FinTech

In this work, fundamental design principles for a central bank digital currency (CBDC) with offline functionality and corresponding countermeasures are discussed. We identify three major objectives relevant for core security and privacy properties of such CBDC proposals: (i) Access Control Security—protection of a user’s funds against unauthorized access by other users; (ii) Security against Depositor Misbehavior—preservation of the integrity of an environment (potentially the wallet) against misbehavior of its owner (for example, double-spending); (iii) Privacy by Design—ensuring privacy is embedded into the system architecture. Our central conclusion is based on the alignment of the objectives to concrete design elements as countermeasures; certain objectives and countermeasures have no or minimal interference with each other. A major consequence of our work is that the integrity of a user’s wallet and, accordingly, the mitigation of double-spending race attacks should be addressed through the adaptation and integration of secure hardware or functionally equivalent trusted execution mechanisms within a CBDC system. The developed framework is applied for the analysis of five academic proposals of payment systems.

Mortgage lenders need evidence on how prediction models reproduce past decisions and distribute approvals across applicant groups. We study 34,012 Delaware mortgage records from 2024 and compare logistic regression with flexible tree-based models. Three findings answer the research questions. First, the selected flexible model predicts recorded lending decisions more closely and reduces average squared probability error by about 30% relative to penalised logistic regression. Second, racial differences persist when race, sex and ethnicity remain outside the model inputs. At a decision threshold chosen to reproduce the overall historical approval rate, the model approves about 66% of Black applicants and 78% of White applicants. Third, checks of probability reliability, missing debt information and decision thresholds identify issues for audit review. The available evidence does not establish individual reasons for denial. Independent mortgage banks account for 56.0% of sample records, and 57.5% record use of at least one automated underwriting system. The analysis concerns historical decisions and provides no evidence on subsequent loan repayment. Lenders and regulators gain a practical sequence for testing models, reviewing group differences and requesting supporting decision records before operational use.

The distinction between whether a shock to the market of any asset is a valuation event or a financial stability event lies in the mechanism through which such a shock is translated into demand for liquidity. The aim of this integrative review is to apply the same concept to crypto-asset markets, where such a translation is largely via stablecoins. This synthesis draws upon seven lines of research, market efficiency, portfolio behavior, stablecoin architecture, DeFi leverage, macro-financial linkages, illicit finance, and regulation, identified through searches of Scopus and Web of Science, citation searching and official sources for the years 2015 to 2026, as well as a date-stamped database of market and regulatory statistics from the 2021 cycle peak to mid-2026. The databases were last searched on 2 October 2026; of the 136 sources included, 32 were among the 9009 unique records retrieved. The review follows PRISMA 2020 where applicable, was not registered, and received no external funding. Three observations can be made from the findings. First, market efficiency in the crypto markets is time varying but not nonexistent, and tail dependency has reduced even while realized volatility has fallen. Second, a comparison of the 2022 collapse of TerraUSD and 2023 de-pegging of USD Coin highlights what differentiates an uncontained run from a contained run: an external reserve, the liquidity of the reserve and not the face value, and a credible resolution mechanism. Third, the reserve-asset channel is asymmetrical—outflows affect short-term Treasury yields two to three times more than equivalent inflows—hence, it cannot be modeled as a linear relationship. A Crypto Liquidity Stress Index is developed based on volatility, deviation from par, redemption pressure, liquidation intensity, and connectedness, all in a four-state escalation framework whose weights and thresholds remain to be estimated.