DNA effect of BiG Tech

What is the DNA effect? The DNA effect is the ability of large technology companies to build a competitive advantage by leveraging user generated data in their networks. DNA in this context stands for ‘data-network-activities’ and refers to how the business model of large technology companies (like Google, Apple, Facebook, Alibaba, Tencent aka Big Tech) depends on direct interactions of users which generates lost of data and the ability of these companies to use this data to scale up operations and enter into new areas like financial services.

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Future of Work

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.

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IMF Global Outlook – 2021

This week, the International Monetary Fund (IMF) released their latest forecast which predicts that world GDP will grow by 5.5% in 2021 against a net decline of -3.5% seen in 2020. IMF foresees that first half of 2021 will see softer growth and the growth momentum will increase in the second half. The rebound in 2021 is based on the twin-punch of a ‘vaccine-powered’ recovery coupled with policy support in large economies.

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Looking at the US Aug Unemployment report

The US unemployment report for Aug showed the 2nd biggest monthly drop in US unemployment after a 2.2% reduction that occurred in June 2020. However there are a three caveats to the impressive rebound; biggest increase was driven by temporary hiring for 20202 Census and the permanent job losses continued to rise while the number of long term unemployed hardly moved.

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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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