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Perché i pagamenti falliscono e come i commercianti possono prevenirli

Perché i pagamenti falliscono e come i commercianti possono prevenirli

Oliver Jake
da 
Oliver Jake
11 minuti di lettura
Blog
Settembre 09, 2025

Validate every transaction at the gateway level using apis that carry risk signals, device data, and historic patterns. This upfront check reduces the impact of failed attempts on customer journeys for both buyers and merchants. When you catch a misrouted request early, you avoid unnecessary charges and support calls, and this made the checkout faster for goods and services alike.

Map the payment path into clearly defined journeys, and trace bottlenecks into routing, consent, and settlement, including slow API calls, mismatched timeouts, and insufficient error details. These bottlenecks affect both sides and increase costs. Create an alternative path when a primary API fails, so the request doesn’t stall the customer journey. Use apis to integrate instrument verification, risk checks, and reconciliation.

Quantify the cost impact: each failed transaction triggers retries, refunds, and potentially disputes, including the cost of customer churn, the total adds up quickly. Track costs across merchant, processor, and issuer, and surface a single dashboard that shows bottlenecks and the effect on goods delivered. This helps you decide where to invest in those improvements.

Choose a modular integration strategy that supports iterative improvements. Provide enough observability by logging status codes, error messages, and retry counts. A single request payload that carries the full path helps them reproduce failures quickly, and developers across parties can correlate issues faster. This can be done here and now with a consolidated routing approach and parallel calls where appropriate. Even small tweaks to timeouts and retry backoffs cut overall failure rates.

Put in place a rapid recovery plan: if a payment fails, present a consumer-friendly message, offer an alternative payment method, and queue a secure retry that respects the user’s preferences. Include clear information for parties about next steps so they can act without delay. This keeps the goods moving and lowers costs and churn, including refunds and chargebacks that might otherwise accumulate.

Diagnose Declines by Root Cause: Issuer, Network, and Merchant Factors

Diagnose Declines by Root Cause: Issuer, Network, and Merchant Factors

Classify declines by root cause and implement automated triage that flags issuer, network, and merchant issues. Build a slate of checks that map decline codes to root causes so you see failure reasons fast. Track daily metrics such as approval rate, average decision time, and failure reasons by providers to guide corrective actions.

Issuer declines typically account for the largest share of failures. Typical causes include insufficient funds, risk flags on new cards, card status holds, and stale AVS data. Map issuer response codes to actions and automate retries or additional verification to keep customers moving–be sure every retry follows policy and is logged for audit.

Network declines arise from misrouting, 3DS friction, or gateway congestion. Monitor network latency, bounce codes, and session identifiers. Build seamlessly integrated fallbacks that route to backup providers to minimize downtime and keep the experience fast for the user.

Merchant factors include code glitches, misconfigurations in applications, incorrect currency or price, tokenization mismatches, or BIN mapping errors. Integrating checks directly into your code and applications helps catch issues before production, and using robust methods like validation hooks reduces risk.

Set up a troubleshooting workflow that yields actionable insights: maintain a slate of checks tied to root causes; log response codes and reasons; alert teams with timely notifications; review declines by provider, credit network, and merchant account every week.

Adopt actionable methods to prevent glitches and minimize risk: configure risk thresholds, implement fast retry logic across multiple providers, and keep production code lean with feature flags. Focus on leading indicators such as rising rejection rates by issuer or by network and respond before customers notice.

Make data-driven decisions: align product teams on the maximum acceptable failure rate; provide clear approval messaging; use a single source of truth for decline reasons; ensure notifications reach the right stakeholders in time.

With root-cause diagnosis, merchants can tighten checks, improve the customer experience, and maintain compliance while preventing repetitive declines that disrupt every checkout.

Validate Transactions with AVS, CVC, and Address Checks

Enable AVS, CVC, and address checks on every transaction and tailor thresholds by country and risk tier to reduce rejects and protect the business. This approach delivers value by catching mismatches before funds move, helping your service scale while keeping the user journeys smooth for many customers.

AVS compares the billing address provided by the user with the issuer’s records; CVC verifies the card’s verification code; address checks add a third safeguard. When these checks align, you significantly lower fraud risk and maintain efficient checkout. If a check fails or returns pending, decide whether to reject, prompt for data correction via a form-based flow, or route to quick manual review depending on risk and order value. There are scenarios where you should reject immediately or route to verification to protect margins and avoid loss on high-value card-present and card-not-present orders.

