Financial Services Use case

The introduction of our intelligent vertical credit model addressed several key challenges. Previously, credit data was fragmented, and no unified assessment framework existed. Loan approvals were slow, relying heavily on manual paperwork, while risk identification lacked precision. Local data sources—ranging from social security and healthcare to agricultural procurement and e‑commerce consumption—were missing from credit evaluations. Marketing efforts also struggled, with little ability to accurately target customer groups. The deployment of our solution transformed these processes, delivering speed, accuracy, and a comprehensive data‑driven approach to both lending and customer engagement.

Our Solution: Intelligent Credit Hub

• Built on Volcengine Doubao’s LLM and the Coze development platform, our hub unifies scattered data into a single framework. By combining credit union systems with local characteristic data and external platforms such as Alipay, WeChat, Douyin, and Rednote, it builds a comprehensive credit view. AI models and graph networks then generate both personal and enterprise credit portraits, assess loan quotas, and deliver proactive risk warnings. OCR and NLP technologies, enhanced by retrieval‑augmented generation, automate document extraction and structured reporting. Meanwhile, our campaign engine creates data‑driven marketing plans aligned to customer needs and trending topics. Security is built‑in, with role‑based and attribute‑based access control, federated learning, and encrypted data storage ensuring compliance and privacy.
• Our Solution: delivers measurable efficiency for county‑level financial institutions. Loan approvals are now processed up to five times faster, reducing waiting times from several days to just a few hours. Risk management has become more accurate with lower non‑performing loan rates and earlier risk detection across portfolios. Intelligent marketing has raised conversion rates by more than 15%, thanks to tailored recommendations and trending campaigns that drive customer engagement. At the same time, localized models and Coze workflows have significantly optimized costs. Most importantly, the system is fully replicable, forming a standardized “county‑level intelligent credit model” that can be adopted widely across rural credit cooperatives.
• Core Functions: The platform combines individual and enterprise credit analysis with deep operational insights. For individuals, it delivers precise loan quota evaluations, generates credit reports, and provides smart consumption recommendations. For businesses, it builds dynamic credit profiles, identifies compliance risks, and enables tailored credit strategies. Internally, the system diagnoses operational issues by flagging anomalies, locating bottlenecks, and uncovering unmet user needs. A document assistant further streamlines tasks by comparing contracts, highlighting differences, and automatically generating documents. On the marketing side, intelligent recommendations and event‑driven campaigns ensure that outreach is strategic and impactful. Designed for county‑level use, the system supports local deployment, lightweight offline operation, and adaptability to diverse regional scenarios.