What is the error in the estimates of the popular Covid-19 models and what can we conclude from that insight?
A summary of the 2019 World Economic Forum report on key obstacles preventing AI from being fully adopted and transforming the financial sector.
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.
Fascinating publication by Reuters on how the world is consuming digital news – watch the 3 minute clip if you don’t have time to read the 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.
In my last post (Risk Management version 2020?) on evolving nature of Risk Management, I noted that non-financial risks dominate financial risks for financial institutions