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

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.

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Money – cash, digital, crypto and more…..

I came across a short and stimulating article by the IMF staff on current state of digital and paper money which identifies essential, conceptual features of all payment types and based on that categorizes them into 5 types. From the paper, I took away three main insights -first there is a compelling argument that traditional forms of payment transactions by banks (referred to as B-Money) will face intense competition from electronic money (or E-Money) in coming years; this will obviously hurt the profitability of the banks given that all retail banks are rely primarily on deposits for funding and will create further disruption in the banking sector. Second, the article conjectures that eventually banks could be forced to offer electronic money or similar products and we can see that happening already with JP Morgan dipping toes into digital money waters by offering JPM Coin by end of 2019. Lastly, role of the central banks will be pivotal as they could jump into the fray and offer central bank digital currency (being explored by Sweden, Uruguay, China, Thailand, Japan and South Korea) and also shape the environment and the pace of innovation for digital money.

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AI in Finance – a report by the Alan Turing Institute report

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.

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