How should finance professionals prepare for the future?

Learn how AI/Machine learning will transform the future of finance.

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What critical skills must finance professionals acquire to prepare for the AI-enabled & data-driven future economy?

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What We Do

What We Provide for Your Organization


The deployment of AI in the world of finance is already becoming increasingly mainstream. In the past decades, AI innovation has fundamentally transformed the world of finance presenting opportunities and challenges for finance professionals. From robotic process automation driven by machine learning and natural language processing (“NLP”) including large language models (generative AI) to improve efficiency through strategic risk management in financial operations and fraud prevention, AI will drive the future of finance.


Finance professionals must acquire new skills in managing AI, failing which they are likely to lose their personal professional competitive advantage. This ”AI In Finance: Delivering Business Value“ course seeks to equip participants with the knowledge and skills to manage the design, development and deployment of AI solutions in finance. The course seeks to equip participants with the fundamentals of AI technologies to enable them to manage the design, development of AI-enabled systems in financial operations in any sector of the economy.


Our Founder

Transforming the Next Generation of Finance Professionals
Industry-Based Training Platform to Prepare for the AI-Enabled Economy
Adding Value to the World of Finance with AI/Machine Learning in Finance
Improving Efficiency of Financial Operations for the Future Data-Driven Economy
About Us

Our Founder

Zaid Hamzah is an AI and data strategist and practitioner specializing in AI in law and finance, AI and data governance and AI in information security. An AI patent holder, he is currently an Executive Education Fellow at the National University of Singapore (NUS) School of Computing’s Advanced Computing for Executives centre where he runs programmes on AI Innovation Management, Intellectual Property Rights in AI Innovation, and Commercialisation of AI Innovation. He has taught legal AI at the Singapore Management University School of Law. Zaid is deeply involved in AI R&D and innovation. His AI patent relating to risk management in a supply chain context was granted by the Intellectual Property Office of Singapore in December 2023. He is currently involved in AI research and innovation in the area of decision intelligence and cybersecurity.

Author of 10 books spanning law, technology, intellectual property and AI, Zaid has over 35 years of professional experience. He has previously served as (i) Microsoft's Director for intellectual property and commercial software; (ii) Senior Legal Advisor to Singtel’s joint venture with Warner Bros and Sony Pictures; (iii) Chief Regulatory Legal & Compliance Officer at publicly-listed Telekom Malaysia; (iv) Associate, Khattar Wong & Partners (Singapore law firm) and (v) Singapore Government service. Zaid has a law degree from the National University of Singapore and completed his Masters in International Relations at the Fletcher School of Law and Diplomacy, Tufts University on a Fulbright scholarship.

Zaid is a member of the Board of Directors, NIE International (which is wholly owned by Nanyang Technological University). Zaid volunteers his professional services to start ups in the area of AI management and intellectual property protection and regularly conducts community programs on how to get young children ready for a data and AI future.

What We Do

Our Program

AI in Finance Course:
Delivering Business Value

This course seeks to equip participants with the knowledge and skills
to manage the design, development and deployment of AI solutions in finance.
Participants will learn the fundamentals of AI technologies to enable them
to manage these tasks in their financial operations.




Course Information







The Context


The deployment of AI in the world of finance is already becoming increasingly mainstream. In the past decades, AI innovation has fundamentally transformed the world of finance presenting opportunities and challenges for finance professionals. From robotic process automation driven by machine learning and natural language processing (“NLP”) including large language models (generative AI) to improve efficiency through strategic risk management in financial operations and fraud prevention, AI will drive the future of finance. Finance professionals must acquire new skills in managing AI, failing which they are likely to lose their personal professional competitive advantage.


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Course Objectives


This course seeks to equip participants with the knowledge and skills to manage the design, development and deployment of AI solutions in finance. The course seeks to equip participants with the fundamentals of AI technologies to enable them to manage the design, development of AI-enabled systems in financial operations in any sector of the economy.


This is a non-technical course that does not involve any coding or programming1.


