Generative AI in Finance and Banking

Become an expert in the use of AI in banking and finance. Master its applications, key technologies, AI agents, ethical considerations and compliance, with a focus on enhancing decision-making, efficiency, and innovation in financial services.

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A one-day AI training for finance professionals presented over two-half days in a virtual class from 9:30am to 1:00pm UK time

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Download: Course Outline

Part One

Module 1: Introduction to Generative AI in Financial Services

By the end of the module, you will understand:

What is Generative AI (GenAI)?
- What are the tools and technologies available under Generative AI?
What are the factors driving the growth and implementation of Generative AI in Finance and Banking?
What is the emerging role of Generative AI? What is AI in banking and financial services?
- What are the strategies adopted by banks and financial initiations to implement Generative AI?
What are the examples of applications of Generative AI in Finance - Fraud Detection & Prevention, Credit Scoring and Risk Modelling, Chatbot and Virtual Assistants, Trading and Investment Strategies in Finance and Banking

Module 2: Data Analytics and AI in Finance and Banking

What are the needs and requirements of Data Analytics and AI in Finance?
- Synergy between Data Analytics and AI in Finance
Combining Data Analytics and AI in Finance:
- What are the benefits and applications in banking and financial services- Risk Management, Credit Scoring Models and Credit Underwriting and Wealth Management
The predictive insights available through the application of Data Analytics and AI in Finance:
- Customer Insights, Streamlining process and operations for efficiency, Personalisation and Risk Management & Compliance Practices
The challenges in implementing AI and Machine Learning Applications in Finance:
- How can Data Analytics help overcome challenges of Data Privacy, Cyber Security and Ethical Considerations?

Module 3: Generative AI and ML Technologies - Overview

What is Generative AI?
- How does Generative AI work?
What are the machine learning (ML) models available under Generative AI- Neural Networks, Generative Adversarial Networks (GAN) and Transformer Models?
- What is the architecture and system design of the machine learning models?
The Mechanics of Generative AI
Prompt Engineering
The use cases and applications of Generative AI Technologies in Finance
Limitations of Generative AI Technologies and how can we overcome them

Module 4: Advanced Generative AI for Banking and Finance

What is Advanced Generative AI in Finance?
The key benefits and use case examples of Advanced Generative AI in Finance:
What are ChatGPT and Gemini?
- Key features and benefits of these models
The differences in generative AI approaches for these models
What is MS CoPilot for Finance?
- Key features and usages in the financial services industry
What are Perplexity, FinGPT and AI agents?
- Key features and benefits in Finance

Module 5: AI Agents in Finance and Banking

What are AI agents in Finance?
- Their applications, examples and usages in banking and financial services
What is the AgentOps Landscape for AI Agents in Finance?
- How do we implement the AI agents in Finance Operations

Part Two

Module 6: Introduction to Perplexity AI in Finance and Banking

What is Perplexity?
- How does it relate to AI-generated content?
- What are the considerations for researchers?
How can enterprises use Perplexity?
- Streamlining Research and Data Analysis, Enhancing Content Strategy, Industry Insights, Investment Analysis and Scenario Simulations
What is Sentiment Analysis conducted What using AI?
- How to leverage the results of sentiment analysis obtained with AI
- Sentiment Analysis tools available

Module 7: Deep Dive into CoPilot Technology and Architecture – Application in Finance and Banking

What is the Microsoft CoPilot Architecture and Design?
Core Components - User Interface, AI Engine, Data Integration Layer, Security Layer, and Reporting and Analytics
What are the key benefits and usages of the core components of the MS CoPilot architecture?
- How can CoPilot transform system architecture and processes?
How do we create client AI plugins for CoPilot in Finance and Business Operations?
How do we integrate CoPilot with Financial Data Systems?
Case Study – Building a CoPilot Application for Advanced Financial Analytics – CoPilot Stack

Module 8: Case Studies & Real-Life Application

What are the examples and real-life case studies of successful implementation of AI in Finance?
- Fraud Detection and Prevention
- Credit Scoring Models
- Investment Trading
- Personalised Customer Service
- Risk Management
- Regulatory Compliance
What has been the transformative impact of AI in Finance?
- Operational Efficiency
- Improved Customer Experience
- Competitive Advantage
- Accurate Models for Prediction and Speed and Precision

Module 9: Ethical Considerations and Compliance

By the end of this AI in finance syllabus, participants will learn and understand:

The role of Ethics in the application of Generative AI in Finance
What are the Ethical considerations and elements that need to be addressed while implementing AI in Finance?
- Transparency
- Accountability
- Privacy
- Bias
- Security and Systemic Risk
What is AI Compliance? Why is compliance important?
What are the international rules and regulations related to AI Compliance in Finance?
- The EU Artificial Intelligence Act
  - How do you ensure adherence to regulations and compliance with AI in Finance
  - What are the Compliance and Regulatory Considerations for AI in Finance
- The Ethics of AI – Balancing Innovation and Responsibility

Redcliffe’s Generative AI in Finance course is delivered by an expert in credit and fraud risk management, anti-money laundering, regulatory compliance, content creation and delivery as a banking and corporate trainer; with over 25 years of banking industry experience across India and the UAE.

