AI-Public Company Financial Analysis

Deeply integrates big data, knowledge graphs, and multimodal machine learning technologies to build an intelligent regulatory analysis platform covering financial anomaly detection, holistic entity profiling, and correlated risk penetration. It provides precise, forward-looking risk insights and decision support for regulators and investment institutions.

Solution Value

Facing the challenges of high complexity and strong concealment in public company financial information, coupled with the lag and inefficiency of traditional analysis methods, we offer a comprehensive "data-driven, intelligent judgment, graph-correlated" analysis solution. Leveraging terabytes of holistic enterprise data and cutting-edge AI algorithms, the solution not only achieves multi-dimensional anomaly detection and root-cause interpretation of financial statements but also constructs dynamic knowledge graphs to deeply correlate multi-source information—including corporate registration, public sentiment, judicial records, and transactions. This enables penetration and revelation of potential financial fraud, related-party transactions, and market manipulation risks, thereby empowering regulators with proactive discovery capabilities and assisting investment institutions in making in-depth value and risk assessments.

Core Modules

Intelligent Financial Anomaly Detection & Early Warning Engine

Integrates multiple machine learning algorithms (e.g., anomaly detection, time-series forecasting, NLP) to conduct deep scans of companies' three major financial statements and notes. Automatically identifies risk points such as abnormal fluctuations in financial indicators, inconsistencies between accounting items, and contradictory textual disclosures. Provides quantified risk scores and suspected cause interpretations, enabling early warning and lead generation for financial risks.

Public Company Holistic Profile & Knowledge Graph Platform

Integrating internal corporate data (annual reports, announcements) with external big data (business registration, tenders, recruitment, litigation, patents, public sentiment) to construct a dynamic knowledge graph centered on the listed company and linked to its shareholders, suppliers, customers, competitors, and key personnel. Through graph computing and frequent subgraph mining, it visually presents the corporate association network, capital operation paths, and potential benefit transfer chains.

Correlated Risk Analysis & Trading Behavior Monitoring

Based on holistic profiles and knowledge graphs, extends analysis to the secondary market. Employs graph algorithms and sequence models to intelligently identify suspected market manipulation behaviors (e.g., matched orders, painting the tape), insider trading patterns, and correlated or coordinated abnormal trading across companies and markets. Precisely pinpoints anomalous time windows and entities, reconstructing violation patterns.

Bond Credit & Default Warning Extension Module

Extends corporate risk analysis capabilities to the credit bond market. Utilizes big data credit assessment technology to dynamically evaluate the integrity and operational risks of issuers and related parties. Constructs an advanced bond default prediction model, enabling dynamic tracking and early warning of credit risks.

Overall Architecture

The solution establishes a three-tiered, integrated analytics platform comprising "Data - Algorithms - Applications"

Data Resource & Fusion Layer

Aggregates structured financial data, unstructured announcement text, and vast amounts of external corporate behavioral data. Employs entity alignment and attribute fusion technologies to build a standardized, correlated enterprise holographic data lake.

AI Algorithm & Model Platform

Deploys financial analysis models, knowledge graph construction engines, graph mining algorithm libraries, trading behavior analysis models, and credit risk assessment models. Supports continuous model training, iteration, and online service encapsulation.

Intelligent Application & Visualization Layer

Provides a risk monitoring dashboard for regulators, a deep investigation and analysis workbench, and risk inquiry/graph exploration tools for investors. Presents complex analysis results intuitively through interactive charts and relational network graphs.

Key Advantages

Comprehensive & Multi-dimensional Analysis

Breaks through the limitations of single-source financial data analysis by integrating corporate "behavioral data" and "relationship networks," enabling penetration analysis from financial performance to business substance.

Early Warning Timeliness

Leverages machine learning models for real-time scanning of massive features, enabling earlier detection of concealed anomaly patterns and potential risk signals compared to traditional manual review.

Deep Correlation & Penetration

Powerful knowledge graph capabilities support second-level penetration and visualization of complex ownership structures, hidden related parties, and fund flow paths, exposing concealed risks.

Strong Technology Fusion

Comprehensively utilizes cutting-edge AI technologies such as multimodal machine learning, NLP, and graph computing, featuring a complete and battle-tested technology stack.

Regulatory Practical Experience

The analytical framework and model design are closely aligned with practical securities regulatory needs, backed by project experience and technical expertise gained from serving high-level regulatory bodies.

Quantified Benefits

Enhances Regulatory Efficiency: Automates preliminary screening of massive announcements and financial data, allowing analysts to focus on deep investigation of high-risk leads, improving efficiency by multiples.
Improves Detection Accuracy: Significantly increases the identification accuracy of complex financial fraud and disguised related-party transactions through multi-dimensional, correlated analysis.
Strengthens Risk Prevention: Achieves warnings for risks like bond defaults months in advance, providing valuable time windows for risk mitigation and reducing market impact.
Promotes Scientific Decision-Making: Provides quantitative data and model support for evaluating regulatory policy effectiveness, assessing industry-wide risks, and aiding investor asset allocation.

Application Scenarios

Intelligent Screening of Public Company Financial Fraud: Deployed a system for a securities regulator. By applying machine learning to historical financial reports and public information, successfully built multiple financial anomaly identification models, generating batch lists of high-risk companies and specific anomaly clues, which became crucial directional references for on-site inspections.
Holistic Due Diligence for Pre-IPO/Listed Companies: Served investment institutions and investment banking departments of securities firms. Used knowledge graphs to quickly clarify corporate association networks, verify the fairness of related-party transactions, and assess actual controller risks, enhancing the depth and efficiency of due diligence.
Dynamic Credit Risk Monitoring for Bond Investments: Provided dynamic issuer risk tracking services for fund management companies and bank wealth management subsidiaries. Enabled continuous big-data monitoring of issuers’ operations, public sentiment, and judicial status, with warnings for risk contagion within associated groups, assisting credit bond investment decisions and risk control.
Market Trading Surveillance & Behavior Analysis: Assisted exchanges and regulatory technology departments. Developed abnormal trading behavior identification models based on trading data and account association graphs, used for monitoring violations like market manipulation and insider trading, helping to purify the market environment.

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