Threats are continually evolving and made more difficult by data breaches that are external to the bank. Firms face increasing pressure from regulators to be more proactive in detecting and preventing fraud. Rules-based systems without advanced analytical capabilities are not adaptive enough to detect sophisticated fraud schemes.
Goals and Objectives
Improve the efficiency of fraud processes to reduce operational costs while more accurately identifying fraudulent activity. Analyze transactions in real time to mitigate risk in instant payments. As a result, operational and reputation risks are reduced.
Hardware: Enterprise hardware
Services: Business consulting and managed services (BPO and SaaS)
Software: Analytics, AI, application platforms, and content workflow and management applications
Innovation accelerators: Cognitive machine learning
3rd Platform technologies: Big Data and analytics
Use Case Summary
Firms leverage AI to enhance fraud detection capabilities to identify sophisticated schemes. Explainable machine learning models are continually tuned by feeding investigative results and external data. Predictive analytics prioritizes case work. Regulatory reporting of suspicious activity is augmented with automation through natural language processing and generation.