He has delivered training in Middle East region banks and financial institutions over the last 6 years. He has experience across retail and corporate banking, wealth management and commercial lending with a deep understanding of banking data sensitivity, compliance requirements and the implementation of enterprise fraud management systems.

He is currently acting as a Risk Management professional and is a trainer cum faculty at the Emirates Institute of Finance (EIF), UAE for the last 6 years. He is currently pursuing his certification in Anti Money Laundering (ACAMS) and is also trainer faculty with multiple institutes and organisations in India and the Middle East.

He employs a practical, results-oriented methodology that balances technological innovation and banking industry realities. He emphasises the responsible implementation of generative AI within regulatory constraints and creates engaging learning experiences through real-world banking case studies, interactive demos, and hands-on labs tailored to different banking roles and knowledge levels.

The trainer has trained thousands of aspiring bankers, middle management executives and C-suite members during the last 6 years and conducted multiple workshops and training courses including the introduction to retail and corporate banking, credit underwriting and control, insurance frauds and risk management, anti-money laundering and regulatory compliance, treasury solutions and investment portfolios, FATCA and CRS implementation in banks and financial institutions.

His areas of expertise include AI applications and machine learning in banking and finance solutions, as well as responsible AI implementation strategies for financial institutions. He has training creation and delivery experience in developing and delivering comprehensive curricula and masterclasses in Generative AI strategy and implementation in banks, regulatory preparedness sessions on emerging AI governance frameworks, and designing specialised training on prompt engineering for banking-specific applications in the Middle East region.

By the end of this Generative AI in Finance course, you will:

Understand Generative AI and its applications in finance and banking.
Leverage Data Analytics and AI for risk management, credit scoring, and decision-making.
AI & ML Models like GANs, Transformers, and Neural Networks
Be proficient in advanced AI Tools such as ChatGPT, Gemini, FinGPT, and MS CoPilot and implement AI agents in finance and operations.
Apply Perplexity AI & Sentiment Analysis for market research and investment insights.
Integrate CoPilot Technology into financial systems for automation and efficiency.
Analyse real-world AI use cases and case studies in fraud detection, trading, and compliance.
Address ethical and compliance challenges related to AI transparency, bias, and regulations.

This course is a ‘must know’ for:

Finance professionals seeking to understand how generative AI can transform their workflows
Risk managers interested in AI-powered scenario analysis tools empowered by generative AI and stress testing
Financial analysts looking to automate report generation and data synthesis
Investment bankers wanting to leverage AI for market analysis and deal evaluation
Compliance officers interested in AI-assisted regulatory monitoring
Technology leaders in financial institutions responsible for AI implementation
Data scientists in Finance who want to expand their generative AI toolkit
Business strategists who want to explore the intersection of artificial intelligence and finance, and evaluate AI investment opportunities in Finance

Redcliffe Training’s Generative AI in Finance course will show you practical applications of AI technologies across various finance domains. You will learn how financial institutions can leverage large language models, synthetic data generation, and other AI technologies to enhance decision-making, improve efficiency, and create innovative financial products and services.

The training consists of key topics and concepts relevant to finance, the technical aspects of the Generative AI implementation, and identifies the key technologies involved, AI agents in Finance, MS Co-Pilot, Perplexity and generative AI use cases in an organisation.

You will fully understand how to evaluate the risks and limitations of AI implementation in financial contexts and the training will help develop strategic frameworks for responsible AI adoption and collaborate effectively with technical teams for AI initiatives.

The first half of the course covers two modules on the foundations of Generative AI in financial services and the technical aspects of the application of Generative AI in banking and finance. The modules cover the evolution of AI in financial services, the key generative models and their capabilities (LLMs, Diffusive Models and GANs). It also looks at the application of synergy between Data Analytics and Generative which is explored in detail along with the predictive insights and practical challenges in the implementation of AI and ML applications in Finance.

The second half of the training introduces Advanced Generative AI models and applications like ChatGPT, Gemini, MS Copilot and other generative AI agents. The concepts of AI agents in Finance are explained regarding Perplexity and its relation to the AI generated content.

The section on the deep dive into MS Copilot enunciates the architecture of its design and implementation in financial services. The high-impact areas and applications of Generative AI in Finance are discussed along with the ethical and compliance considerations at the end of the training course module.

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