Fraud Detection · Risk Management
BORICA needed to modernize national fraud prevention so it could stop fast, cross-channel attacks in milliseconds without disrupting legitimate payment traffic across multiple banks and service providers.
Reported impact
BORICA now operates a centralized fraud platform that protects most domestic card traffic in real time, scales to high transaction volumes and improves resilience across the national payment ecosystem.
BORICA partnered with IBM and IBS to replace fragmented, batch-oriented fraud controls with a centralized, real-time decisioning platform built on IBM Safer Payments. The solution introduced a multi-tenant architecture that serves participating banks and payment service providers from a shared national platform rather than requiring separate local controls. IBM Safer Payments uses in-memory processing, machine-learning-driven detection and horizontal scalability to score card and digital-payment activity as it happens, while IBS integrated the platform directly into BORICA’s payment workflows. This changed fraud handling from slow rules and delayed review into behavioral, real-time monitoring that can cover card transactions, AML checks and new channels such as instant payments. The architecture gives BORICA national-scale protection, consistent controls across institutions and room to extend the model as transaction volumes and attack patterns evolve.
Payment events flow from participating banks and PSPs into IBM Safer Payments, which scores activity in memory and returns fraud and AML decisions directly into BORICA’s shared payment workflows in real time.
BORICA partnered with IBM and implementation partner IBS to modernize its national fraud prevention architecture.
The system processes 4,000+ transactions per second with sub-10 millisecond latency.
Together, they deployed IBM Safer Payments as a centralized, multi-tenant platform replacing legacy batch-based systems with real-time behavioral risk scoring.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
In-house Sample Sep 24, 2026 Mastercard Fraudsters are using AI to find vulnerabilities, adapt tactics, and scale attacks across more payment channels, while traditional transaction-by-transaction controls struggle to catch coordinated fraud and authorized… Last year, scam losses globally were estimated at $442 billion 
NVIDIA Sample Jun 2, 2026 Revolut Revolut needed to replace siloed fraud, credit, marketing, and recommendation models that depended on months of feature engineering with a single behavioral foundation model that could learn from raw financial event… 16.1% improvement in credit risk prediction performance from NVIDIA Llama-Nemotron-Embed-1B-v2 NVIDIA H100 GPUs · NVIDIA Llama-Nemotron-Embed-1B-v2 · Nebius AI Cloud NVIDIA Sample Mar 17, 2026 Mastercard Mastercard wants to turn its transaction data into a reusable AI insights engine for payments and commerce, especially to strengthen cybersecurity and fraud detection, improve personalization and loyalty use cases, and… billions of transactions Databricks · Nvidia's advanced accelerated computing platform · large tabular model (LTM) 
