OlaXBT signs a memorandum of cooperation with Fengqi Financial: AIO NEXUS further enhances the capabilities of "dynamic credit evaluation × risk warning × voice recognition."
Web3 AI data platform OlaXBT announced a memorandum of understanding with Funki Finance. OlaXBT will provide pre-processed, auditable multi-source datasets and foundational machine learning models through the AIO NEXUS Data Layer to support Funki Finance's existing deep learning dynamic credit scoring model, AI fully automated loan approval system, time series forecasting analysis platform, and anomaly detection risk monitoring dashboard.This collaboration will focus on expanding credit features and interpretability, enhancing market volatility foresight, shortening overdue warning times, and improving multi-language STT (speech-to-text) accuracy and low latency, thereby driving a dual upgrade of inclusive finance and risk control capabilities through technology. Through this partnership, both parties will establish a joint KPI and A/B testing framework, implementing it in phases under the principles of compliance and privacy-first, continuously optimizing user experience, improving approval efficiency, and reducing capital mismatch risks.OlaXBT is a decentralized AI data infrastructure equipped with a powerful financial data layer AIO NEXUS, multi-factor analysis patented technology, and MCP server market. It provides reinforcement learning AI agents that can deliver strategy information in real-time; employs a hybrid architecture to preprocess macro data, on-chain indicators, and sentiment analysis for tracking large fund movements, trend judgments, automated treasury management, and AI agent minting functions.Funki Finance is a licensed money lender in Hong Kong and a wholly-owned subsidiary of the listed company King Yat Holdings Limited (HKEX: 00070). Positioned as a fintech company, it is committed to providing flexible and convenient online loans and one-stop financial services through innovative technology, emphasizing credit management and risk control, and continuously introducing deep learning and machine learning technologies to optimize decision-making processes.
