AI-Powered Fraud Detection with IBM & Azure

Financial fraud is one of the most cost-intensive risks in the finance and insurance industry. Whether credit card fraud, insurance fraud or money laundering - the threat situation is diverse and developing rapidly. Conventional rule-based systems are no longer sufficient to efficiently detect or prevent attacks.

Technological progress in the areas of artificial intelligence (AI) and machine learning (ML), particularly with solutions from IBM Watson and Microsoft Azure, is now opening up new possibilities for early, highly accurate fraud detection - in real time and integrated into existing processes.

Why fraud detection is currently the most market-relevant AI use case

According to the PwC AI Investment Study 2024, fraud detection is a top priority for banks and insurers in the field of artificial intelligence - even ahead of topics such as customer retention or credit risk modeling.

The reasons are clear:

• Directly measurable cost savings
• Reduction of reputational and compliance risks
• Strengthening customer trust
• Immediate ROI through fewer false positives and automated processing

IBM Watson: Combating fraud with deep learning and self-learning models

IBM Watson combines advanced machine learning techniques with domain-specific expertise in finance & insurance. In conjunction with IBM Safer Payments and Watson Studio, it creates a powerful platform for anomaly detection in transactions and claims processes.

Core functions:

• Use of LSTM and autoencoder architectures for sequential transaction analysis
• Reinforcement learning for adaptive decision making
• Natural language processing for the analysis of unstructured data such as emails, call center logs or damage descriptions

A real-life example:
A major European bank uses IBM Watson to evaluate over 200 data points in real time for each payment - including device data, usage behavior and geographical patterns. The result: a 27% increase in the recognition rate with a simultaneous 16% reduction in false positives.

Microsoft Azure AI: Scalable models and seamless integration into core systems

With Azure Machine Learning, Azure Synapse Analytics and Dynamics 365 Fraud Protection, Microsoft Azure offers a modular, fully integrable AI framework. Companies benefit from a strong cloud infrastructure, low-code model provision and native connection to operational systems.

Typical architecture components:

• AutoML-supported model building with Azure ML
• Data streaming and feature extraction via Azure Event Hubs and Azure Functions
• Model provisioning via Azure Kubernetes Service (AKS) or container instances
• Combination with Synapse for real-time analytics and reporting

A practical example:
A leading insurance company used Azure to train an ensemble model from XGBoost and Deep Neural Networks on seven years of claims data. Within four weeks, manual review was reduced by over 40% while maintaining the same level of security.

Integration into the tech stack: crucial for project success

An isolated AI solution offers little added value. The key lies in seamless integration into existing systems such as CRM, claims management, core banking or customer support. IBM and Azure enable precisely this connection.

Conclusion

AI-based fraud detection is increasingly becoming the standard in the finance and insurance industry. Companies that already rely on scalable AI solutions such as IBM Watson or Microsoft Azure not only gain a technological advantage, but also benefit immediately in the form of risk minimization, cost reduction and increased efficiency.

Next steps

Would you like to establish fraud detection as a productively integrated AI process? We will evaluate your specific use case together and support you from model architecture to productive implementation - based on IBM or Azure.

Let us evaluate your use case and build an MVP with Azure or IBM Watson together.

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