The impact of healthcare crisis, coronavirus aka Covid-19 has been devastating with more than 119,000 deaths worldwide as of April 11. This healthcare crisis has
Money laundering is a massive drain on the world’s financial, legal and economic institutions and current rule based AML controls with a false positive rate of 90% are just not adequate to detect and monitor them. AML is ripe for disruption and innovation through use of Artificial Intelligence (AI) and Machine Learning (ML) and even the regulators are encouraging the same. Key areas of AML where AI and ML have been shown to work through recent published papers are risk scoring; customer segmentation and transaction monitoring using clustering (k-means); classification (support vector machines) and deep learning (graph convolutional networks). These approaches shows us a glimpse of the near future state of AML controls and how new technology can help solve the seemingly insurmountable problem of money laundering as it exists now.
View of model risk from the actuarial industry with some unique insights into types of model users and how they impact model risk and expected vs unexpected model risk error.
OCC Spring 2019 Semiannual Risk Report Background: The Office of the Comptroller of the Currency (OCC) publishes a report (Semiannual Risk Perspective) twice a year
In my last post (Risk Management version 2020?) on evolving nature of Risk Management, I noted that non-financial risks dominate financial risks for financial institutions