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.
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
A recent report by the Alan Turing Institute notes that use of AI in Finance in financial services will impact 4 principal areas – fraud detection; use of chatbots; algorithmic trading and increase in regulatory and policy making. The report makes for a good read and while the first three use cases are credible there is skepticism about the impact AI/ML will have on algorithmic trading. In this post I have included a short summary of the report with an assessment and critique of the 4 areas with supporting links to relevant articles.