Course Outline
Introduction to Multimodal AI for Finance
- Overview of multimodal AI and its applications in the financial sector
- Types of financial data: structured versus unstructured
- Challenges in adopting AI within financial institutions
Risk Analysis with Multimodal AI
- Fundamentals of financial risk management for government and private sector entities
- Utilizing AI for predictive risk assessment
- Case study: AI-driven credit scoring models
Fraud Detection Using AI
- Common types of financial fraud and their impact on public sector operations
- Advanced AI techniques for anomaly detection in financial transactions
- Strategies for real-time fraud detection to enhance security and compliance
Natural Language Processing (NLP) for Financial Text Analysis
- Extracting actionable insights from financial reports and news articles
- Sentiment analysis for predicting market trends and investor behavior
- Leveraging large language models (LLMs) for regulatory compliance and auditing processes
Computer Vision in Finance
- Using AI to detect fraudulent documents and enhance document verification
- Analyzing handwriting and signatures for authentication purposes
- Case study: Implementing AI-driven check verification systems for government
Behavioral Analysis for Fraud Detection
- Monitoring customer behavior using AI to identify potential fraud
- Biometric authentication methods and their role in preventing financial crimes
- Analyzing transaction patterns to detect suspicious activities and enhance security measures
Developing and Deploying AI Models for Finance
- Data preprocessing techniques and feature engineering for financial data
- Training AI models tailored to financial applications
- Deploying AI-based fraud detection systems in financial institutions
Regulatory and Ethical Considerations
- Ensuring AI governance and compliance within financial institutions for government and private sectors
- Addressing bias and fairness issues in financial AI models
- Best practices for responsible AI use in the financial industry
Future Trends in AI-Driven Finance
- Advancements in AI for financial forecasting and decision-making
- Emerging AI techniques to prevent fraud and enhance security
- The evolving role of AI in the future of banking, investments, and public sector finance
Summary and Next Steps
Requirements
- Fundamental knowledge of artificial intelligence and machine learning principles
- Proficiency in understanding financial data and risk management practices
- Experience with Python programming and data analysis techniques
Audience
- Finance professionals for government
- Data analysts
- Risk managers
- AI engineers in the financial sector
Testimonials (3)
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
it has opened my mind to new tool that can help me in creating automation
Alessandra Parpajola - Advanced Bionics AG
Course - Machine Learning & AI for Finance Professionals
I very much appreciated the way the trainer presented everything. I understood everything even if Finance is not my area, he made sure that every participant was on the same page, while keeping up with the time left. The exercises were placed at good intervals. Communication with the participants was always there. The material was perfect, not too much, not too little. He elaborated very well on a bit more complicated subjects so that it can be understood by everyone.
