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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
14 Hours
Testimonials (3)
Trainers can answer all questions and accept any queries
Dewi Anggryni - PT Dentsu International Indonesia
Course - Copilot for Finance and Accounting Professionals
The background / theory of LLMs, the exercise
Joanne Wong - IPG HK Limited
Course - Applied AI for Financial Statement Analysis & Reporting
Interaction with the audience, not too technical