The course covers the following 6 modules:

Module 1: Introduction to AI and Data Science
Module 2: Application of AI in Finance Today
Module 3: Planning and Managing AI in Finance Projects
Module 4: Managing AI Innovation in Finance
Module 5: Managing AI Pitfalls and Ethical Considerations
Module 6: Future of AI in Finance: Management Perspectives


The 6 Modules are organized based on the following flow:

Understand (Modules 1 and 2) → Plan (Module 3) → Act (Module 4) → Reflect and Review (Modules 5 and 6) — referred to as “UPARR” approach.


1Participants will be given an overview of the technical process including an understanding of the main programming languages Python and R (the two most popular for AI solutions development).


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Target Audience


  • Senior finance professionals and business decision-makers seeking to learn how AI can improve business performance and deliver value to their organizations.
  • Mid-senior stage finance professionals looking to develop new skills in AI technology deployment and management for high-growth career prospects.
  • Senior Finance Directors, CFO’s, Business Owners, Accountants, Financial Managers, Financial Consultants, Financial Analysts, Auditors, Accounts Managers, Sales Managers, Treasurers, Asset Managers, Financial Services Professionals seeking to understand and apply how AI can improve decision-making in finance.


Prerequisite Knowledge

  • As this course is for finance professionals, participants should have a solid understanding of finance.
  • No prior knowledge and experience of AI applications or coding is required.


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Course Approach


This course takes a practical and industry-oriented approach with real world context. It is not an “academic” or theoretical course. While the basic theories are covered, the emphasis is on real world application of the knowledge and skills to solve problems and add value at the workplace.


Through carefully curated exercises, this course seeks to get participants to apply what they have learnt and apply their new knowledge and skills at their workplace to drive business performance and deliver value to their organizations. Because of the rapid developments in the world of AI, the course contents will be constantly updated to take into account the latest state of the art AI technologies to equip participants with evolving knowledge and skills “live”.


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Key Competencies or Exit-Level Outcomes


Learning Outcomes

By attending this course, participants will be able to:


  1. Understand the fundamentals of how AI and data science work in the finance context and to frame the right questions.
  2. Apply the concepts and principles of AI in finance for better decision making and to deliver business value.
  3. Design, develop and implement AI in finance projects at the firm level to deliver higher performance.
  4. Manage AI project risks by taking into account ethical considerations and regulatory compliance requirements.
  5. Manage the end to end AI innovation process from ideation, protection of innovative products and services and commercial rollout.
  6. Anticipate future trends in AI in finance to enable organizations to be ready both strategically and operationally.


Business Impact

To achieve business impact at the workplace and to ensure the desired corporate outcomes2, the contents of this course and the application of knowledge and skills gained in this course will focus on how participants can apply what they learn during the online course at the workplace.


2Using Kirkpatrick levels 3 and 4 method of evaluation.


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Course Outline

 
Module Topic
Module 1    Introduction to AI and Data Science
Module 2 Application of AI in Finance Today
Module 3 Planning and Managing AI in Finance Projects
Module 4 Managing AI Pitfalls, Ethical Considerations and Regulatory Compliance
Module 5 Managing AI Innovation in Finance
Module 6 Future of AI in Finance: Management Perspectives


Note: The “heart” of the course are Modules 2, 3 and 4 where the focus is on how AI is applied in finance today followed by the practical aspects of planning and implementing AI in finance projects. Module 2 focuses on the knowledge and skills of applying AI in finance today while Module 3 focuses on execution approaches to deliver business value. Module 4 covers critical issues like ethics, governance and the need to ensure compliance with the law.


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Module 1: Introduction to AI and Data Science

 
Exit Learning Outcome Able to understand the fundamentals of how AI and data science work in the finance context and to frame the right questions.
Learning Outcomes (Unit Level)
  • Able to identify global trends today in the use of AI in finance.
  • Understand how AI works and be able to apply the workings of AI in daily operations in finance including understanding AI limitations.
  • Able to frame and ask the right questions to better manage data science resources.
Module Breakdown Unit 1: AI in Finance: The Strategic Context
  • Evolution of finance technology from Lotus 123, excel based micro, standalone GL system, ERP, cloud computing/SaaS model, API, AI/Blockchain, Web 3.0 etc.
  • Big data in the world of finance and the rise of fintech
  • State of the AI in Finance market today
  • Concept of open finance and decentralized finance (DeFi)
  • AI-enabled innovation in the financial sector
  • The role of blockchain and the AI in finance context

