The Bank of International Settlements (BIS) and the Bank of England just unveiled Project Pyxtrial.
The technology offers a stablecoin monitoring solution to ensure that issuers of the digital asset have enough backing assets that exceed their liabilities.
Key Features of Project Pyxtrial
According to the BIS and BoE concept paper, Project Pyxtrial utilizes a prototype data analytics pipeline to ensure that the backing asset of stablecoin issuers exceed their liabilities. The platform enables data collection, storage, and evaluation for regulatory purposes and for the information of project managers.
The system possesses partially automatic and real-time data gathering. It collates hourly information sourced from on-chain and off-chain stablecoin hosts. After that, it matches the data held by issuers or regulators. In addition to boosting the efficiency of the process, it allows supervisors to respond faster to any issue or risk that may arise during operations.
Pyxtrial has a modular infrastructure for its Application Programming Interface (API), integration layer, data model, data storage, and dashboard. This way, it can be configured by each user to match their specific operational or system requirements. Moreover, these adaptive elements are reusable and can be repurposed for future upgrades.
Besides tracking stablecoins, the tech could be used to monitor tokenized assets. These include real-world asset (RWA) tokens as well as tokenized securities or bonds.
Project Pyxtrial Framework (Source: BIS and BoE Paper)
Limitations of BIS and BoE’s Stablecoin Monitoring System
The BIS and BoE noted some limitations present in their stablecoin monitoring system. First, they reiterated that the solution is not fully automated.
Authorized users must see to it that they have sufficiently provided the parameters of the inputs, and they should indicate the frequency of the data gathering process. They should also be well-versed in blockchain technologies and regulations.
Next, the platform relies on the self-declared data submitted by issuers. Hence, the flawed information could create conflicts in the veracity of its outputs. It could likewise leave problems in the synchronization or mediation of data, particularly in the issuers’ backing assets and liabilities.
Furthermore, the outputs of the system may be prone to biases in data quality. BIS and BoE pointed out that it does not assess asset quality, the complexities of asset valuations, or the nature of the sources. Variations in the appraisal of several data sources could easily impact its outputs, too.
Lastly, if the supervisors will only depend on information from external data providers, the Pyxtrial database must be connected to them at all times. This could be accomplished through an API. However, the setup could prove to be challenging if the network is unstable or exploitable in some way.
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