Big Data AI & ML in Financial Services
The Delta Capita / Alqami Big Data, AI & Machine Learning in Financial Services programme is a three-hour course aimed at executives, exploring the role of data and AI techniques on financial decision-making.
Gain valuable insights and knowledge from industry practitioners, data scientists and case studies, as you learn to apply key elements of big data, AI and ML to your organisation, spot opportunities and potential ways to remedy deficiencies.
Big Data, AI & Machine Learning in Financial Services provides a sound framework, examining origins, use and capabilities, as well as the inherent bias and trust challenges of AI and Machine Learning.
Through Big Data, AI & Machine Learning in Financial Services, you will gain an understanding of the fundamentals of Big Data, AI and machine learning. You will also learn how they apply to financial functions such as KYC (Know Your Clients), cybersecurity, fraud detection, risk, regulatory compliance and customer experience.
Furthermore, the course builds an understanding on how to operationalise these technologies and identify the key areas where they can be applied within your workplace. You will be able to appreciate the value-add that Big Data, AI and Machine Learning techniques can add to various portfolio and risk management strategies. Additionally, you will understand the limitations and challenges of Big Data, AI and Machine Learning and how to overcome them
This course will leave you well informed and able to develop an opinion and strategy on the applications of Big Data, AI & Machine Learning, as you reflect on its strengths and weaknesses, and build a business case for its implementation within your organisation.
Is this course right for you?
This programme is designed for executives wanting to have a more in-depth understanding about Big Data, AI, and Machine Learning in Finance, including members of the exchanges, regulatory agencies and those who make decisions that affect financial results. There are no additional prerequisites.
Section 1 – Introduction to Machine Learning and Big Data
- 1.1 Big Data Concepts
- 1.2 Machine Learning Concept
- 1.3 Infrastructure for Big Data & Machine Learning
Section 2 – Data Strategy & Governance
- 2.1 Data Strategy
- 2.2Competitive Advantages of Data
- 2.3 Project Pipeline and Management
Section 3 – State of the Art & Developments
- 3.1 Alternative data
- 3.2 Tools, Frameworks, Platforms and Auto ML
- 3.3 Cutting edge developments
Section 4 – How to identify where to apply ML & Big Data
- 4.1 Identification of your Challenges
- 4.2 Challenges in Finance
Section 5 – Trust and Ethics
- 5.1 Trust
- 5.2 Ethics
- 5.3 A privacy-aware Society
Section 6 – Futurology
- 6.1 General Trends
- 6.2 The Singularity
- 6.3 Quantum Computing