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Item type:Publication, Off-chain real estate underlying a trust as a collateral for an on-board asset backed loan for investment(European Organization for Nuclear Research, 2021-10-01)By 2021 a structural disconnect existed between two large pools of capital: real estate owners holding significant value in physical property but constrained by the high cost of capital and the bureaucratic and legal burdens of accessing bank credit lines, and crypto investors holding liquid capital but lacking real-world collateral against which to deploy it productively within the emerging decentralized finance (DeFi) ecosystem. This whitepaper describes the Bitestate architecture, designed to bridge that gap. Real estate is transferred to a local trust managed by a registered trustee bank that secures legal enforceability and ensures KYC and AML compliance; the underlying "Rights of Disposal" are then tokenized as non-fungible tokens (NFTs) and used as collateral against revolving asset-backed credit lines provided by crypto lender pools; in the event of default, smart contracts trigger a notification flow that instructs the trustee bank to liquidate the compromised asset and repay the lender. The architecture targets real estate portfolios across Latin America — Peru, Mexico, Colombia, Brazil and Chile — with the investment vehicle anchored in a European regulatory regime contemplating the Tokens and Trustworthy Service Providers Act (TVTG) and its Ordinance (TVTV), commonly referred to as a "Blockchain Act" (the specific jurisdiction was left as an open placeholder in the 2021 draft). The contribution of the document is a concrete, legally structured architecture for integrating real-world real estate as collateral in the DeFi ecosystem. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets(Institute of Electrical and Electronics Engineers Inc., 2025-11-10)In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexplored. This paper presents a framework focused on predicting which beverage clients are most likely to deliver strong returns in volume after receiving a cooler. Using a private dataset from a well-known Central American brewing and beverage company of 3,119 B2B traditional trade channel clients that received a cooler from 2022-01 to 2024-07, and tracking 12 months of sales transactions before and after cooler installation, three growth thresholds were defined: 10%, 30% and 50% growth in sales volume year over year. The analysis compares results of machine learning models such as XGBoost, LightGBM, and CatBoost combined with SHAP for interpretable feature analysis in order to have insights into improving business operations related to cooler allocation; the results show that the best model has AUC scores of 0.857, 0.877, and 0.898 across the thresholds on the validation set. Simulations suggest that this approach can improve ROI because it better selects potential clients to grow at the expected level and increases cost savings by not assigning clients that will not grow, compared to traditional volume-based approaches with substantial business management recommendations2