Build practical skills in AI fraud detection, fraud prevention, and machine learning for finance, insurance, trade finance, credit risk, and SMEs. You’ll learn how supervised and unsupervised learning, neural networks, clustering, NLP, anomaly detection, data preprocessing, feature engineering, and model evaluation can identify fraud patterns and strengthen risk decisions.

Designed for learners working with financial, insurance, risk, compliance, or SME environments, the course connects fraud analytics with procurement fraud, tax compliance, transaction monitoring, cyber risk, credit insurance, counterparty risk, construction risk, and SME operations. You’ll evaluate model outputs using explainability, visualization, human oversight, governance, and continuous monitoring while addressing privacy, bias, fairness, ethics, data silos, and concept drift. You’ll also explore default and loss risk, real-time risk scoring, trade-finance model validation, privacy-preserving analytics, cloud-enabled ML pilots, low-code platforms, and edge AI. By the end, you’ll be able to evaluate fraud and risk use cases, apply appropriate ML concepts, interpret model results, and plan responsible AI adoption. The course uniquely connects advanced fraud and risk analytics with practical implementation across finance, insurance, and SMEs.