--- title: "Banking Hub" description: "Real-time fraud detection, payments, AML monitoring, and regulatory reporting for banks. Powered by Apache Flink with sub-10ms latency." lastUpdated: 2026-10-14T09:41:38.000Z source_url: html: "https://www.ververica.com/banking" md: "https://www.ververica.com/banking.md" --- # Real-Time Stream Processing Built for Banking. Fraud detection in under 10ms. Instant payments at 6.9B records/sec. Continuous AML monitoring, risk management, and regulatory reporting. All on one platform, built by the creators of Apache Flink. - **<10ms — Sub-10ms latency** - **6.9B — records/sec** - **DORA — Built for DORA requirements** ## How do banks use stream processing? Stream processing for banking means processing every transaction, payment and market event the moment it arrives, instead of in overnight batches. Banks use Apache Flink on Ververica for fraud detection, instant payments, AML monitoring, risk management and regulatory reporting, with exactly-once processing so no transaction is lost or counted twice. ## Trusted in Production ## Batch Processing Is a Liability In Modern Banking The Problem ### Fraud settles before jobs finish. AML alerts arrive 24 hours late. Regulatory reports miss the deadline. Customers receive yesterday's offers. ### The financial cost. Billions in undetected fraud, millions in compliance fines, irreversible customer attrition. ### Built for a slower world. Batch was built for a slower world. Banking moved on. Your infrastructure did not. ## Banking Solutions ### Fraud Detection Score 2B+ transactions per day. Block fraud in under 150ms. ### Real-Time Payments Instant settlement. Exactly-once processing. Peak-hour resilience. ### AML Monitoring Continuous monitoring. 60% fewer false positives. Adaptive thresholds. ### Risk Management Real-time VaR. Counterparty exposure. Continuous stress testing. ### Regulatory Reporting T+0 reporting. Automated validation. Immutable audit trail. ### Core Modernization Strangler fig migration. Zero downtime. Dual-run validation. ### Customer Personalization Act on customer behavior as it occurs. Serve the right offer in under 50ms. ### Mainframe Offloading Cut MIPS costs by 40% or more without replacing the mainframe. ### Fintech Monitoring Real-time portfolio risk. Instant wealth management signals. ## Why Banks Run On Ververica ### Sub-10ms Latency Fraud, payments, and risk decisions execute in under 10 milliseconds. Not aspirational. Measured in production across tier-1 banks. ### 6.9B Records/Sec Throughput The VERA engine processes 6.9 billion records per second. Peak volumes during market events and payment surges do not degrade performance. ### 2x Faster Than Open-Source Flink VERA engine delivers double the throughput of standard Apache Flink at 52% lower resource consumption. Built by the team that created Flink. ### 40% Lower TCO Measured total cost of ownership reduction versus legacy stream processing stacks. Fewer clusters. Less operational overhead. Same workloads. ### Exactly-Once Processing Financial transactions demand zero data loss and zero duplicates. Ververica guarantees exactly-once semantics at scale. No exceptions. ### Multi-Cloud Deployment Deploy on AWS, Azure, GCP, or on-premises. BYOC mode keeps all data in your environment. Engineered in Europe. Sovereign by design. _CUSTOMER STORY_ ### ING BANK ING Bank deployed Ververica to process financial events in real time across its global operations. The result: fraud detection that acts before transactions settle, not after. **Result:** Fraud detection that acts before transactions settle, not after. 2B+ events per day. Fraud detection across 40 countries. Sub-second decision latency. - **2B+**: Events scored per day - **>1MS**: Sub-second decision latency ## Frequently Asked Questions ### What is real-time stream processing for banking? [Real-time stream processing](https://www.ververica.com/ecosystem-introduction/stream-processing-with-apache-flink-beginners-guide) analyzes financial data the instant it arrives, without waiting for batch windows. Banks use it for fraud detection, payment processing, AML monitoring, and regulatory reporting. Ververica processes 6.9B records/sec with sub-10ms latency on the VERA engine. ### How does Ververica differ from open-source Apache Flink? Ververica was founded by the creators of [Apache Flink](https://www.ververica.com/ecosystem-introduction/what-is-apache-flink). The VERA engine delivers 2x the throughput at 52% lower resource consumption. Ververica adds enterprise security, multi-tenancy, automated operations, and 24/7 support. It is Flink, built for production banking workloads. ### How does Ververica support DORA requirements? Under DORA, the bank stays responsible for its compliance. Ververica is built for DORA requirements: it supports ICT risk management, continuous monitoring, incident detection and operational resilience testing. Ververica holds SOC 2 Type II and ISO 27001 certifications and is built for GDPR requirements. BYOC and self-managed deployment keep data in the bank’s own environment. ### What deployment options are available for banks? Banks can deploy Ververica Cloud (fully managed), BYOC (your cloud, our platform), or self-managed (your infrastructure). BYOC is the most common choice for regulated institutions. All data remains in your environment. No vendor access to production data. ### How long does implementation take for banking use cases? Typical banking deployments reach production in 8 to 12 weeks. Fraud detection and real-time payments are the most common first use cases. Ververica provides pre-built connectors for Kafka, mainframe CDC, and core banking systems. Professional services teams specialize in financial services. ### Does Ververica support exactly-once processing for financial transactions? Ververica guarantees [exactly-once processing semantics](https://www.ververica.com/developers/flink-exactly-once-kafka). Every financial transaction is processed once and only once, even during failures or restarts. This is non-negotiable for payments, settlements, and regulatory reporting. The guarantee holds at full throughput.