Layered checks that adapt to risk

Combine AVS and CVC with device signals and historical risk data to create a risk score that scales with value. For low-risk orders, auto-approve on a clean match; for mid-risk cases, require a quick form-based confirmation; for high-risk or pending results, escalate to issuer verification or human review. This approach avoids unnecessary friction for trusted users and lowers rejects across multiple markets, while providing visibility into which journeys need attention. Chrome users, mobile devices, and desktop environments benefit when address data is captured accurately at form entry and autofill is handled carefully, reducing form-based errors and rework.

Monitor, optimize, and protect users

Track AVS/CVC pass rates, pending statuses, and the share of orders requiring verification. Use a single dashboard to compare issuers and card types, and adjust thresholds around risk appetite and merchant category. Ensure users see clear, actionable messages when data is incomplete, and require additional verification only when risk is high. This practice keeps many orders progressing and avoids blocking legitimate transactions, supporting a reliable service for your users and protecting revenue against fraud attempts.

Implement Adaptive 3DS and Risk-Based Authentication

Enable adaptive 3DS and risk-based authentication as a core service by enabling configurations that distinguish low-risk recurring payments from high-risk orders. For low-risk cases, allow frictionless 3DS2 flows so payments submit flawlessly, using device fingerprinting and merchant risk indicators. Such a setup keeps costs down and increases authorization success rate, while still requiring strong identification where needed. The system must be able to adapt to location signals and other risk flags and adjust prompts accordingly.

Build with a risk-based scoring model and a test plan. Use examples, including device fingerprints, IP reputation, velocity checks, and account-level risk indicators to categorize each order. Design workflows that respond to inconsistent signals by requesting additional identification only when a real risk is detected. When signals are clear, allow submission to the issuer with minimal friction; if not, sending extra data and prompts to the user helps verification. We dont rely on a single signal; instead combine location, device, and behavioral signals to improve accuracy. Use merchant-facing tools to monitor outcomes and adjust rules over time and run end-to-end tests to validate changes.

Adaptive 3DS workflow

Define the prompts for high-risk actions, like issuer challenges and step-up authentication, while enabling a frictionless flow for trusted customers. Use such signals as device integrity, location consistency, and user behavior to decide whether to show a challenge or not. Ensure the merchant can submit necessary data to the issuer and payment network to complete the flow end-to-end.

Risk-based scoring and thresholds

Establish a governance cadence with regular end-to-end test cycles and live monitoring. Review false positive rates by location and merchant category, and tune configurations to reduce friction for recurring customers. Provide clear customer messaging if a challenge occurs and offer self-service options to submit verification data when needed.

Use Intelligent Payment Routing to Maximize Approvals

Enable multi-issuer routing with a technical decision engine that analyzes real-time signals and selects the best path for every transaction. In your environment, configure integrations to try the primary network first, then switch to backups such as paypal or alternate issuer routes when an authorization is declined or a card is expired, to ensure a prompt fallback that keeps the checkout moving.

Collect and act on precise decline data: store codes, reason codes, and issuer responses, then adapt routing rules accordingly. Communicate success profiles to your product team and adjust offers to match issuer capabilities, providing actionable retries without triggering abandonment. Maintain a flexible rule set that balances risk, speed, and approval likelihood, which is inevitable when you are making these adjustments.

To maximize approvals, route through multiple networks and add options like paypal as a fallback offer, providing a smoother path for cards that fail on primary routes. Show merchants that a single failure does not mean abandonment; offering a seamless switch reduces friction and increases average order value across the world.

Track metrics: lift in approvals, rate of authorization success, and changes in abandonment. Showing tangible results helps justify ongoing routing experiments. In our data, merchants see a 5-12% rise in approvals with intelligent routing and a 3-7% drop in abandonment after enabling prompt, cross-network checks. Provide ongoing communication with issuers via secure integrations, improving the odds of credible authorization, and lowering unnecessary retry attempts caused by expired credentials or codes. This approach makes the business more resilient and scalable.

Tune Fraud Rules for Precision: Minimize Blocking Legitimate Buyers

Apply dynamic risk scoring at checkout, as youre deploying updated rules across the gateway to scale precision without harming shoppers.

