AI technologies, such as machine learning (ML) algorithms, can analyze large amounts of data and detect patterns and anomalies that may indicate fraudulent activity. Fraud management systems based on artificial intelligence can identify and prevent various types of fraud, such as payment fraud, identity theft, or impersonation attacks. Imposter scams, which the Federal Trade Commission (FTC) identified as the most common type of consumer fraud, can just as easily target a CEO as they are at a retired construction worker. Bank realized that fraud analysis based on artificial intelligence and machine learning algorithms was the only foolproof way to protect its millions of customers from fraud as effectively as possible.
AI could also improve the customer experience by reducing false positives (mistakenly marking a transaction as fraudulent) during fraud detection processes. Hear from Elenita Elinon, from JPMorgan Chase, recognized as one of the 40 leading women in data and artificial intelligence for her work with risk and fraud. Whether it's false negatives or false positives, inaccuracy in fraud detection should be as infrequent as possible. Now, AI has its sights set on improving the landscape of fraud detection and management, an area plagued by difficulties as the number of online and mobile transactions increases.
AI can improve this situation by detecting and preventing fraudulent activities as they occur and, subsequently, blocking malicious users and transactions. This requires a truly unified view of transactional and behavioral data, as well as machine learning models that are capable of analyzing fraud risk well in advance. Analysts noted that different use cases of AI for fraud management require different capabilities, something that is important to consider when implementing AI for fraud detection and management.