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.

In the financial industry, operational requirements for risk alerting and internal controls are typically defined jointly by relevant regulations from the competent authority (the Financial Supervisory Commission) and each institution's own internal control system, and they shift with policy direction. As a result, most institutions continuously review the timeliness and coverage of their intelligence gathering: the traditional approach of researchers manually reading through news and research reports often falls short on both breadth of coverage and response speed. In addition, enterprises accumulate large volumes of regulatory documents, research reports, and risk-control records internally, and employees often struggle to quickly find the information they need.

This paragraph is a general description and does not refer to any specific provision, official interpretation, or penalty. The actual scope of application and operational requirements should still be determined by the competent authority's latest announcements and your organization's (or company's) legal counsel.Laws & Regulations Database of the Republic of China (Taiwan)

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

• Risk signals moved from next-day reports to real-time alerts, allowing risk control teams to view aggregated abnormal sentiment volume and negative coverage during trading hours
• Compliance teams' regulatory research time shortened from an average of 2 hours to just minutes
• Established a real-time risk alert mechanism covering all listed and OTC companies in Taiwan; the actual list of monitored entities and combination of data sources are adjusted according to subscription settings and each source's licensing scope
• The RAGi knowledge base indexes tens of thousands of internal documents, serving as the core information portal for daily work
• Overall risk control operational efficiency improved by 60%, allowing human resources to be reallocated to higher-value strategic analysis work

How the results were measured, and the preconditions for applying them

The figures above are measurement results from this specific project, over a specific period and monitoring scope; they vary with the combination of data sources, keyword settings, and existing workflows, and should not be applied directly to other institutions. When evaluating similar solutions, we recommend confirming three things with the vendor first: first, which operational steps the efficiency-gain denominator covers — just report writing, or also data collection and manual review; second, how the pre-improvement baseline was obtained — actual measurement or respondent recall; and third, whether the alert's timing starts from the original post's publish time or the time it was ingested into the system, since the gap between the two can be considerable for closed-community sources.

It should also be noted that sentiment signals are supplementary information for risk management: they reflect changes in public discussion, not market facts or future trends, and the system may produce false positives or missed detections due to source delays, word-segmentation errors, or confusion between similarly named companies. This output does not constitute investment advice; final risk judgment and handling should remain the responsibility of risk-control and research staff following internal procedures.

How to operationalize risk signals

A financial-risk workflow must separate public-information signals from verified risk events. A volume spike, negative article or social discussion is a triage input, not a credit, trading or compliance conclusion. The operating design should connect each alert type to an evidence package, analyst queue, severity rule, escalation owner and disposition code. Analysts need the source, timestamp, matched entity and triggering rule so they can resolve namesakes and contextual errors quickly.

Knowledge retrieval requires a different control set. Regulations and internal policies need version dates, ownership, access labels and superseded-document rules before indexing. Evaluation questions should cover ordinary queries, conflicting documents, missing answers and restricted content. A reliable system cites the governing passage or refuses when evidence is insufficient, and records reviewer corrections for later evaluation without silently rewriting historical results.

• Define alert evidence, severity, reviewer and escalation path.
• Measure precision and recall by entity and risk category, not only total volume.
• Version regulations and policies before knowledge-base ingestion.
• Test citations, refusal behavior and permission isolation with real roles.
• Track analyst disposition and downstream action to measure operational value.