Course Outline
Introduction to Multimodal AI for Finance
- Overview of multimodal artificial intelligence (AI) and its applications in the financial sector
- Types of financial data: structured versus unstructured
- Challenges in adopting AI within financial institutions for government and private sectors
Risk Analysis with Multimodal AI
- Fundamentals of financial risk management
- Utilizing AI for predictive risk assessment
- Case study: AI-driven credit scoring models
Fraud Detection Using AI
- Common types of financial fraud
- AI techniques for anomaly detection
- Real-time fraud detection strategies for government and private sector applications
Natural Language Processing (NLP) for Financial Text Analysis
- Extracting insights from financial reports and news articles
- Sentiment analysis for market prediction
- Using large language models (LLMs) for regulatory compliance and auditing
Computer Vision in Finance
- Detecting fraudulent documents with AI for government and financial institutions
- Analyzing handwriting and signatures for authentication purposes
- Case study: AI-driven check verification systems
Behavioral Analysis for Fraud Detection
- Tracking customer behavior using AI technologies
- Biometric authentication and fraud prevention methods
- Analyzing transaction patterns to identify suspicious activities
Developing and Deploying AI Models for Finance
- Data preprocessing and feature engineering techniques
- Training AI models for various financial applications
- Deploying AI-based fraud detection systems in financial institutions for government oversight and private use
Regulatory and Ethical Considerations
- AI governance and compliance within financial institutions for government regulatory frameworks
- Addressing bias and ensuring fairness in financial AI models
- Best practices for responsible AI use in the financial sector
Future Trends in AI-Driven Finance
- Advancements in AI for financial forecasting and decision-making
- Emerging AI techniques for enhancing fraud prevention
- The evolving role of AI in the future of banking and investments for government and private sectors
Summary and Next Steps
Requirements
- Fundamental knowledge of artificial intelligence and machine learning concepts for government applications
- Understanding of financial data and risk management principles
- 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.
