like Crafting Software specialize in implementing these multi-rail strategies to ensure no single vendor's technical problems can completely halt your payment operations.

5. What's the industry standard maximum acceptable maintenance window for payment infrastructure?

For modern payment systems, zero-downtime deployments should be the target rather than scheduled maintenance windows. Implement blue-green deployments or rolling updates where new versions deploy alongside old versions, traffic gradually shifts, and old versions retire only after validation. For infrastructure requiring downtime, maintenance windows should not exceed 4 hours and must avoid known high-traffic periods. Critical payment rails processing international transactions should implement follow-the-sun maintenance strategies where regional systems update during local low-traffic periods while other regions maintain service.

6. How do we validate that failover systems actually work before production failure occurs?

Implement chaos engineering practices where you deliberately introduce failures in production-like environments to test recovery. Conduct quarterly disaster recovery drills where you force failover to backup systems and measure recovery time objectives (RTO) and recovery point objectives (RPO). Use canary deployments that route small percentages of traffic to backup systems continuously, validating their functionality. Deploy synthetic transaction monitoring that continuously tests failover paths. Document and automate runbooks so failover doesn't depend on specific personnel being available.

7. After the CrowdStrike incident affected 750+ hospitals, how can payment software vendors help us maintain billing operations during similar third-party security tool failures?

Payment software vendors can implement several protective layers against third-party tool failures. First, deploy containerized or sandboxed payment applications that isolate payment processing from security tools, so security software failures don't freeze billing systems. Second, implement progressive deployment strategies where security updates roll out to 1-5% of systems first, allowing detection of issues like the CrowdStrike defect before widespread impact. Third, maintain offline payment processing capabilities where critical billing functions continue operating in a degraded but functional mode even when security tools fail—transactions queue locally and sync when systems recover. Fourth, vendor platforms should include independent health monitoring that detects when third-party dependencies (security tools, network services, authentication systems) are causing operational issues and automatically reroutes workloads to unaffected infrastructure. Fifth, cloud-based payment platforms inherently provide isolation—if your local security tools fail, payment processing can shift entirely to cloud infrastructure running different security stacks. The key is ensuring payment operations don't have hard dependencies on any single third-party tool, including security software.

8. What automated data validation steps can eliminate manual entry errors in payment processing?

Implement real-time IBAN validation using checksum algorithms before form submission. Deploy address verification services (AVS) that validate billing addresses against card issuer records. Use SWIFT BIC validation libraries that check bank identifier codes against official registries. Implement recipient name matching that flags mismatches between account holder names and beneficiary names. Deploy machine learning models trained on historical payment data to identify anomalous patterns suggesting data entry errors. Use autocomplete with verified data sources for common fields like bank names and routing numbers.

9. We're a small retail business—after seeing the UK payment processor outage that cost £1.6B, what can payment terminal vendors do to prevent us from losing a full day's revenue?

Modern payment terminal vendors should provide multi-acquirer support built directly into their hardware and software, allowing automatic failover between payment processors.  Technology partners like Crafting Software specialize in implementing multi-acquirer architectures where your payment acceptance infrastructure connects to multiple processors (Stripe, Adyen, Worldpay, etc.) simultaneously. When the system detects transaction failures from your primary processor—like the September 2024 UK outage—it automatically retries through secondary processors without any action required from you or your staff. These solutions include intelligent routing algorithms that continuously monitor processor health and success rates, shifting traffic away from degraded providers before customers experience declined transactions. For point-of-sale systems, vendors like Crafting Software integrate cellular/mobile network failover so if your primary internet connection fails, transactions seamlessly route through 4G/5G networks. The platform also implements local transaction queuing—if all processors are temporarily unavailable, transaction data stores securely on-premise and automatically processes once connectivity restores, preventing complete revenue loss. Working with specialized payment infrastructure providers who offer 24/7 monitoring with proactive alerts via SMS, email, or Slack the moment processor issues are detected, plus real-time dashboards showing processor health, allows small businesses to access enterprise-grade redundancy that would be cost-prohibitive to build in-house. Crafting Software's approach focuses on making this vendor-level orchestration accessible and affordable for small retailers who need protection but lack the resources for complex internal systems.

10. How do we balance fraud prevention sophistication with minimizing false declines?

Implement risk-based authentication that applies stronger verification only for transactions showing genuine risk signals rather than blanket rules. Use machine learning models that consider 100+ data points (device fingerprinting, behavioral biometrics, transaction history, geographic patterns) rather than simple rule-based filters. Deploy dynamic friction where low-risk transactions proceed instantly while borderline cases trigger step-up authentication (3DS, SMS verification) rather than automatic decline. Maintain continuous feedback loops where declined transactions marked as false positives retrain your models. Consider outsourcing to specialized fraud prevention platforms that aggregate data across thousands of merchants, providing better fraud detection than any single merchant can achieve independently. Regularly audit decline reasons and challenge assumptions embedded in rules that may no longer reflect current customer behavior patterns.