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Advanced Customer Data Analytics Redefining Banking

Advanced Customer Data Analytics Redefining Banking

The bank customer of the day has a new avatar. Easy, instant access to wider information, extensive knowledge about choices available and privileged treatment by organizations in other industries have made them remarkably well-informed and demanding. They do not hesitate to quickly shift to whosoever serves their needs best.

AI-based Hybrid Approach to Banking Fraud Detection and Prevention

AI-based Hybrid Approach to Banking Fraud Detection and Prevention

The use of hybrid fraud detection models to ensure high fraud detection rates with low false positives is a vital aspect of enterprise fraud management. A ‘hybrid’ fraud detection model comprises 4 primary techniques are used to accurately risk score every transaction and to advise the right action of whether to approve, decline or challenge the transaction in real-time with sub-second response time.

Exploring RaaS for Enterprise Financial Crime Risk Management in Financial Institutions

Exploring RaaS for Enterprise Financial Crime Risk Management in Financial Institutions

Risk as a Service with a focus on Enterprise Financial Crime Risk Management (EFCRM) is fast becoming a core component in a bank’s overall risk management framework. EFCRM-centric RaaS addresses a spectrum of areas ranging from transaction monitoring to fraud management to money laundering to CFT (combating the financing of terrorism) programs to cyber-security.

Applying Machine Learning And AI In Fraud Detection: A Primer

Applying Machine Learning And AI In Fraud Detection: A Primer

In the world of financial transactions, rule based heuristics are often employed to detect fraud, rather than to detect anomalies. The cat and mouse chase of financial institutions and fraudsters is an ongoing battle that has costed the global economy close to half a trillion dollars. Given the fundamental shortcomings of rule based heuristics, how does machine learning help in fraud detection?

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