Generative AI in Finance: Forecasting, Fraud & Regulation Training Course
Generative AI refers to a category of artificial intelligence methods designed to create new content or predictions based on existing data, encompassing technologies such as Large Language Models (LLMs) and Generative Adversarial Networks (GANs).
This instructor-led live training, available online or onsite, is tailored for finance professionals at beginner to intermediate levels who aim to leverage generative AI for forecasting, anomaly detection, and compliance within the financial services sector.
Upon completion of this training, participants will be equipped to:
- Grasp the core concepts underpinning generative AI models.
- Utilize LLMs and GANs for applications such as fraud detection and the creation of synthetic data.
- Craft effective prompts to support financial forecasting and reporting tasks.
- Assess the ethical and regulatory aspects associated with generative AI implementations.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical sessions.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- For tailored training requirements, please get in touch with us to arrange.
Course Outline
Introduction to Generative AI
- Overview of generative models and their significance in finance
- 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: creating realistic transaction data for testing purposes
Large Language Models (LLMs) and Prompt Engineering
- How LLMs interpret and generate financial text
- Developing prompts for forecasting and risk analysis
- Use cases: summarizing financial reports, KYC processes, and red flag detection
Financial Forecasting with Generative AI
- Time series forecasting using hybrid LLM and ML models
- Scenario generation and stress testing
- Use case: revenue prediction leveraging structured and unstructured data
Fraud Detection and Anomaly Identification
- Employing GANs for anomaly detection in transactions
- Identifying emerging fraud patterns through prompt-based LLM workflows
- Model evaluation: distinguishing false positives from true risk indicators
Regulatory and Ethical Implications
- Explainability and transparency in generative AI outputs
- Risks of model hallucination and bias in finance
- Compliance with regulatory standards (e.g., GDPR, Basel guidelines)
Designing Generative AI Use Cases for Financial Institutions
- Developing business cases for internal adoption
- Balancing innovation with risk and compliance
- Governance frameworks for responsible AI deployment
Summary and Next Steps
Requirements
- A fundamental understanding of finance and risk management principles
- Experience with spreadsheets or basic data analysis
- Familiarity with Python is advantageous but not mandatory
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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