Practical rule tuning

  • Define risk bands by gateway and country; low-risk orders flow fast, high-risk orders trigger review.
  • Use address, cardholder, account, and contact signals to confirm identity; if mismatch, require code-based verification instead of an outright decline.
  • Enable fallbacks for suspected orders: automatic or manual review with a defined time window and a clear contact path for rapid resolution.
  • Informa gli acquirenti quando si verifica un blocco e offri un percorso semplice per la verifica; includi un tempo stimato per la decisione.
  • Non blocca le transazioni legittime dei titolari di carta utilizzando un percorso di rifiuto soft e offrendo metodi di pagamento alternativi.
  • Combatti i segnali di frode utilizzando controlli multi-fattore ed evitando l'overfitting a una singola regola; applica l'autenticazione a due fattori (2FA) ove appropriato.
  • Monitoraggio proattivo: i modelli di incontro attivano avvisi in tempo reale e regolazioni automatiche delle soglie per prevenire blocchi a raffica.
  • Allineamento delle parti: mantenere un unico punto di contatto tra i team di rischio, supporto e ingegneria per gli aggiornamenti delle regole.
  • Integrazione: assicurati che la logica delle regole fluisca attraverso la tua piattaforma di shopping, app e gateway con risultati coerenti.
  • Applica le regole al momento del checkout e progetta percorsi di codice veloci per approvare gli ordini a basso rischio e trattenere solo quelli ad alto rischio fino alla verifica.
  • Quando applichi nuovi controlli, fornisci un percorso rapido di verifica per ridurre al minimo l'attrito per gli acquirenti legittimi.

Measurement and governance

  1. Traccia i falsi positivi, il valore bloccato e il tempo necessario per la decisione; aggiorna le dashboard settimanalmente man mano che le regole si evolvono dal cambiamento.
  2. Definisci obiettivi come la riduzione dei rifiuti legittimi a meno dell'11% degli ordini e il mantenimento del tempo medio di revisione al di sotto dei 20 minuti.
  3. Esegui dei test in modalità shadow prima di applicare le modifiche al traffico live; confronta i risultati con la baseline.
  4. Documenta le modifiche e pubblica il codice aggiornato e i valori dei parametri per mantenere la coerenza dei contenuti tra i team.
  5. Esaminare le tendenze degli incontri con le parti in ambito rischio, supporto e ingegneria per affinare le soglie.

Migliora l'UX del checkout e fornisci messaggi di riprova chiari

Migliora l'UX del checkout e fornisci messaggi di riprova chiari

Implementa la messaggistica di riprova in tempo reale che spieghi al titolare dell'account perché un pagamento non è andato a buon fine e lo guidi verso un'opzione alternativa per completare la vendita. Utilizza un testo conciso e amichevole come: "Il tuo pagamento non è andato a buon fine: riprova o seleziona un metodo diverso". Questo mantiene i clienti abituali in movimento senza intoppi e ti aiuta a recuperare rapidamente una vendita. Sono in grado di agire senza uscire dal flusso di checkout.

Guida che riduce l'attrito

Mostra lievi cali con un chiaro passo successivo. Visualizza l'importo, le ultime quattro cifre e il prodotto, quindi fornisci opzioni come riprova, salva per dopo o un metodo di pagamento alternativo. Includi notifiche su tutti i canali in modo che rimangano informati in tempo reale e assicurati che i tuoi sistemi tengano traccia dei progressi dal tentativo al completamento. Possono vedere lo stato nel loro account e sentirsi sicuri del percorso di ripristino. Questo approccio aiuta a prevenire l'abbandono del carrello e migliora la sicurezza durante il flusso di riprova.

Implementazione e metriche su cui puoi agire

Limita il numero di tentativi all'interno di una finestra regolare per evitare colli di bottiglia; mantieni l'esperienza ricca di contesto e con una guida sufficiente per completare l'acquisto. Quando un cliente ritorna, dovrebbe vedere un percorso di completamento coerente, con i dettagli del prodotto e uno stato visibile in modo che la vendita possa essere completata senza attriti. Tracciare i risultati in tempo reale attraverso l'account, il metodo di pagamento e il dispositivo ti aiuta a identificare le sfide e ad aggiustare il flusso.

Strategia What to do Metrica chiave Note
Messaggistica di ripetizione in tempo reale Spiega il fallimento, offri la possibilità di riprovare e opzioni alternative Tempo per il tentativo, conversione dopo il tentativo Mantieni le copie specifiche per il prodotto e l'account; evita confusione
Calo lieve con opzioni Etichetta come soft, mostra last4, importo e azioni suggerite Tasso di completamento al tentativo successivo, tasso di abbandono Limitare a un numero ragionevole di scelte per evitare di sopraffare
Notifiche attraverso i canali Invia banner in-app, email o notifiche push con link di riprova Tasso di apertura delle notifiche, completamento del follow-up Garantire la sicurezza e la privacy in tutti i canali
Limiti di frequenza e cadenza Limita i tentativi per finestra; mantieni una cadenza regolare Tasso di abbandono, tasso di completamento Regola la cadenza in base al valore di vendita e al rischio
Monitoraggio e analisi in tempo reale Sincronizza lo stato tra i sistemi per riflettere il fatto che sia completato, in sospeso o fallito Precisione in tempo reale, time-to-resolution Usa i dati per ottimizzare il flusso e prevenire futuri errori

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