Earlier this month, the Presidents Working Group on Financial Markets (PWG) released a report on Stablecoins. This represents first attempt in designing prudential safeguards for stablecoin issuers.
Category: banking regulation
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.
FRB bitcoin
Reality of a central bank digital currency (CDBC) is coming closer. However the development of a CDBC is said to pose the biggest threat to banks in developed economies like US?
Review of the 2019 World Economic Forum report on ‘Responsible Innovation with AI in Financial Services’
A summary of the 2019 World Economic Forum report on key obstacles preventing AI from being fully adopted and transforming the financial sector.
Regulatory Expectations for use of AI in financial sector
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
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.
Random Thoughts... 