Generative AI in Finance: Forecasting, Fraud & Regulation Training Course
Generative AI encompasses artificial intelligence techniques designed to create new content or predictions derived from existing data, including Large Language Models (LLMs) and Generative Adversarial Networks (GANs).
This instructor-led, live training (available online or onsite) is tailored for beginner to intermediate finance professionals eager to leverage generative AI for forecasting, anomaly detection, and regulatory compliance within financial services.
Upon completing this training, participants will be equipped to:
- Grasp the foundational concepts underlying generative AI models.
- Apply LLMs and GANs to practical scenarios such as fraud detection and the creation of synthetic data.
- Craft effective prompts to support financial forecasting and reporting.
- Assess ethical and regulatory aspects associated with generative AI applications.
Course Format
- Interactive lectures and discussions.
- Numerous exercises and practical practice sessions.
- Hands-on implementation within a live-lab environment.
Customization Options
- To arrange customized training for this course, please contact us.
Course Outline
Introduction to Generative AI
- Overview of generative models and their significance in the financial sector
- Types of generative models: LLMs, GANs, VAEs
- Strengths and limitations within financial contexts
Generative Adversarial Networks (GANs) for Finance
- Mechanism of GANs: generators versus discriminators
- Applications in synthetic data generation and fraud simulation
- Case study: producing realistic transaction data for testing purposes
Large Language Models (LLMs) and Prompt Engineering
- How LLMs interpret and generate financial text
- Designing prompts for forecasting and risk analysis
- Use cases: summarizing financial reports, KYC processes, and detecting red flags
Financial Forecasting with Generative AI
- Time series forecasting using hybrid LLM and ML models
- Scenario generation and stress testing
- Use case: predicting revenue by leveraging structured and unstructured data
Fraud Detection and Anomaly Identification
- Utilizing GANs for anomaly detection in transactions
- Identifying emerging fraud patterns through prompt-based LLM workflows
- Model evaluation: distinguishing between false positives and true risk indicators
Regulatory and Ethical Implications
- Ensure explainability and transparency in generative AI outputs
- Addressing the risks of model hallucination and bias in finance
- Adhering to regulatory expectations (e.g., GDPR, Basel guidelines)
Designing Generative AI Use Cases for Financial Institutions
- Developing business cases for internal adoption
- Balancing innovation with risk management and compliance
- Establishing governance frameworks for responsible AI deployment
Summary and Next Steps
Requirements
- A solid understanding of fundamental finance and risk management concepts
- Experience with spreadsheets or basic data analysis
- Familiarity with Python is advantageous but not mandatory
Target Audience
- Risk managers
- Compliance analysts
- Financial auditors
Open Training Courses require 5+ participants.
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Testimonials (1)
Trainer was very knowledgeable and easy to speak to
Gareth Gird - Teleflex Medical Europe Ltd
Course - Copilot for Finance and Accounting Professionals
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