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Generative AI in Finance and Banking

0 Part Course  |  Explore an overview of Generative AI in Finance and Banking, covering its applications, key technologies, AI agents, ethical considerations, and compliance, with a focus on enhancing decision-making, efficiency, and innovation in financial services.

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

PART ONE

Module 1: Introduction to Generative AI in Finance and Banking

By the end of the module, the participants would be able to learn and understand:

  • What is Generative AI
    • 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 What 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 application 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 is the need and requirement 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
  • What are the predictive insights available through the application of Data Analytics and AI in Finance?-
    • Customer Insights, Streamlining process and operations for efficiency, Personalization and Risk Management & Compliance Practices
  • What are 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 Consideration

 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?
  • What is the mechanics of Generative AI
  • What is Prompt Engineering
  • What are the use cases and applications of Generative AI Technologies in Finance
  • What are the limitations of Generative AI Technologies and how can we overcome them?

 Module 4: Advanced Generative AI in Finance and Banking

  • What is Advanced Generative AI in Finance
  • What are the key benefits and use case examples of Advanced Generative AI in Finance?
  • What are ChatGPT and Gemini
    • What are the Key Features and Benefits of these models
  • What are the difference in generative AI approaches for these models
  • What is MS CoPilot for Finance
    • What are its key features and usages in the financial services industry
  • What are Perplexity , FinGPT and AI agents
    • What are their key features and benefits in the Finance

 Module 5: AI Agents in Finance and Banking

  • What are AI agents in Finance
    • What are 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
    • What are the 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
  • What are its 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 – AI in Finance and Banking – 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
    • Personalized 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 – Use of AI and ML in Banking

By the end of the module, the participants would be able to 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 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 of AI in Finance
      • What are the Compliance and Regulatory Considerations for AI in Finance
    • The Ethics of AI – Balancing Innovation and Responsibility

The trainer an expert in credit and fraud risk management and 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 a track record of delivering trainings in the 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 implementation of the enterprise fraud management systems.

He is currently acting as a Risk Management professional and is also 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 organizations in India and the Middle East region.  He employs a practical, results-oriented methodology that balances technological innovation with banking industry realities. He emphasizes responsible implementation of generative AI within regulatory constraints, 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 workshop's and training delivery on courses including Introduction to retail and corporate banking, risk management, 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 solutions for banking use cases, and responsible AI implementation strategies for financial institutions. He has training creation and delivery experience in the areas of developing and delivering comprehensive curriculum and master class in Generative AI strategy and implementation in banks, regulatory preparedness sessions on emerging AI governance frameworks and designing specialized training on prompt engineering for banking-specific applications in the Middle East region.

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

  • Understand Generative AI and its applications in finance and banking
  • Leverage Data Analytics and AI for risk management, credit scoring, and decision-making
  • Explore AI & ML Models like GANs, Transformers, and Neural Networks
  • Use Advanced AI Tools such as ChatGPT, Gemini, FinGPT, and MS CoPilot
  • Implement AI Agents in finance operations and data analysis
  • Apply Perplexity AI & Sentiment Analysis for market research and investment insights
  • Integrate CoPilot Technology into financial systems for automation and efficiency
  • Analyze Real-World AI Use Cases in fraud detection, trading, and compliance
  • Address Ethical & Compliance Challenges related to AI transparency, bias, and regulations

This course equips finance professionals with the knowledge to apply Generative AI effectively.

  • Finance professionals seeking to understand how generative AI can transform their workflows
  • Risk managers interested in AI-powered scenario analysis 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 wanting to expand their generative AI toolkit
  • Business strategists evaluating AI investment opportunities in Finance

This course on Generative Artificial Intelligence (AI) in Finance and Banking explores practical applications of generative AI technologies across various finance domains. The participants 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 the key topics including the key Generative AI concepts relevant to finance, the technical aspects of the Generative AI implementation identifies the key technologies involved, AI agents in Finance, Deep dive into MS Co Pilot, Perplexity and generative AI use cases in an organization.

The course evaluates the risks and limitations of AI implementation in financial contexts. The training helps 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 foundations of Generative AI in finance 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(LLM’s, Diffusive Models and GAN’s). It also looks at the application of synergy between Data Analytics and Generative 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 Co Pilot and other generative AI agents in the market landscape. The concepts of AI agents in Finance is explained with reference to Perplexity and its relation to the AI generated content. The section on the deep dive into MS Co Pilot enunciates the architecture, design and implementation of the MS Co Pilot 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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