Generative AI in Finance: Forecasting, Fraud & Regulation Training Course
Generative AI encompasses a range of artificial intelligence techniques designed to produce new content or predictions from existing datasets, including Large Language Models (LLMs) and Generative Adversarial Networks (GANs).
This instructor-led training, available either online or onsite, is tailored for finance professionals ranging from beginner to intermediate levels who aim to leverage generative AI for forecasting, anomaly detection, and compliance within financial services.
Upon completion of this training, participants will be able to:
- Grasp the fundamental concepts underpinning generative AI models.
- Utilize LLMs and GANs for practical applications such as fraud detection and synthetic data creation.
- 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 paired with discussions.
- Ample opportunities for exercises and practice.
- Practical implementation within a live-lab environment.
Customization Options
- For organizations seeking a customized version of this course, please reach out to us to make arrangements.
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 of these models in financial contexts.
Generative Adversarial Networks (GANs) for Finance
- Mechanisms of GANs: distinguishing between generators and 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.
- Strategies for designing prompts for forecasting and risk analysis.
- Practical use cases: summarizing financial reports, KYC processes, and detecting red flags.
Financial Forecasting with Generative AI
- Time series forecasting using hybrid models combining LLMs and machine learning.
- Scenario generation and stress testing techniques.
- Use case: predicting revenue by analyzing both structured and unstructured data.
Fraud Detection and Anomaly Identification
- Leveraging GANs for detecting anomalies in transactions.
- Spotting emerging fraud patterns through prompt-based LLM workflows.
- Evaluating models: distinguishing false positives from genuine risk indicators.
Regulatory and Ethical Implications
- Ensuring explainability and transparency in generative AI outputs.
- Addressing risks related to model hallucination and bias in financial applications.
- Adhering to regulatory expectations, such as GDPR and Basel guidelines.
Designing Generative AI Use Cases for Financial Institutions
- Developing business cases to encourage internal adoption.
- Balancing innovation with risk management and compliance requirements.
- Establishing governance frameworks for the responsible deployment of AI.
Summary and Next Steps
Requirements
- A solid understanding of basic finance and risk management principles.
- Prior experience with spreadsheets or fundamental data analysis.
- Familiarity with Python is beneficial 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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