Payment Optimization Engine

The client is the global digital-payments two-sided network: hundreds of millions of active accounts across roughly two hundred markets, with trillions in total payment volume and tens of billions of payment transactions annually. The global payments company owns their payment processing platform, the P2P platform, their international money transfer service, the guest checkout product, their digital currency, and a BNPL business at tens of billions in 2024 BNPL volume. Taller's engagement at this organization is the largest in the portfolio. Six master-deck case studies cover the engagement across the P2P platform, the global payments company, and their payment processing platform deliverables. The four cases below address the global payments company-specific case studies the dossier proposed.

The global payments company's authorization optimization stack — vault freshness via network tokenization and account updaters, predictive decline ML, smart retries, machine-learned routing, direct network reach — requires multiple foundational platform primitives delivered against the global payments company's broader optimization engine, plus ongoing engineering capacity in the Credit Platform team and the ML routing surface.

Taller's contribution spans five delivered foundational platform primitives plus same-client contributor capacity. The Apache Fineract SOR is the foundational ledger primitive for balance, credit, and their BNPL product. The gRPC API edge is the latency primitive directly relevant to smart-retry round-trip economics. The Post-Purchase Notifications component integrates with the global payments company's AI-powered cashback and Smart Receipts and includes the C++-to-Java migration onto the latest their internal API framework. The payment processing platform modernization is the processor surface where optimizations are realized: Ruby on Rails stabilization, refactoring, and the AWS migration with automated CI/CD. The Unified Native Onboarding platform delivers the network-tokenization and account-updater integration primitives. Around those, Taller engineers contribute to the global payments company's Credit Platform Java team and the Machine Learning Engineer roles supporting predictive-decline and smart-routing ML.

Across the engagement: the SOR reduced operational costs, faster configuration changes, and enabled global product expansion; the gRPC implementation improved user experience and reduced infrastructure costs; the Post-Purchase Notifications shipped AI-powered personalized notifications and modernized the legacy codebase; their payment processing platform modernization produced thirty percent faster integration, forty percent reduction in deployment times, and zero downtime during migration; the Unified Native Onboarding consolidated thirty-plus flows into one platform.

The optimization outcomes are substantial: hundreds of millions of cards tokenized across global markets, roughly 100 bps issuer-decline-rate reduction in the global payments company's wallet, 60–240 bps authorization-rate improvement for certain merchants when predictive decline modeling pairs with tailored UX, an additional 30 bps from smart retries on certain domestic U.S. token transactions, card authorization rates up to five percentage points above market averages for the largest enterprises globally, and a 5.4 percent approval-rate increase in one published case. These are the global payments company optimization outcomes earned at this company scale across the optimization engine the global payments company architects. Basis-point movement at trillions in TPV translates into very large merchant revenue capture; that capture is the global payments company's. Taller's contribution is the five foundational platform primitives across the authorization stack plus contributor capacity into the Credit Platform and ML engineering functions.