Unit 2: Fundamentals of AI and Data science
  • Human intelligence and AI
    • General AI versus narrow AI
    • AI and cognitive computing
      • Computational thinking in finance
  • How AI works
    • Definitions and scope: AI and Data Science
    • Sub-sets or techniques in AI
      • Machine learning
        • How AI systems learn
        • Types of machine learning: supervised, unsupervised and reinforcement learning
        • Types of machine learning algorithms
        • Artificial neural networks and deep learning
      • “Traditional” AI versus “Generative AI”
      • Natural language processing
        • Large language models & generative AI
    • Understanding the AI lifecycle
      • Components of the AI systems
        • Data, algorithms, operating environment
  • Data and AI lifecycle management
    • Types of data
      • Structured, unstructured, semi-structured
    • Types of analytics (descriptive, diagnostic, predictive, prescriptive)
    • CRISP-DM (CRoss Industry Standard Process for Data Mining)
    • Handling qualitative data
      • Integrating qualitative data into quantitative/historical data to improve future decision making
    • Machine learning operations (MLOps)
    • AI objectives and its uses
      • Improve efficiency and productivity through automation
      • Better and faster analysis for better decision making in both risk management and value creating context
      • Predict better outcomes
    • How AI solutions are deployed
      • Conversational AI
      • Recommendation engine
      • Demand/Time series forecasting
      • Fraud/anomaly detection
      • Back office and robotic process automation
      • Autonomous systems
      • Image recognition
    • Robustness and resilience of AI models
      • Training and testing performance
    • Limitations in AI
      • “Garbage in garbage out” principle
      • Potential bias in algorithm development
      • Explainability in AI algorithms
      • Black box and AI performance issues
  • People in data and AI projects and their roles
    • Data engineers, machine learning engineers, data scientists
    • Software engineers, statisticians and mathematicians
    • Domain experts
  • How AI and Finance Intersect
    • Suitability of AI in Finance
    • Data science thinking in finance
    • Managing the data and AI lifecycle in finance
    • Managing performance in AI deployment in finance


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Module 2: Application of AI in Finance Today

 
Exit Learning Outcome Able to apply the concepts and principles of AI in finance for better decision making and to deliver business value.
Learning Outcomes (Unit Level)
  • Able to identify and apply use cases of AI in finance today.
  • Able to design and develop business models for the application of AI in finance.
  • Learn from industry case studies and to be able to apply such learnings to the learners’ own organization.
Module Breakdown Unit 1: Application of AI in finance today
  • AI application in finance across industries (manufacturing, services etc.)
  • AI application in the banking and financial sector
    • Credit intermediation and assessment of creditworthiness
    • Algorithmic trading
    • Portfolio allocation in asset management
    • Blockchain-based finance
  • AI application in finance (functional emphasis)
    1. Financial accounts management
      1. Financial advisory services
      2. Managing finances and personalized banking
    2. Enhancing customer and stakeholder interactions
    3. Improving productivity through robotic process automation
    4. Improved forecasting via AI
    5. Risk management
      1. Fraud detection, management and prevention
        1. Reducing false positives and human errors
      2. Loan risk assessment
      3. Preventing cyberattacks
    6. Credit decisions
    7. Trading
    8. Underwriting
  • Business models in AI in finance
    • Open data and open finance
    • Platform models

Unit 2: Case Studies
  • Banking Case Studies: DBS Bank, JP Morgan
  • Finance Departments Case Studies
  • SME Case Study: SSA Academy Pte Ltd


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Module 3: Planning and Managing AI in Finance Projects

 
Exit Learning Outcome Able to design, develop and implement AI in finance projects at the firm level to deliver higher performance.
Learning Outcomes (Unit Level)
  • Able to design and plan AI-based projects to drive efficiencies in financial operations, enhance risk management approaches and deliver business value as part of an overall business strategy.
  • Able to oversee the development of AI solutions for implementation in financial operations.
  • Able to manage performance issues from both the business and technical perspectives.
Module Breakdown Unit 1: AI Strategy at the Organizational Level
  1. Business understanding and management
    1. Business value creation context for AI development projects
    2. Business leaders and AI practitioners must ask the right questions before embarking on AI projects
  2. What is enterprise AI?
    1. Building blocks of AI projects
  3. Measuring value creation from the start
    1. Understanding customer lifetime value
  4. Talent management
    1. Upskilling and managing teams
    2. Managing the domain experts
    3. Collaborating with the technical teams – managing and supervising technical teams
    4. Data scientist hiring and retention
    5. Organizational structure
    6. Engagement process

