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Architected from scratch
The key to NPA recovery often lies in how quickly and completely one can grasp a debtor's risk and asset information. ZhiKong targets NPA debt monitoring and research, building big-data retrieval and research capability that aggregates scattered clues from industrial-commercial, judicial and asset channels into a searchable knowledge base.
The system delivers real-time, dynamic, full-volume search, query and management of enterprise risk and debt information, so researchers no longer switch across multiple data sources to build a panoramic profile of a debtor.
By cutting the time cost of information retrieval and processing, it helps uncover hidden asset clues and directly raises NPA recovery amounts — turning "data is available" into "recovery is achievable".
A Java microservice architecture plus a big-data pipeline, combined with web crawling and ETL, continuously aggregates multi-source risk and debt data into a high-quality foundation for search and analytics.
A text-search engine powers full-volume, low-latency retrieval of enterprise risk and debt information, letting researchers get data in seconds rather than waiting for batch reports.
Visual risk profiles and debt-relationship graphs support disposal decisions, turning "data" into "actionable insight" and lowering the barrier to judgment.
ZhiKong and ZhiAn belong to the same Century Cloud period — one leaning left (SaaS business system), the other right (big-data foundation) — together forming my early understanding of the full "finance → data → decision" chain. This mindset was later fully reused and amplified in the insolvency domain: PoyiYun moved business workflows to the cloud, while Wupo Data structured the data and then used RAG and AI Agent for intelligent querying.