Artificial Intelligence in the Financial Sector

Course Info

Code IND02-114

Duration 5 Days

Format Classroom Online

Artificial Intelligence in the Financial Sector

Course Summary

Within the modern world, artificial intelligence and machine learning systems have taken over and became a necessary feature in almost all industries, regardless of sector. This is especially true for the financial sector, as artificial intelligence is perfectly designed to gather and process financial data.
To successfully implement an artificial intelligence system, it is essential to consider the final goal or outcome of the system. These systems can be used to automate simple and tedious processes to relieve the responsibility from employees so they can focus their energy elsewhere but can also replace entire departments within an organisation if programmed effectively. To tailor a system to meet the organisation’s needs, those involved need to have a full understanding on the types of artificial intelligence and machine learning systems.

When preparing for system implementation, there also needs to be extensive planning, designing, and monitoring of its progress. Integration may need to happen over a period of time, to ensure the organisation can carefully adjust to the new technology. Close monitoring post-implementation will also make mistakes easier to notice before they have a chance to cause significant issues.

During this course, you’ll learn:
  1. To understand the rising importance of artificial intelligence and machine learning within the financial sector.
  2. To develop various artificial intelligence financial systems – credit default predictor, fraud detection system, recommender system and customer segmentation system.
  3. To evaluate existing artificial intelligence systems to highlight key structures and processes.
  4. To explain the concepts, principles, and typical architecture of an artificial intelligence system.
  5. To explore the benefits and limitations of artificial intelligence systems.
  6. To create an action plan detailing goals, objectives, innovative ideas, and project stages.
  7. To comprehend the risks associated with a full reliance on a technology system and balance technology with human influence.
This course is designed for anyone within the financial sector who wishes to incorporate artificial intelligence into their standard practices. It would be most beneficial for:
  • Finance Managers
  • Accountants
  • Risk Managers
  • Senior Executives
  • Business Owners
  • Banking Managers
  • Artificial Intelligence and Machine Learning Engineers
  • Operations Managers
This course uses a variety of adult learning styles to aid full understanding and comprehension. Participants will review case studies of established financial organisations who utilise artificial intelligence systems to highlight key features and processes.
Participants will be provided with all the necessary tools and equipment needed to successfully carry out the offered learning exercises. They will partake in a variety of presentations, group discussions, demonstrations, and individual activities. A combination of these activities alongside the supplied case studies will ensure a full development of the related skills and knowledge.

Course Content & Outline

Section 1
  • Fundamentals of Artificial Intelligence
  • Defining what an artificial intelligence (AI) and machine learning (ML) system is.
  • Reviewing the typical uses of an AI system and recognising popular already established in the financial sector.
  • Assessing the concepts, principles and structures of an AI and ML system.
  • Common applications used to create AI systems – python, R and WEKA.


Section 2
  • Machine Learning
  • How machine learning functions are integral to an AI system.
  • Describing the different features of an ML system – independent and dependent variables.
  • Data clustering, classification, and regression and how these should be used.
  • Comparing the two main types of ML system – supervised and unsupervised.
  • The benefits, limitations, and ideal uses for supervised and unsupervised systems.


Section 3
  • Deep Learning and Neural Networks
  • Understanding the structure and purpose of neural networks.
  • How neural networks and deep learning uses data inputs, weights and bias to mimic how the human brain processes information.
  • Integrating deep learning into AI and ML systems.
  • Programming deep learning and neural networks to process financial datasets and organise them appropriately.
Section 4
  • Preparing for AI Implementation
  • Establishing goals and objectives for the AI system.
  • Automating tedious processes with AI to maximise productivity and better utilise resources.
  • Creating action plans detailing the entire creation process from designing to post-implementation monitoring.
  • Encouraging openness to change within the working environment.
  • Exploring the ethical challenges faced with integrating AI into an organisation.
Section 5
  • Developing Financial Oriented Systems
  • Understanding the natural system language and generation.
  • What type of system is most suitable for processing and presenting financial data.
  • Evaluating the associated risks with automating financial processes.
  • Balancing an AI system with human employees to remove the potential for human error and biased data.

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Course Video