Unit 2: Managing AI in Finance Projects: Practical Steps
  1. Establish framework for AI projects
    • Operating model for AI initiatives
      1. Hub and spoke
      2. Centralized approach
      3. Decentralized approach
  2. Defining the project focus – define and design the solution offer
    • The What, Why and How of a project plan
    • Balance scorecard
    • Building an AI project plan
  3. Build/Assemble or buy considerations
    • Leveraging pre-built AI
    • Buy end to end or stitch together best of breed tools
  4. How to deal with multiple vendors
    • Managing fears of vendor lock-ins
  5. Project-based Services and Managed Service Model
  6. Deciding which technology to use
  7. Project Costings and ROI management
    • Cost and benefit analysis
    • Total cost of ownership (TCO)
      1. How to reduce TCO in AI projects
      2. Cost optimization approaches
    • Understanding use case and feasibility
    • Measuring ROI
  8. AI project execution and machine learning ops (MLOps)
    • Data curation and governance
      1. Data cleaning and preparation
    • Model building
      1. Training and testing models
      2. Managing production
    • Operationalization – deploy to production environment
      1. Model maintenance
      2. Model reviews and approvals
      3. Deploying models and monitoring models in production
    • Leveraging open source
  9. Prototyping
    • Designing the prototype
    • Scrum overview
    • Develop feedback loop
    • Designing the prototype
    • Technology selection
    • Cloud API and microservices
    • Internal APIs
  10. Model Management
    • Building a model library
    • Quantifying model performance
  11. Production
    • Continuous integration
    • Automated testing
    • Ensuring a robust AI system
    • Cloud deployment paradigms
    • Cloud API’s Service Level Agreements
  12. Managing cybersecurity threats in AI projects
  13. Ownership & accountability
    1. Human feedback loop
    2. Finance function can own & drive AI projects with the help of technology
    3. Learn simple ML code and become self-sufficient on making changes to the AI rule engine
    4. Own the AI framework for finance function

Unit 3: Managing Performance Issues in AI Projects
  1. Defining performance in the context of AI projects
    • Data quality challenges
  2. Quantifying model performance
    • Metrics to measure business performance outcomes for AI Projects
    • Metrics to measure algorithmic performance
  3. Build reliable and resilient AI assets
  4. Understanding the limits of AI
  5. Metrics to measure ROI in AI projects and dealing with unclear ROI
  6. Optimization and growth trajectory


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Module 4: Managing AI Pitfalls, Ethical Considerations and Regulatory Compliance

 
Exit Learning Outcome Able to manage AI project risks by taking into account ethical considerations and regulatory compliance requirements.
Learning Outcomes (Unit Level)
  • Able to manage pitfalls in AI projects to ensure that the outcomes of AI projects meet the requirements of stakeholders.
  • Able to reflect on AI ethical issues and apply these principles to ensure explainability of AI algorithms, transparency and accountability in AI in finance projects.
  • Able to design and develop an AI governance policy at the corporate levels.
  • Able to manage regulatory compliance issues from the perspective of both the regulator and the regulated.
Module Breakdown Unit 1: Managing AI ethical risks – strategic and operational context
  • Role of management teams in managing AI ethics and governance issues
    • Explainable AI – avoiding bias and ensuring accountability and transparency
    • Data concentration and competition in AI-enabled financial services and products
    • Risk of bias, discrimination and unfair treatment as a result of poor data quality
    • Difficulty to supervise AI algorithms and machine learning models
  • Regulatory Compliance Requirements
    • Are there laws and regulations relating to AI ethics and governance that must be complied with?
  • Regulations relating to data protection and privacy
    • Regulatory compliance practice

Unit 2: AI Governance Good Practices - Practical implementation steps
  • Governance of AI systems and accountability
    • Rules and policies
  • AI algorithm quality assurance process
  • Data governance – the foundation of AI governance
    • Traditional data governance issues: Data provenance and lineage, data security, reference and master data management, data architecture, metadata management
  • Model governance arrangements
  • Optimizing AI through control and collaboration
  • Regulatory considerations

