Payment Failures & Routing

Payment teams should never operate without a profound understanding of where failures originate, which providers underperform for specific transaction profiles, and how much revenue is lost to preventable failures. Failure patterns span issuers, geographies, card networks, payout rails, and routing decisions simultaneously — making root cause identification difficult without structured analysis. Without visibility into PSP performance gaps, retry effectiveness, and recoverable revenue, optimisation decisions are based on incomplete data and routing changes go unvalidated.

This solution identifies where payment failures occur across acquiring, payout, and transfer flows, quantifies the revenue impact, and supports with routing logic & retry strategies needed to recover it.

WHAT DOES IT SOLVE?

Failure Trends & SLA Monitoring

• Answers: How are failure rates performing, where are the spikes, and are providers meeting contracted commitments?

• Contains: Key failure metrics and benchmarks by transaction type and payment rail, trend analysis, anomaly detection, automated alerts when rates exceed thresholds, early warning indicators, SLA definition and automated compliance tracking by provider, breach documentation and reporting, performance data package for contract renewal and accountability discussions

Failure Root Causes & Patterns

• Answers: Why are payments failing, which patterns should be prioritised, and how does performance vary by value and timing?

• Contains: Failure reason categorisation and breakdown, multi-dimensional pattern detection across geography, network, issuer, card type, payment rail, transaction value tier and time, actionable insight surfacing for remediation, peak period degradation identification, volume concentration analysis

Provider Performance Comparison

• Answers: Which provider performs best for specific transaction profiles across geographies, networks and issuers, and where are avoidable failures concentrated

• Contains: Success rate comparison across PSPs and payout providers by geography, card type, transaction size, card network and issuer, failure reason breakdown by provider, BIN-level performance for high-volume issuers, cross-border vs domestic processing patterns, processing latency benchmarks, performance trends over time

Recoverable Revenue Analysis

• Answers: How much revenue is being lost to preventable failures and provider performance gaps, and what is the financial opportunity?

• Contains: Identification of false decline and preventable failure patterns, retry success rate analysis, estimated recoverable revenue by failure type, actual vs potential success rates by provider, financial impact modelling, opportunity prioritisation by impact

Fraud Rules Impact Assessment

• Answers: Are fraud prevention rules causing unnecessary legitimate failures, and how should thresholds be optimised?

• Contains: Fraud rule impact on legitimate failure rates, rule-specific false positive identification, velocity controls analysis

Failure-Driven Churn Analysis

• Answers: How do payment failures trigger customer churn, and which failure types put high-value customers at risk?

• Contains: Churn pattern analysis following payment failures, identification of failure types and volumes that trigger churn, customer LTV at risk quantification

CORE MODULES

Descriptive & Diagnostic

Routing Optimisation

Answers: What routing rules should be implemented to maximise net revenue, balancing success rates against processing costs?
Contains: Combined success rate and processing cost analysis by provider, geography and transaction profile, optimal provider selection logic by geography, transaction type and value tier, cascade routing recommendations, expected net revenue capture modelling, implementation specifications, statistical A/B testing framework, rollout and rollback decision criteria

Retry Strategy Optimisation

Answers: When and how should failed transactions be retried across issuers and providers to maximise recovery?
Contains: Decision model for optimal timing and sequencing, predicted retry success probability by bank, identification of issuers where retries are viable vs futile, cross-provider retry strategies, hard vs soft decline treatment logic

Account Updater ROI Model

Answers: Should account updater services be invested in, and what is the expected return? Contains: Card expiration and update pattern analysis, cost-benefit analysis of account updater services, expected failure reduction from implementation, vendor comparison and selection criteria

ML-Based Failure Prediction

Answers: Which transactions are likely to fail before processing, and how can intervention be made proactively?
Contains: Machine learning model predicting failure probability, risk scoring for transactions, early warning for at-risk

ADVANCED MODULES

Predictive & Prescriptive

DELIVERABLES

Built in PowerBI, Tableau, or Looker & adhering to client's brand book

Dedicated tab per analysis plus executive summary overview

AI-generated insights and recommended actions per analysis

SQL queries built in client's database system with controlled access

Python scripts for statistical and ML models (if applicable)

Added to client's GitHub repository, or delivered as standalone package

Technical Guide: Data sources, logic, formulas & maintenance procedures

Analysis Handbook: Metric definitions, interpretation, use cases & action framework

Dashboard

Code Base

Documentation

Knowledge Transfer

Live & Recorded walkthrough of dashboard functionality and insights

Q&A session covering methodology, use cases, and recommendations

30-day post-delivery support for questions and adjustments

MAIN REQUIREMENTS

• Transaction and operational data must be accessible in a relational database

• BI Platform Subscription with data base gateway for dashboard automation.

• Relevant APIs & ETL workflows should be functional and consistent.

Data Infrastructure*

• Transaction & Authorization Data All payment attempts with authorization outcomes, decline codes & retry history

• Payment Instrument Data - Card BIN, type/network, and issuing bank details

• Customer & Account Data - Customer segments, location, and transaction history

• PSP & Routing Data - Payment routing logic and fee structures (if using multiple PSPs)

Data Sets

*Data infrastructure set up is out of scope. It can be provided as a separate engagement.