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CFTE

Generative AI for Research Analyst in Financial Services

  • up to 6 weeks
  • Intermediate

This program enables research analysts in financial services to leverage Generative AI’s benefits responsibly. Over six weeks, participants will gain hands-on experience with AI tools, understand the current landscape, and learn to drive AI strategy in their teams.

  • Generative AI fundamentals
  • AI applications in research
  • Data extraction and report summarization
  • Ethical considerations in AI
  • Global AI regulations

Overview

The course provides a comprehensive understanding of Generative AI in research and analytics. Participants will learn the fundamentals of Generative AI, explore real-world applications, and gain practical skills in using AI tools for data extraction and report summarization. The program also covers ethical considerations and global regulations, equipping learners to implement AI strategies effectively in their organizations.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Market Research Analysts

Professionals who analyze market conditions to examine potential sales of a product or service.

Data & Financial Analysts

Experts who analyze financial data and trends to provide insights and recommendations.

Business Intelligence (BI) Analysts

Specialists who transform data into insights to drive business decisions.

This course equips research analysts with the skills to leverage Generative AI, enhancing productivity and providing real-time insights. Ideal for professionals in financial services, it covers key AI tools, applications, and ethical considerations, helping learners stay competitive and drive AI strategy in their teams.

Pre-Requisites

1 / 3

  • Basic understanding of AI and machine learning concepts

  • Familiarity with financial services industry

  • Proficiency in data analysis tools

What will you learn?

Week 1: Generative AI Fundamentals
This week explores the definition, the inner mechanisms, and the evolution of Generative AI.
Day 1: Definition of Generative AI
Understanding what Generative AI is and its basic principles.
Day 2: Differences between traditional Machine Learning and Generative AI
Comparing traditional machine learning methods with Generative AI.
Day 3: Transformers: a revolutionary breakthrough
Exploring the role of transformers in Generative AI.
Day 4: Scaling transformers
Understanding how transformers can be scaled for better performance.
Day 5: LLMs as a new human/machine interface
Examining Large Language Models as interfaces between humans and machines.
Week 2: The Landscape of Generative AI
This week discusses the Generative AI landscape and key players such as infrastructure providers, model providers, and application providers.
Day 6: Model providers
Identifying key model providers in the Generative AI space.
Day 7: Infrastructure providers
Understanding the role of infrastructure providers in Generative AI.
Day 8: Application providers
Exploring various application providers in the Generative AI ecosystem.
Day 9: Hands-on project: Getting started with ChatGPT and Customer Personas
Practical project on using ChatGPT for creating customer personas.
Day 10: Hands-on project: Analyzing & extracting information from PDF Reports with AI
Practical project on using AI to analyze and extract information from PDF reports.
Week 3: Current landscape of Generative AI in Research
This week delves into the Research domain and compares traditional approaches with AI-driven methods.
Day 11: Introduction and background of Research
Overview of the research domain and its importance.
Day 12: Challenges in Research
Identifying key challenges faced in the research domain.
Day 13: Traditional approaches vs AI-driven approaches in Research
Comparing traditional research methods with AI-driven approaches.
Day 14: Current landscape of Generative AI in Research
Examining the current state of Generative AI in research.
Day 15: Expert interview: TBC
Interview with an expert in the field (To Be Confirmed).
Week 4: Applications and use cases of Generative AI in Research
This week discusses Generative AI applications and current initiatives and use cases in Research.
Day 16: Applications of Generative AI in Research (1/2)
Exploring various applications of Generative AI in research.
Day 17: Applications of Generative AI in Research (2/2)
Continuing the exploration of Generative AI applications in research.
Day 18: Case studies (1/2)
Examining case studies of Generative AI applications in research.
Day 19: Case studies (2/2)
Continuing the examination of case studies in Generative AI applications.
Day 20: Expert interview (TBC)
Interview with an expert in the field (To Be Confirmed).
Week 5: From Theory to Practice: Practical Approach
This week focuses on practical applications of generative AI tools in Research.
Day 21: Navigate key Generative AI tools
Learning to navigate and use key Generative AI tools.
Day 22: Understanding the Prompt Engineering
Understanding the concept and importance of prompt engineering.
Day 23: Use case
Practical use case of Generative AI in research.
Day 24: Use case
Another practical use case of Generative AI in research.
Day 25: Use case
Additional practical use case of Generative AI in research.
Day 26: Use case
Final practical use case of Generative AI in research.
Week 6: Opportunity and risk in Research
This week examines the advantages and key success factors of implementing AI in Research and teaches the privacy and data usage in Generative AI in Research.
Day 27: Advantages / Key Success Factors of AI implementation in Research
Identifying the advantages and key success factors of AI implementation in research.
Day 28: Impact and assessment
Assessing the impact of AI in research.
Day 29: Data, Risk, Privacy and Ethical considerations
Understanding data management, risk, privacy, and ethical considerations in AI.
Day 30: Changes in skills
Examining the changes in skills required for AI-driven research.

What learners say about this course

  • “The content was really relevant. I really liked that it gave a lot of examples on how AI can be applied in the industry, the companies that are successful implementing AI and high level view on what AI is really about.”

    Magdalene Loh

    Senior Vice President and Head of Innovation at Prudential Singapore

  • “The interviews covered in the course introduced the insights that are very important from those with 20+ years experience professionals.”

    Priscilla Cournede

    Deputy Director at Life Reinsurance at Covéa

  • “Since I finished the course, I raised suggestions to my team to integrate different technologies and AI in the different parts of the organisation.”

    Goh Theng Kiat

    Chief Customer Officer at Prudential Singapore

Meet your instructors

  • Huy Nguyen Trieu

    CEO, The Disruptive Group

    Huy Nguyen Trieu is a co-founder of CFTE, a global Fintech education platform. He is also the CEO of The Disruptive Group, a firm that builds innovative finance businesses with a strong leverage on technology and advises CEOs of large organisations.

  • Yangchen Huang

    Staff AI Researcher (GenAI/LLM), AlphaSense

    Yangchen Huang is a Staff AI Researcher at AlphaSense, focusing on GenAI/LLM R&D. Her research and professional experience include machine learning, deep learning, NLP, and computer vision.

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