Unit 3: Regulatory Aspects of AI Project Implementation
  • Policy considerations in the development of AI policy framework
  • Perspectives from the regulators
  • Perspectives from the regulated


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Module 5: Managing AI Innovation in Finance

 
Exit Learning Outcome Able to manage the end to end AI innovation process from ideation, protection of innovative products and services and commercial rollout.
Learning Outcomes (Unit Level)
  • Able to manage the AI innovation process in the finance sector including managing technological innovation with the emergence of Web 3.0.
  • Able to protect AI innovation in the form of intellectual property rights protection and be able to commercialize AI innovations.
Module Breakdown Unit 1: Driving Innovation through AI
  • Exploring the human/AI continuum
    • Ideation and becoming an innovation-focused organization
    • Coming up with ideas and value analysis
      • Ideas bank
      • Brainstorming
  • Managing human-machine interface to drive innovation
  • Balancing innovation and creativity with productivity
  • Applying design thinking in AI innovation
  • Nurturing CFO 3.0 (from 1.0 to 2.0) through innovation

Unit 2: Protecting and Commercializing AI Innovation
  • Intellectual property rights strategy and management
    • Copyright
    • Patent
    • Trade Secret
    • Design Rights
  • Commercialization of AI Products and Services
    • Pricing strategy and corporate finance approaches
    • Understanding customer needs before pricing
    • Commercialization approaches
      • Licensing
      • Outright sale
      • Embedding AI into devices
      • AI-enabled services


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Module 6: Future of AI in Finance – Management Perspectives

 
Exit Learning Outcome Able to anticipate future trends in AI in finance to enable organizations to be ready both strategically and operationally.
Learning Outcomes (Unit Level)
  • Able to sense and carry out horizon scanning of prevailing trends in the future of AI in finance including impact of crypto currency, blockchain-based finance, distributed data on Web 3.0 as well as addressing new or novel ways of doing business.
  • Able to reflect on future challenges in the field of AI in finance and proactively manage future impact through future-ready teams.
Module Breakdown Unit 1: AI in finance: The future trajectory
  • Web 3.0, intelligent workplace of the future (finance function focus)
  • Exploring human-AI continuum in AI innovation
  • Revisiting general AI versus narrow AI
    • Artificial neural network and deep learning in finance
  • AI and blockchain – making sense of DeFi (decentralized finance)
  • Quantum computing and future of AI in finance
  • Can an AI CFO be a reality?

Unit 2: Strategic Management Challenges in the Future of AI in Finance
  • Accelerating AI maturity and value
  • Measuring AI in finance maturity
  • Strategic AI governance – remains a challenge
  • Change management and nurturing a future-ready team
    • Managing new ways of doing things in AI in finance
    • Talent management in AI projects
      • AI capabilities framework
      • Reskilling, upskilling and deep skilling
      • Developing strategic foresight capabilities
      • Nurture agility and adaptability
      • Nurturing the next generation of CFOs


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Learning Strategy

Our PEbAAL Pedagogy


At FinanceFuture.ai, our learning strategy emphasises experiential, performance-based, adaptive and agile learning which we term PEbAAL (Performance-based, Experiential, Adaptive, Agile Learning).


The PEbAAL pedagogy will be applied across all our learning platforms that integrates content, pedagogical strategies and real world lifelong learning. For example, when a student learns either through face to face learning, e-learning or hybrid learning, the learning experience will go beyond the classroom and into the workplace.


We require our students to be exposed to real world setting from the time they start school and continuing their lifelong learning journey. Likewise, when a practising lawyer, in-house counsel or business executive learns through our platform, the emphasis is on real world experience that would boost corporate or organizational performance.


The PEbAAL pedagogy works this way:

1. Identify the Problem

Using design thinking methodology, we start by identifying the problem to be solved or the value to be created.

We carry out a user needs analysis to figure out what needs to be done in order to solve a problem or create value.

2. Develop a Learning Roadmap

If a problem can be solved through learning and development, we then develop a learning roadmap geared towards solving the problem.

If the problem cannot be solved through learning problems (for example, personality clashes), we do not proceed with the PEbAAL approach but attempt to solve the problem through face to face human interactions.

