Bordelais & Cie

Bordelais & Cie manages the foreign-exchange reserves of the Bordelais family through a probabilistic model (mathematical and statiscal approach) that applies regression analysis, time series analysis, panel data analysis and machine learning algorithms. Embedded in our econmetric analysis are models that capture inherent data variability and robustly test hypotheses that enable trade and investment decisions to be formed on case studies that confirm the technical and operational feasability of a minimum viable product (MVP) rather than adopting a speculative approach based on mere probabilities and likelihoods. During the proof of concept stage the applications used range from: • Chain Code: In the context of Blockchain technology is a program that defines the logic for how data is stored and updated on hyperledger fabric, essentially acting as a self-executing smart contract for real-time, peer-to-peer, cross-border payments and foreign exchange transactions. • APIs: A well-designed API layer can facilitate interoperability between different private sector applications and central bank ledger designs. • Machine Learning: Used for various tasks like classification, regression, and clustering. • Data Science: Analyzing and understanding uncertainty in datasets. • AI: Artificial Intelligence is used for detecting financial crime, monitoring supply chains, enhancing cyber resilience and making predictions and decisions under uncertainty. • Data analytics: Macroeconmic tools leverage network analytics to identify financial crime patterns. • Payment systems infrastructure: Engineering different payment options, including online, in-store, and offline payments using asset-backed securties (ASB) and central bank digital currencies (CBDCs). • Supply chain management: We explore AI and big data for supply chain disclosure and adaptation.