Background

A major financial institution operating across banking, securities, and insurance divisions recognized that timely monitoring of market sentiment and risk signals is critical for effective risk management in a heavily regulated environment. With global financial markets moving rapidly, social media discourse and news coverage often serve as leading indicators of market volatility.

在金融業，風險預警與內部控制的作業要求通常由主管機關（金融監督管理委員會）的相關規範與各機構自訂的內控制度共同界定，並會隨政策方向調整。多數機構因此會持續檢視情報蒐集的即時性與覆蓋範圍：傳統仰賴研究員人工翻閱新聞與研究報告的方式，往往在覆蓋廣度與回應速度上出現落差。此外，企業內部累積大量法規文件、研究報告與風控紀錄，員工往往難以快速找到所需資訊。

本段為一般性說明，未指涉特定條文、函釋或罰則。實際適用範圍與作業要求，仍應以主管機關最新公告及貴機關（或貴公司法務）認定為準。全國法規資料庫

Challenges Faced

• Tracking the public sentiment of thousands of listed companies and financial market developments daily involves an enormous volume of data
• Market risk signals are scattered across news outlets, social media, and research reports across different channels, making unified aggregation difficult
• Regulatory changes are frequent, requiring the compliance team to monitor domestic and international financial regulatory developments in real time
• Tens of thousands of regulatory documents and research reports have accumulated internally, resulting in low employee query efficiency
• Risk control alerts require real-time responsiveness, which traditional daily-report-based intelligence gathering methods cannot deliver

Industry Solutions

The institution simultaneously deployed LargitData's InfoMiner sentiment analysis platform and RAGi enterprise AI engine, building a comprehensive AI-driven risk management framework.

InfoMiner Sentiment Intelligence Risk Control Module

• Financial Market Sentiment Monitoring: real-time tracking of news and social media discussions related to listed companies, financial markets, and economic indicators
• Risk Signal Alerting: automatically detect risk signals such as surges in negative sentiment and abnormal discussion patterns using AI sentiment analysis
• Compliance Monitoring: track regulatory announcements and policy changes from domestic and international financial regulatory authorities
• Individual Stock Sentiment Dashboard: create dedicated monitoring dashboards for key investment targets, integrating multiple information sources

InfoMiner Social Listening →

RAGi Enterprise AI Retrieval-Augmented Generation Engine

• Regulatory Knowledge Base: import tens of thousands of regulatory documents, allowing employees to query relevant provisions using natural language
• Research Report Retrieval: quickly search historical research reports and market analysis documents, with source citations and summaries provided
• Risk Control Case Library: build a historical risk control event database for the risk management team to reference and learn from

RAGi Enterprise AI Retrieval-Augmented Generation Engine →

Implementation Results

• 風險訊號從原本的隔日報告提前為即時預警，風控團隊在盤中即可看到異常聲量與負面報導的彙整
• 合規團隊查詢法規條文的時間從平均 2 小時縮短至數分鐘
• 建立全台上市櫃公司即時風險預警機制；實際可監測的標的清單與資料來源組合，依訂閱設定與各來源的授權範圍調整
• RAGi 知識庫收錄數萬份內部文件，成為員工日常工作的核心資訊入口
• Overall risk control operational efficiency improved by 60%, allowing human resources to be reallocated to higher-value strategic analysis work

成果的量測口徑與適用前提

上述數字是該專案在特定期間、特定監測範圍下的量測結果，會隨資料來源組合、關鍵字設定與既有作業流程而不同，不宜直接套用到其他機構。評估同類方案時，建議先與供應商確認三件事：第一，效率提升的分母涵蓋哪些作業步驟，是只算報告撰寫，還是包含資料蒐集與人工覆核；第二，改善前的基準線如何取得，是實測還是受訪者回憶；第三，預警的計時起點是原始貼文發布時間，還是資料入庫時間，兩者的差距在封閉社群來源上可能相當可觀。

另需說明，輿情訊號屬於風險管理的輔助資訊，反映的是公開討論的變化，而非市場事實或未來走勢；系統可能因來源延遲、斷詞誤判或同名公司混淆而產生誤報與漏報。相關輸出不構成投資建議，最終的風險判斷與處置，仍應由風控與投研人員依內部程序負責。