3. Elements of the Learning Roadmap

The learning roadmap will comprise the following elements:

a. Problem identification

b. User needs analysis

c. Learning strategy design which will focus on adaptive and agile learning

d. Tailored learning contents directed at problem solving

e. Integrated learning and development platform backed by a learning and training management system

f. Evaluation framework using the updated Kirkpatrick evaluation methodology

g. Review and refinement to ensure that the organizational KPIs (key performance indicator) are met

4. Create a Student e-Portfolio

Academic institutions are encouraged to create a student e-Portfolio that would capture the students’ learning experiences which they can share with their future employers or partners.

Organizations that require support to create these cloud based e-Portfolio can contact us.

5. Create a Skills Portfolio

Organizations and enterprises are also encouraged to create their own Skills Portfolio for their employees and this can be connected with our knowledge and skills bank that is constantly updated as part of our adaptive and agile learning.

Services

Our Products & Services

We develop comprehensive AI-enabled product and service to enable enterprises,
organizations and governments to achieve improved efficiencies, cost optimization,
enhanced brand perception and promote greater customer centricity.

Platform

We develop for our partners and clients:

AI-Enabled Dashboard as a centralized platform with interactive dashboards for real-time insights and decision-making.

API Gateway for easy integration with existing enterprise systems and third-party applications.

Security and Compliance measures and compliance with data protection regulations.

Technology Services

We work with our partners and clients to design and develop:

Large Language Models (LLMs): We utilize state-of-the-art LLMs like GPT-4 for natural language understanding and generation.

Machine Learning Algorithms: Implement ML algorithms for data analysis, predictive analytics, and anomaly detection.

Computer Vision: Employ computer vision algorithms for image and video analysis.

Cloud Infrastructure: Utilize scalable cloud platforms like AWS, Azure, or Google Cloud for processing and storage.

IoT Integration: Incorporate IoT sensors and devices for data collection and real-time monitoring.

API Strategy

API Documentation: We provide comprehensive API documentation to assist developers in integrating with your AI-powered solutions.

Developer Support: We offer developer support and forums to address technical queries and issues.

Scalability: We help ensure APIs are designed for scalability to accommodate increasing demand.

Process

Data Collection and Integration: We develop processes to gather data from various sources within the enterprise, including operational data, customer data, and market data.

Data Preprocessing: We support our partners and clients to clean, normalize, and transform data to make it suitable for analysis.

Predictive Modeling: We help build machine learning models for demand forecasting, quality control, and resource allocation.

Customer Feedback Loop: We establish a process for continuous feedback analysis from customer interactions to enhance product/service offerings.

Optimization Framework: We help implement algorithms for resource optimization, supply chain management, and pricing strategies.

Agile Development: We support our partners and clients to adopt agile methodologies for software development to respond quickly to changing market demands.

Data

We work with our partners and clients to manage the following:

Data Lake and Data Warehouse: Store and manage data in a centralized data lake or data warehouse for easy access and analysis.

Data Quality Assurance: Implement data quality checks and data governance practices to ensure data accuracy.

Data Analytics: Utilize data analytics tools for in-depth analysis of historical and real-time data.

Data Monetization: Explore opportunities to monetize data by offering insights to partners or customers.

Our Approach

Customer Centricity is Key

We work with our partners and clients on strategies and programs on:


Personalization

How to use AI to personalize customer experiences and recommendations.

Chatbots/Virtual Assistants

How to implement AI-driven chatbots and virtual assistants to enhance customer support.

Sentiment Analysis

How to analyze customer sentiment through NLP to improve products and services.

Feedback Loop

How to create a feedback loop with customers to iterate and enhance offerings.

Talent Development

Building Your Most Important Resource

We work with our partners and clients to develop talents on:

AI Training and Skill Development

Invest in training employees to understand and leverage AI technologies.

Cross-Functional Teams

Form cross-functional teams including data scientists, engineers, business analysts, and domain experts.

Change Management

Implement change management strategies to ensure smooth adoption of AI-driven processes.

Continuous Learning

Encourage a culture of continuous learning and adaptation to stay updated with evolving AI technologies.

Contact

Email Us

To contact us regarding our offerings, email Zaid Hamzah (Mr),
our Founder, at [email protected].


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