September 2019 – Random Thoughts…

Machine Learning (ML) & Anti-Money Laundering (AML) – made for each other

September 26, 2019 amits99

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.

Machine Learning Explainability – QII, Shapley Values and Data Shapley

September 14, 2019 amits99

Introduction: One of the key challenges of using machine learning (ML) is the lack of explainability which progressively decreases as the model complexity increases starting

Model Risk – view from Actuarial Industry

September 2, 2019 amits99

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.