Automated Investigation Workflows: Stop Fraud Faster
Blog post from Didit
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Manual fraud investigations can consume up to 60% of fraud operations teams’ time, leaving costly investigator resources focused on repetitive work and false positives while global fraud losses continue to grow. Automated investigation workflows address these challenges by using risk scoring, data enrichment, centralized case management, workflow-based decisions, and AI anomaly detection to prioritize genuine threats and speed resolution. An e-commerce example describes scoring new seller accounts based on identity, payment, and behavioral signals, automatically approving low-risk accounts, escalating medium-risk cases, and suspending high-risk accounts, reportedly reducing fraudulent sellers by 40% and saving $250,000 annually in chargeback losses for one client. Effective systems depend on accurate, current data, carefully selected fraud indicators, and regular model calibration, while Didit promotes its identity verification, risk scoring, no-code workflow, case management, and API tools as infrastructure for implementing these capabilities.
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