I. Multi-Source Data Integration and Processing Module
This module serves as the foundation of the report generation system, featuring comprehensive enterprise-wide data ingestion and integration capabilities.
(1) Structured Data Integration
· Utilizes ETL tools to connect with both internal core systems (e.g., credit systems, credit investigation systems) and external data providers (e.g., iFinD, SCI99, Wisers).
· Enables automated collection of business registration, financial, credit, credit-reporting, and public-opinion data.
· Achieves over 95% accuracy in external data acquisition.
(2) Unstructured Data Processing
· Integrates OCR recognition (Paddle framework) and document parsing technologies to analyze layouts, recognize tables, and extract text from scanned financial statements, audit reports, and articles of incorporation.
· Combines with a human proofreading mechanism to ensure high-accuracy transformation of unstructured data into structured formats.
(3) Data Fusion Engine
· Performs cross-source consistency checks through rule-based and verification algorithms (e.g., reconciling financial statements with internal records).
· Eliminates data conflicts and builds a standardized data asset pool, providing high-quality data support for report generation.
II. Intelligent Report Production Module
This module is the core functional carrier of the system, encompassing template management, content generation, and data population.
(1) Template Management
Supports both “template-driven” and “content-driven” modes for flexible report configuration:
1731. Visual Design Tool — Offers a drag-and-drop design interface allowing business users to define report sections and field attributes.
1732. Intelligent Adaptation Engine — Uses rule-based logic to automatically select the appropriate template based on enterprise industry and report type (e.g., manufacturing due diligence reports, technology enterprise credit reports).
1. Supports multi-version template management and seamless switching between new and old templates.
(2) Content Generation and Population
Divided into objective data filling and subjective analytical writing:
3. Objective Data Filling — Through field mapping, standardized data (e.g., executive profiles, shareholder structure, financial statement data) from the data asset pool is automatically populated into the corresponding report sections, eliminating manual input.
4. Subjective Analysis Writing — Powered by LLM (DeepSeek-32B) combined with industry knowledge graphs to generate intelligent, section-level narrative content such as:
1. Industry analysis (integrating SCI99 market data)
2. Operational performance analysis (based on enterprise annual report decomposition)
3. Risk analysis (combining public-opinion and credit data)
4. Supports chapter-by-chapter generation or one-click full report generation, meeting varied business needs.
III. Analysis and Decision Support Module
Focused on maximizing the analytical value and decision-support capabilities of reports, this module includes three key sub-functions:
(1) Financial Analysis
Leverages a financial indicator engine and linkage analysis model to enable multi-dimensional financial diagnostics:
3505. Indicator Calculation — Automatically computes metrics such as liquidity (current ratio, quick ratio), profitability (gross margin, net margin), and efficiency (accounts receivable turnover), and visualizes results through trend charts.
3506. In-Depth Analysis — Embeds financial logic for interpretive insights, e.g.:
1. Asset growth analysis (e.g., total assets in 2025 increased by 93.83% compared to 2021, with detailed decomposition of drivers).
2. Profitability comparison (e.g., gross margin in the aluminum sector rose from 20.74% to 27.26% due to improved cost efficiency).
(2) Risk Assessment
Establishes a multi-dimensional risk evaluation system:
3. Risk Identification — Based on credit reports, court rulings, and media sentiment, the LLM-based risk model identifies credit, legal, and reputational risks.
4. Risk Alerts — Automatically generates risk alerts (e.g., other receivables over three years accounting for 28.48%—potential recovery risk) and provides risk mitigation recommendations.
(3) Compliance Review
Integrates financial regulatory rules and internal approval logic to conduct compliance validation on reports — including credit line conformity and guarantee compliance — ensuring adherence to supervisory and internal standards.
IV. Auxiliary and Management Module
This module provides end-to-end assistance and quality control, ensuring report accuracy and traceability.
(1) Online Editing
Allows relationship managers to manually refine or adjust AI-generated content.
· Offers tools for text editing, format optimization, and table customization to meet personalized business needs.
(2) Data Traceability
Implements blockchain or log-based tracing to record the origin of every data point (e.g., data sourced from iFinD 2024 Annual Report or internal credit system record of March 2025).
· Guarantees verifiable and auditable data lineage.
(3) Reference Material Library
Backed by a knowledge base integrating corporate annual reports, industry studies, and best practices.
· Through RAG (Retrieval-Augmented Generation), enables smart retrieval of relevant materials to assist relationship managers in report writing.
(4) Task Management
Supports task creation, assignment, and progress tracking for report production.
· Visualizes task status, improving collaboration and project oversight.
V. Integration and Extension Module
Ensures compatibility with financial institutions’ IT ecosystems while supporting future business scalability.
(1) Financial IT Integration
Natively integrates with internal platforms such as HiAgent, core business systems, and CRM, enabling data interoperability and workflow linkage.
· Example: Automatically triggers a report generation task upon receiving customer information from CRM.
(2) Third-Party Data Integration
Provides standardized APIs for quick connection with new external data sources (e.g., additional credit bureaus, industry data providers), supporting business expansion.
(3) Technical Architecture Design
· Adopts containerized deployment and microservice architecture to ensure high availability and scalability.
· Enables linear performance scaling with business growth, supporting large-scale financial institution operations.
Overall Value:
The Intelligent Report Generation System combines data integrity, automation, and AI-driven analysis to deliver high-quality, compliant, and actionable financial intelligence reports — significantly improving efficiency, standardization, and readability across the reporting workflow.
