Fraud detection, claims automation, credit decisions, customer service: the use cases are clear, but most AI programmes in banks and insurers stall on data quality, model governance and where the model runs. We fix all three. Data platforms that serve analytics and AI from one trusted source. AI Foundation Services with over 15 language models behind one API, hosted on T Cloud in Germany. And hyperautomation that links AI, RPA and process mining across the back office.
The fastest returns usually come from fraud and anti-money-laundering scoring, document and claims processing, customer service assistants and knowledge bots for internal teams. They share three traits: high volumes, clear rules and measurable outcomes. In insurance, claims triage and first-notice-of-loss automation typically lead; in banking, transaction monitoring and KYC. T-Systems helps you rank use cases by value, data readiness and regulatory risk before you build.
The EU AI Act classes credit scoring and life and health insurance pricing as high-risk AI. That brings duties for data governance, technical documentation, logging, human oversight and accuracy monitoring. Fraud detection and customer service bots carry lighter transparency duties. T-Systems AI Foundation Services include logging, model documentation and audit trails, and our governance teams help you set up the AI risk register and model cards regulators expect.
On T Cloud in German data centers, operated by Deutsche Telekom under EU law. AI Foundation Services serve more than 15 open and closed source language models through a single API, with retrieval-augmented generation and fine-tuning on your own documents, from credit policies to claims manuals. GPU capacity for training and inference is available in Europe, including the Industrial AI Cloud in Munich. Your data is not used to train third-party models.
Hyperautomation combines robotic process automation, AI, process mining, low-code platforms and workflow tools so that whole processes, not single tasks, run automatically. For a bank that could be loan onboarding from application to payout; for an insurer, claims from first notice to settlement. T-Systems provides consulting, implementation and operations, with more than 3,000 automation specialists and partnerships with UiPath, ServiceNow and PEGA.
Most failed AI projects fail on data. T-Systems designs data platforms that combine governance, lineage and quality rules with the analytics and AI workloads that consume the data. The Data-Driven Financial World whitepaper describes the approach: one governed data foundation for reporting, risk, actuarial analytics and AI, built to grow with new use cases rather than for a single project.
Yes. Real-time transaction scoring, adaptive models and explainable AI reduce false positives while keeping decisions traceable for regulators. The same approach detects claims fraud at insurers. Explainability matters: under the EU AI Act and BaFin expectations, a fraud or AML decision has to be understood by a human. T-Systems builds these systems on sovereign infrastructure so that payment and customer data stay in Europe.
