Intelligent Payment Recovery System
Deploys 11 specialized AI agents using A2A Protocol v2.0 that analyze failures in parallel, predict optimal retry timing using ML, update payment methods automatically, ensure PSD3/DORA compliance, and communicate with customers through sentiment-aware personalized messaging.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI-Powered Payment Recovery dashboard displaying payment failure case selection with customer details, 11 AI agents activation panel, and multi-stage workflow orchestration for intelligent failure resolution.
Agent Orchestration view showing real-time multi-agent analysis with 11 AI agents processing payment failure, displaying tool invocations timeline, inter-agent messaging, and working memory with case context.
AI Analysis Results screen presenting comprehensive findings including root cause diagnosis, customer behavioral profile, compliance assessment, fraud evaluation, optimal retry timing prediction, sentiment analysis, and ML model pipeline metrics.
Payment Successfully Recovered outcome dashboard showing execution timeline, key agent decisions from all 11 specialists, data summary, and AI vs Manual Recovery comparison demonstrating 91% success rate versus 23% manual rate.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex payment recovery requires coordinating multiple specialized analyses simultaneously while synthesizing results into actionable recovery strategies.
Core Logic
Powered by GPT-4, coordinates the entire recovery workflow across all 11 specialist agents using A2A Protocol v2.0. Analyzes incoming failures, activates relevant specialists in parallel, synthesizes their recommendations, and makes final recovery decisions with confidence scoring. Manages workflow phases from initial detection through execution and learning.
Diagnostic Agent
Payment failures have diverse root causes (insufficient funds, expired cards, fraud, bank issues) that require expert-level analysis to properly classify and address.
Core Logic
Uses GPT-4o with fine-tuned XGBoost ensemble to parse ISO 8583 failure codes, classify root causes across 30+ categories, analyze customer payment history, detect fraud versus legitimate failures, and cross-reference bank system status. Provides confidence percentages (e.g., 89% confidence temporary insufficient funds) with detailed evidence.
Timing Predictor Agent
Retry attempts at wrong times fail repeatedly. Success depends on customer-specific factors like salary deposit patterns and bank processing windows that vary per individual.
Core Logic
Leverages GPT-4o with Prophet forecasting to analyze 36+ months of customer payment patterns, correlate with salary deposit cycles, factor bank processing windows, and calculate probability curves. Recommends specific time windows with success probabilities (e.g., 'Tomorrow 10 AM - 2 PM with 91% success chance').
Payment Method Agent
Expired or invalid payment methods cause recurring failures. Customers often have alternative payment options but manual updates create friction and delays.
Core Logic
Integrates with Card Updater APIs (Visa/Mastercard networks) and Open Banking services (PIS/AIS). Automatically checks card expiration, queries updater networks for new card numbers, identifies linked alternative methods (Bizum, Apple Pay, Google Pay), and auto-updates expired cards where authorized.
Compliance Agent
Payment recovery actions must comply with complex EU regulations (PSD2/PSD3, DORA, AML6, GDPR) that vary by transaction type and customer jurisdiction.
Core Logic
Validates all recovery actions against current regulatory requirements in real-time. Checks PSD2/PSD3 compliance, DORA operational resilience requirements, AML6 transaction monitoring thresholds, and determines SCA (Strong Customer Authentication) requirements with exemption eligibility analysis.
Fraud Detection Agent
Legitimate payment failures must be distinguished from fraudulent activity. False positives block valid customers while false negatives enable fraud.
Core Logic
Employs XGBoost with behavioral pattern recognition to perform real-time fraud assessment. Analyzes behavioral biometrics, velocity patterns, geolocation anomalies, device fingerprinting, network connections, and synthetic ID indicators. Returns fraud scores with signal breakdown and severity levels.
Sentiment Agent
Customer emotional state and churn risk vary significantly. Generic recovery communications can frustrate already-unhappy customers and accelerate cancellation.
Core Logic
Uses NLP sentiment analysis with churn prediction models to analyze customer sentiment across all interactions. Measures frustration, satisfaction, urgency, and loyalty scores. Identifies customer lifetime value tier (Bronze/Silver/Gold/Platinum) and recommends appropriate communication tone and urgency level.
Communication Agent
Recovery communications need personalization, optimal timing, and appropriate channel selection to maximize response rates without appearing intrusive.
Core Logic
Powered by GPT-4 with communication templates to draft personalized failure notifications, optimize message timing based on customer timezone and preferences, select optimal channel (email, SMS, in-app), generate secure payment update links, and track communication history for follow-up optimization.
Regulatory Agent
Cross-border transactions in Spain, Portugal, and EU face jurisdiction-specific requirements that change frequently and vary by transaction characteristics.
Core Logic
Manages jurisdiction-specific compliance for cross-border payment recovery. Determines applicable regulations by transaction origin, assesses transaction risk levels, identifies regulatory flags, and ensures SCA requirements are properly applied based on customer location and transaction type.
Risk Assessment Agent
Recovery decisions impact revenue, churn risk, and customer relationships. Quantifying these trade-offs requires sophisticated risk modeling.
Core Logic
Performs Monte Carlo simulations to calculate churn risk with and without intervention, estimate revenue at risk, assess network effect risk (negative word-of-mouth potential), and generate probability-based recovery forecasts with confidence intervals for executive decision support.
Learning Agent
Recovery strategies need continuous improvement based on actual outcomes. Manual analysis cannot scale to millions of transactions.
Core Logic
Implements federated learning for privacy-preserving model improvement. Records outcomes from each recovery case, updates ML models based on success/failure patterns, optimizes recovery strategies over time, and shares learnings across the agent network while maintaining data privacy.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
9 technologies
Architecture Diagram
System flow visualization