The popular view that emerging technologies like Artificial Intelligence (AI), Robotics will dramatically improve our personal and professional lives usually gets contrasted against the threat of the millions of jobs that are at risk from automation. Against this backdrop, a report last year based on a three year study by MIT offers a balanced perspective on the relationship between these emerging technologies and future of work and the labor market.
Summary of ‘Prediction Machines – the Simple Economics of Artificial Intelligence’ by Ajay Agrawal, Joshua Gans, and Avi Goldfarb
A summary of the 2019 World Economic Forum report on key obstacles preventing AI from being fully adopted and transforming the financial sector.
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.