Summary of ‘Prediction Machines – the Simple Economics of Artificial Intelligence’ by Ajay Agrawal, Joshua Gans, and Avi Goldfarb
The impact of healthcare crisis, coronavirus aka Covid-19 has been devastating with more than 119,000 deaths worldwide as of April 11. This healthcare crisis has
The impact of Covid-19 on our lives has been profound and unprecedented both socially and economically. As more countries implement implement social distancing, now a
In January this year, the Federal Reserve Bank (FRB) published a working paper which assessed impact on the financial system from a hypothetical cyber attack
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.
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
View of model risk from the actuarial industry with some unique insights into types of model users and how they impact model risk and expected vs unexpected model risk error.
In an announcement this month that went under the radar due to the US-China trade war grabbing the headlines, the People’s Bank of China (PBoC) announced
In a new July 2019 report titled “Cybersecurity: Linchpin of the digital enterprise”, McKinsey emphasizes that cybersecurity must support the digital agenda of the company. In
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.
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.
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.
BackgroundTargeted Review of Internal Models (TRIM) is a regulation under ECB (European Central Bank) which is designed to bring common understanding and consistency across capital
OCC Spring 2019 Semiannual Risk Report Background: The Office of the Comptroller of the Currency (OCC) publishes a report (Semiannual Risk Perspective) twice a year
Summary: New working papers by Bank of International Settlements (BIS) and Financial Stability Board (FSB) conclude that financial institutions are more vulnerable to BigTech companies
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
Two recent reports on banking industry indicate point to the future of the Risk Management function. The first one was a 2019 report by Boston
Algorithmic Accountability Act Finally, something to speak about other than the Mueller Report in politics…… US lawmakers have introduced a new bill to hold giant
An approach to measuring the value of the free digital services provided by the Big Tech companies i.e. Google, Facebook, Wikipedia by valuing how much users would have to be compensated to not use them for a period of time.