• AI-Enhanced Phishing and Social Engineering Prevention: Develop a real-time, AI-powered platform that scans emails, chat messages, and other communications within the organization to detect and flag phishing attempts and social engineering attacks.
• Third-Party Risk Scoring and Monitoring Platform: Create a system that continuously monitors the cybersecurity posture of third-party vendors, utilizing real-time data feeds, security ratings, and vendor assessments. The system could use machine learning to predict potential risks based on vendor behavior and historical incident data.
• Automated Regulatory Change Management Platform: Build an AI-powered tool that tracks changes in global financial regulations, automatically assesses their impact on the organization’s processes, and recommends adjustments to remain compliant. This could integrate with existing systems to adjust operational workflows based on regulatory changes.
• AI-Powered Cyber Incident Response Playbook: Create an AI-driven incident response system that automatically identifies cyber threats, recommends response actions, and orchestrates workflows in real-time during a cyber incident. The platform could integrate with existing SOC tools and provide predictive recommendations based on similar incidents across the financial industry.

Use cases in Predictive modeling and forecasting: Analyze historical data and identify patterns that can be used to predict future trends, movements, and events in Capital Markets

• Sentiment Analysis:  Leverage natural language processing (NLP) techniques to analyze news articles, social media posts, and other industry-wide textual data sources to gauge market sentiment and predict potential market movements.
• Regulatory Compliance:  Utilize ML models to continuously monitor and ensure compliance with various financial regulations, such as anti-money laundering (AML) and know-your-customer (KYC) requirements.
• Sentiment and topic modelling:  Build quantitative strategies using textual data such as news, filings, earnings call transcripts, and social media. Use the same signals to alert discretionary traders to new opportunities and risks
• Predictive Liquidity Management: Forecast liquidity demands and collateral movement across CSDs. Prevent trapped liquidity by allowing participants to better manage collateral and minimize settlement delays. Develop an AI algorithm that suggests optimal collateral substitutions in real-time for margin calls across participants to measure and manage risk.

• Corporate Action Reference Data Ingestion: Ingest corporate action data from multiple sources such as regulatory filings, exchange announcements, emails, and news feeds. NLP models process unstructured and semi-structured formats to extract key corporate action details such as issuer, events, event type, affected securities, etc.
• Regulatory Compliance: Develop NLP models to automatically analyze and interpret financial regulations, public legal documents, and compliance policies, ensuring adherence to relevant laws and guidelines.
• Industry FAQ Chatbot: Compile a real-time superset of valuable capital markets data from publicly available sources to help capital market participants quickly access accurate, actionable insights, enhancing decision making and market intelligence without the need for manual research.
• Risk Assessment and Reporting: Implement NLP models to automatically generate risk assessment reports by analyzing financial statements, regulatory filings, and other relevant documents, enabling more efficient risk management processes.
• Corporate Action Verification: Utilize transaction data to identify inaccurate corporate action allocations and resulting claims due to late settlements, change of beneficial owner, etc.