Multimodal AI for Finance

• Basic knowledge of AI and machine learning concepts
• Understanding of financial data and risk management
• Experience with Python programming and data analysis

Audience

• Finance professionals
• Data analysts
• Risk managers
• AI engineers in the financial sector

Multimodal AI for finance integrates multiple data types—such as transaction logs, textual reports, customer interactions, and behavioral patterns—to improve risk assessment and fraud detection.

This instructor-led, live training (online or onsite) is aimed at intermediate-level finance professionals, data analysts, risk managers, and AI engineers who wish to leverage multimodal AI for risk analysis and fraud detection.

By the end of this training, participants will be able to:

• Understand how multimodal AI is applied in financial risk management.
• Analyze structured and unstructured financial data for fraud detection.
• Implement AI models to identify anomalies and suspicious activities.
• Leverage NLP and computer vision for financial document analysis.
• Deploy AI-driven fraud detection models in real-world financial systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Multimodal AI for Finance

• Overview of multimodal AI and its financial applications
• Types of financial data: structured vs. unstructured
• Challenges in financial AI adoption

Risk Analysis with Multimodal AI

• Fundamentals of financial risk management
• Using 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

Natural Language Processing (NLP) for Financial Text Analysis

• Extracting insights from financial reports and news
• Sentiment analysis for market prediction
• Using LLMs for regulatory compliance and auditing

Computer Vision in Finance

• Detecting fraudulent documents with AI
• Analyzing handwriting and signatures for authentication
• Case study: AI-driven check verification

Behavioral Analysis for Fraud Detection

• Tracking customer behavior with AI
• Biometric authentication and fraud prevention
• Analyzing transaction patterns for suspicious activities

Developing and Deploying AI Models for Finance

• Data preprocessing and feature engineering
• Training AI models for financial applications
• Deploying AI-based fraud detection systems

Regulatory and Ethical Considerations

• AI governance and compliance in financial institutions
• Bias and fairness in financial AI models
• Best practices for responsible AI use in finance

Future Trends in AI-Driven Finance

• Advancements in AI for financial forecasting
• Emerging AI techniques for fraud prevention
• The role of AI in the future of banking and investments

Summary and Next Steps