Overview

Trade surveillance has become one of the most critical regulatory functions in modern markets. Yet, the volume, velocity and variety of data (trades, orders, communications and market signals) make manual investigation impractical and costly. HCLTech’s InsightGen, built natively on AWS, delivers a GenAI–powered framework that automates alert triage, contextualizes disparate data and generates evidence-backed, auditable summaries. The result is faster alert closure, higher accuracy and transparent compliance at scale.

The problem: Manual surveillance in a data-heavy world

Surveillance platforms generate thousands of alerts daily, out of which more than 90% are false positives. Investigators must correlate alerts with orders, trades, emails, chats, voice transcripts, news and market data, typically scattered across silos with limited context. This leads to:

• High operational overhead and delayed investigations.
• Fragmented insights and inconsistent reporting.
• Time-consuming evidence collection without explainable traceability.

Introducing InsightGen

InsightGen is HCLTech’s AI-enabled platform for end-to-end alert analysis and market abuse detection. It correlates alerts with structured (orders, trades, market, reference and unstructured (emails, chats, voice, news) data to deliver holistic, explainable case dossiers that investigators can trust.

What makes InsightGen different

• GenAI–driven correlation of multi-source data with temporal and semantic reasoning.
• Agentic AI frameworks for performing deeper, contextual investigations autonomously.
• AWS-native design for security, scale and cost efficiency.
• Explainability-first approach with clear evidence trails and audit logs.
• Analyst-centric UX: summaries, event timelines, network graphs and cross-communication analysis.

Business and technical objectives

Business objectives

• Automate manual investigation workflows.
• Reduce false positives and lower cost per alert.
• Accelerate evidence collection and regulatory reporting.
• Strengthen detection and protect reputation.

Technical objectives

• Automate multi-source correlation and context enrichment.
• Leverage Amazon Bedrock for LLM-driven summarization, reasoning and embeddings.
• Use serverless and managed AWS services for elastic scale and reliability.
• Deliver explainable insights via an intuitive investigator UI.

Data model and correlation logic

Core datasets

• Alert data.
• Order data.
• Trade data.
• Communications data: emails (text), text chats (transcripts), voice (transcripts).
• News and market data (instrument IDs, price/volume movement).

Reference data

• Instruments.
• Traders.
• Desks.
• Clients/accounts.

Correlation patterns

• Direct:
  • Alert → Order → Trade → Instrument → Trader/Client → Desk.
• Indirect (time/entity/semantic):
  • Communications ↔ time windows + trader identities.
  • News ↔ time windows.
  • Market ↔ time windows + instrument IDs.

Communication analysis challenges

Massive, loosely linked communications are filtered via time slicing, entity matching and semantic similarity. LLM-based NLP pipelines and pgvector embeddings perform context-aware retrieval, minimizing noise and surfacing relevant, explainable signals.

Solution overview: GenAI–driven alert investigation

InsightGen automates the correlation of alerts with trade, market, communication and reference data using AWS-native AI. At its core:

• Amazon Bedrock LLMs execute contextual reasoning, temporal analysis and summarization.
• LangChain + LangGraph orchestrate multi-step reasoning workflows and Agentic decision-making for correlation logic.
• Agentic AI frameworks enable dynamic task decomposition and adaptive investigation strategies.
• pgvector (Amazon RDS for PostgreSQL) powers embedding creation and similarity search.
• Event-driven orchestration (AWS Lambda, SNS) ensures scalable, resilient processing.
• Outputs include structured summaries, evidence dossiers, risk and confidence scores and explainable communication graphs.

This approach reduces time-to-resolution while maintaining consistency, accuracy and auditable traceability.

InsightGen solution architecture (AWS-native)

Figure 1 – InsightGen Solution Architecture

Core AWS services

• Amazon VPC: isolated network with private subnets.
• AWS Lambda: backend data processing and AI workflow orchestration.
• Amazon RDS (PostgreSQL + pgvector): vector storage and retrieval.
• Amazon Bedrock: LLMs for correlation, temporal analysis, summarization and embeddings.
• Amazon App Runner: hosts the Angular-based InsightGen UI.
• Amazon Cognito: identity and access management.
• Amazon SNS: event-driven messaging and alerting.
• Amazon CloudWatch: centralized monitoring and audit logging.

Processing flow (high level)

• Ingest alerts, orders, trades, comms, market and reference data.
• Embed unstructured content (communications, news) using Bedrock + LangChain → store in pgvector.
• Correlate via time/entity constraints + semantic similarity; enrich with market context (LangGraph orchestration).
• Summarize findings with Bedrock, assign risk and confidence scores.
• Present explainable dossiers, event timelines and comms graphs in the UI.
• Notify via SNS; log metrics and traces in CloudWatch.

Core components and functional design

• Correlation engine: GenAI logic is built using Amazon Bedrock, LangChain and LangGraph for temporal and contextual correlation across alerts, comms, market and reference data. Agentic AI capabilities dynamically adapt workflows based on case complexity, producing relevance rankings and explainable evidence.
• Synthetic data generator: Uses LangChain, LangGraph and Agentic AI frameworks to simulate alerts, trades, market movements and communication patterns. This enables continuous validation of detection strategies across scenarios (spoofing, insider trading, front-running, wash trades, manipulation).
• InsightGen UI (Angular on App Runner): Investigator-facing dashboards: alert summaries, event sequences, network diagrams, cross-communication analysis and investigator notes.
• Interactive chatbot (Streamlit): Natural-language querying for contextual clarifications, “show me related comms,” “explain risk drivers,” and “summarize this case”.
• AI-powered summarization module: Uses Amazon Bedrock to produce concise, compliant case summaries with evidence references and explainability cues.
• Analyst dashboard: A curated “workbench” that prioritizes cases, highlights key insights and consolidates supporting evidence and audit trails.

Deployment architecture and automation

Figure 2 – Deployment pipeline

• Cloud native IaC via AWS CloudFormation.
• CI/CD with AWS CodePipeline, CodeBuild, CodeDeploy (sourced from GitHub).
• Isolated environments (dev/test/prod) within dedicated VPCs, private subnets and role-based IAM.
• Version-controlled infra, automated rollbacks and blue/green or canary deployments for reliability and minimal downtime.

Security, compliance and governance

• Encryption: Data at rest with AWS KMS; data in transit with TLS.
• IAM: Least-privilege policies; fine-grained access to datasets and models.
• Observability and auditability: CloudWatch logs/metrics; CloudTrail API audit trails; tamper-proof logging patterns.
• Regulatory alignment: GDPR, SEC, FINRA, MiFID II.
• Explainable AI: Traceable prompts, retrieved context and evidence citations embedded in case files.

Monitoring, observability and maintenance

• CloudWatch for latency, throughput and model performance.
• CloudTrail for user and system API tracing.
• RDS snapshots and backups for durability.
• Automated patching, KMS key rotation and model retraining cycles to maintain performance and compliance alignment.

Benefits and business impact

• Operational efficiency: Automated correlation and summarization reduce investigation time by up to 80%.
• Cost optimization: Serverless, managed AWS services scale on demand, lowering the total cost of ownership.
• Regulatory confidence: Evidence-backed, explainable insights support transparent, consistent reporting.
• Analyst empowerment: Analysts focus on judgment, not data wrangling.
• Scalability: Global-ready architecture supports jurisdictional variations and policy evolution.

Typical investigator journey (illustrative)

• Alert lands in InsightGen (spoofing signal on Instrument X - Order).
• Auto-correlation links to orders/trades; enriches with market microstructure movements.
• Comms retrieval finds relevant emails/chats/voice within time windows; embeddings surface semantically related messages.
• Bedrock summarizes a case dossier with risk score, confidence and evidence citations.
• Investigator validates the timeline and network graph; adds notes; triggers closure or escalation.
• Audit package generated automatically for compliance review.

Conclusion

HCLTech’s InsightGen integrates Generative AI, LangChain, LangGrapha and Agentic AI frameworks with AWS-native scale and explainable analytics at the core of trade surveillance. It accelerates alert closure, improves accuracy and reduces operational costs while ensuring audit-ready transparency across global regulatory regimes. For institutions seeking a secure, intelligent and cost-efficient compliance platform, InsightGen represents the future of explainable trade surveillance.