Enterprise Cross-Border Payment Optimization Platform
Orchestrates 5 specialized AI agents using Saga Pattern architecture with circuit breaker protection. Agents execute in parallel phases: Phase 1 runs compliance and fraud screening simultaneously, Phase 2 optimizes FX timing and payment routes in parallel, Phase 3 performs Monte Carlo risk analysis.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agent Orchestration Processing - Multi-agent pipeline at 40% progress showing Compliance Sentinel and Fraud Hunter agents completing analysis with 98.7% and 98.2% accuracy, live decision stream displaying real-time payment flagging and FX optimization recommendations
AI Decision Review with Explainable AI - XAI decision review interface displaying 60 approved, 38 flagged, and 2 blocked payments with $1,594,393 total savings, transparent confidence scoring and payment-level decision details for human oversight
Batch Processing Complete Results - Final pipeline results showing 60% success rate with 60 of 100 payments approved for execution, $1,594,393 cost savings achieved through intelligent FX timing and optimal routing in just 4.8 seconds processing time
Enterprise Payment Orchestration Input - Batch configuration showing 100 payments totaling $81.5M across 12 currencies and 13 countries, 6 specialized AI agents ready for deployment with estimated savings of $978K-$2.28M
AI Agents
Specialized autonomous agents working in coordination
Compliance Screening Agent
Manual sanctions screening against OFAC, EU, and UN lists is slow and error-prone. False positives waste investigator time while false negatives create regulatory risk. Name variations, aliases, and transliterations make exact matching insufficient.
Core Logic
Implements Bayesian Inference combined with Levenshtein Distance for fuzzy name matching with 85% similarity threshold. Calculates false positive probability using contextual factors (country, DOB, address matching). Screens payments against OFAC SDN, EU Consolidated, and UN Security Council sanctions lists. Time complexity O(N x M x LĀ²) with 98.7% screening accuracy and 2.3% false positive rate. Processes 3,205 screenings/second.
Fraud Detection Agent
Traditional rule-based fraud detection misses novel fraud patterns and generates excessive false positives. Detecting anomalies across transaction velocity, amounts, timing, and geography requires sophisticated pattern recognition beyond human capability at scale.
Core Logic
Employs Isolation Forest ensemble anomaly detection with 100 trees and 256 subsample size. Analyzes behavioral patterns including amount deviation from vendor baseline, 24-hour transaction velocity, new country/currency flags, unusual timing, and high-risk jurisdiction indicators. Time complexity O(T x Ļ x log Ļ). Achieves 98.2% detection accuracy with 1.8% false positive rate and 3% contamination rate.
FX Prediction Agent
Executing foreign exchange at suboptimal rates costs organizations significant money on cross-border payments. Treasury teams lack real-time predictive capabilities to determine whether to execute immediately or wait for better rates.
Core Logic
Implements ARIMA(2,1,1) time series forecasting combined with technical indicators including RSI, MACD, and Bollinger Bands. Provides 90-minute prediction horizon with execute-now, delay, or wait recommendations based on rate favorability and volatility. Uses LRU cache achieving 94.3% hit rate. Delivers average savings of $487K per batch with 89% prediction accuracy.
Route Optimization Agent
Payment routing decisions involve complex trade-offs between cost, speed, and reliability across SEPA, SWIFT, ACH, and local payment corridors. Manual route selection often defaults to expensive SWIFT transfers when cheaper alternatives exist.
Core Logic
Applies Multi-Criteria Dijkstra's Shortest Path algorithm with weighted scoring: cost (50%), speed (30%), reliability (20%). Evaluates SEPA (ā¬0, 24h), SEPA Instant (ā¬0.50, <1min), SWIFT ($25-40, 48h), ACH ($0.25, 48h), and Local ($0.30-1.50, 24h) corridors. Time complexity O(E + R log R). Achieves 96% optimization accuracy with $182K average savings per batch across 65+ countries.
Risk Analysis Agent
Treasury teams lack visibility into portfolio-level currency exposure risk across large payment batches. Without quantitative risk metrics like Value-at-Risk, organizations cannot make informed hedging decisions or identify concentration risks.
Core Logic
Executes Monte Carlo Value-at-Risk simulation with 10,000 scenarios using Geometric Brownian Motion for FX rate modeling. Applies Box-Muller transform for normal distribution generation. Calculates VaR at 95% and 99% confidence levels, identifies concentration risk by vendor/country/currency, and generates hedging recommendations. Time complexity O(N x M) with 40,486 scenarios/second throughput and 94% accuracy.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization