Kiffmeister’s #Fintech Daily Digest (12/03/2020)

US Lawmakers Introduce Bill That Would Require Stablecoin Issuers to Obtain Bank Charters

A new U.S. Congressional bill would require prospective stablecoin issuers to obtain a banking charter, and notify and obtain approval from the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), and the appropriate banking agency 6 months prior to its issuance. Such issuers would also be required to conduct an ongoing analysis of any systemic risk; and have FDIC insurance or maintain reserves at the Federal Reserve for easy conversion back into U.S. dollars, on demand.  

Getting ahead of the curve on preventing cryptocurrency providers from repeating the crimes against low- and moderate-income residents of color that traditional big banks have is — and has been—critically important. Facebook has attempted  to take advantage of the financial exclusion and gap in the market—just one of the actors that have pursued issuing stablecoins by pegging them to a basket of major conventional currencies. JP Morgan, Apple, and Paypal/Venmo have also considered issue their own stablecoin variants that also have the potential to take advantage of unbanked and underbanked communities.

Project Helvetia: Settling tokenised assets in central bank money

Project Helvetia, an experiment between the Bank for International Settlements Innovation Hub Swiss Centre, the Swiss National Bank (SNB) and the financial market infrastructure operator SIX, successfully shows the feasibility of integrating tokenised assets and central bank money. The project demonstrates the functional feasibility and legal robustness of settling tokenised assets with a wholesale CBDC (PoC1) and with linking a DLT platform to existing payment systems (PoC2) in a near-live setup. However, the experiment should not be interpreted as an indication that the SNB will issue a wholesale CBDC. 

S&P Dow Jones Indices Builds Crypto Indexing Capabilities with Lukka

S&P Dow Jones Indices (S&P DJI) is launching global crypto-asset index capabilities with Lukka, a New York City-based crypto asset software and data company. S&P DJI will provide S&P DJI-branded and customized indexing and benchmarking solutions supported by Lukka’s proprietary crypto asset pricing data. 

CCAF, World Bank, WEF: Covid-19 caused uneven growth across fintech

A joint report by The Cambridge Centre for Alternative Finance, the World Bank and the World Economic Forum, has found that while 12 out of 13 fintech sectors reported year-on-year growth during the first half of 2020, significant discrepancies between sectors and regions remain. Digital banking, digital identity and regtech sectors showed a more modest growth increase of 10% compared to their counterparts in digital payments, digital savings, wealthtech and digital asset exchanges which saw transaction volumes grow in excess of 20%. Procyclical digital lending fintechs saw an average 8% decrease in transaction volumes. Fintechs based in Emerging Markets or Developing Economies saw higher average growth in transaction numbers and volumes (15% and 12% respectively) than those in Advanced Economies. 

Asia’s Crypto Derivatives Market Overview and Infographic 2020

95% of trades in the crypto derivatives market happen across futures based on 3 main crypto currencies: BTC, ETH and EOS. At the same time, the top 6 exchanges currently take approximately 83% of all derivatives trading volume. This dominance by large exchanges is however increasingly challenged by new market entrants that specialise on new products such as options and derivatives on alternative crypto currencies. Unlike in traditional finance, where most volumes are concentrated in Western economies, crypto derivatives are overwhelmingly traded on exchanges based in Asia. Singapore especially has been chosen by many companies as its operational base due to its reputation in the financial industry and its openness for innovation by regulatory bodies. 

On the risk-adjusted performance of machine learning models in credit default prediction

This paper proposes a new framework for supervisors to measure the risk-adjusted performance of machine-learning (ML) credit assessment models, harnessing the process for validating internal ratings-based systems for regulatory capital to detect ML’s limitations in credit default prediction. From a supervisory standpoint, having a structured methodology for assessing ML models could increase transparency and remove an obstacle to innovation in the financial industry.