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.