Business lessons learnt by the data scientists at Booking.com after deploying 150 Machine Learning (ML) models. These observations are insightful and offer a practitioner view regarding implementation of ML models for business purpose and some are even non-intuitive.
Another month and another publication on Artificial Intelligence (AI) by regulators is out. This time it is by the De Nederlandsche Bank (DNB). The principles
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.
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
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.
In an announcement this month that went under the radar due to the US-China trade war grabbing the headlines, the People’s Bank of China (PBoC) announced