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 to detect and prevent fraud. Rules-based systems without advanced analytical capabilities are not adaptive enough to detect sophisticated fraud schemes.
Goals and Objectives
Improve efficiency of fraud processes to reduce operational costs while more accurately identifying fraudulent activity. As a result, operational and reputation risks are reduced.
Artificial intelligence, machine learning, natural language processing & generation, and RPA
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. Suspicious activity regulatory reporting is highly automated through natural language processing and generation.