{"slug":"red-hat-helps-denizbank-transform-banking-services-through-ai-and-machine-learning-innovation","url":"https://findausecase.com/use-cases/red-hat-helps-denizbank-transform-banking-services-through-ai-and-machine-learning-innovation","title":"Red Hat Helps DenizBank Transform Banking Services Through AI and Machine Learning Innovation","description":"DenizBank, Turkey's fifth largest private bank with nearly 15,000 employees, adopted Red Hat OpenShift AI via its IT subsidiary Intertech to automate and standardize data science pipelines for its 120 data scientists working in risk management, marketing and customer relations. The platform reduced time-to-market for new microservices models from several days to just 10 minutes, enhanced credit qualification prediction models, and improved fraud prevention, while GPU usage was optimized through automatic scaling.","company":"DenizBank","industry":"Financial Services","country":"Turkey","aiCapabilities":["Fraud & Anomaly Detection","Predictive Analytics","AI Model Development & MLOps"],"technology":["Red Hat OpenShift AI","Red Hat OpenShift"],"deployment":"Hybrid","problemStatement":"DenizBank's 120 data scientists working across risk management, marketing and customer relations relied on Intertech to ensure the reliability and stability of their environments, but faced lagging speeds which hindered the progress of current and new projects.","solutionApproach":"DenizBank's IT subsidiary Intertech, with the help of Red Hat Consulting, integrated Red Hat OpenShift AI (building on Red Hat OpenShift, adopted in 2023) to automate and standardize data science pipelines, develop self-service capabilities with plug-and-play model development environments, scale model serving, and increase operational efficiency. Intertech adapted the solution to existing DevOps and GitOps best practices, and integrated hardware accelerator dashboards so Red Hat OpenShift AI automatically scales the GPU slices available to a model as needed.","businessValue":"DenizBank achieved a reduction in time-to-market for new microservices models from several days to just 10 minutes, enhanced credit qualification prediction models, and improved fraud prevention by more quickly identifying potentially problematic financial patterns. GPU usage was optimized through automatic scaling, maximizing resource utilization and allowing more workloads to run simultaneously without additional GPU hardware.","evidence":{"band":"high"},"sourceUrl":"https://www.redhat.com/en/about/press-releases/red-hat-helps-denizbank-transform-banking-services-through-ai-and-machine-learning-innovation","dates":{"publishedAt":"2026-08-15T23:01:53.157Z","publishedAtSource":"ledger","updatedAt":"2026-08-26T08:09:10.974Z"},"license":"Open for reading and citing with a link to https://findausecase.com/use-cases/red-hat-helps-denizbank-transform-banking-services-through-ai-and-machine-learning-innovation. Bulk republication requires permission."}