Machine Learning and AI in Finance

Master disruption in the financial services with the integration of Machine Learning and AI.

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Course Overview

Following recent advances in big data and cloud computing, AI is fast becoming a game-changer for financial institutions—allowing them to capitalize on data-driven growth. No leader or innovator in finance can neglect AI as a core part of their wider digital transformation strategy.

Financial services need to get more out of their data, or they will face significant challenges from new players in the market, among them FinTech startups and technology companies innovating in predictive and customer-facing techniques. This course provides leaders and innovators in finance with the know-how and critical awareness to lead their company’s AI strategy. Participants will learn how to exploit their data assets and new developing techniques in Machine Learning and AI for better decision-making. You will learn approaches for success, how to secure buying from employees, and how to justify the business case for AI solutions to your company board.

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Why choose this course?

What to Expect

  • Latest Use Cases: Gain insights from current use cases of AI and Machine Learning within financial services organisations;
  • Practical Framework: Acquire a practical framework for success utilising core strategic tools;
  • Expert Delivery: Learn from a recognised expert, trainer and advisor on FinTech related advancements and opportunities.

Is it for you?

  • Leaders & managers Who want to deliver competitive advantage through intelligent use of data;
  • Strategists Who want to learn best practices through solid case studies for efficient and effective immediate implementation;
  • Innovators who want to be able to predict future needs and wants of customers.

Programme Modules

  • Session 1: Current Overview of ML and AI in Finance
  • Session 2: Critical Use Cases of ML and AI for Finance
  • Session 3: Trends in the Adoption and Deployment of ML and AI
  • Session 4: Regulatory and Technology Constraints
  • Session 5: AI Strategy

Learning outcomes

Benefits for Professional

  • Gain strategic and tactical insights drawn from the world’s best AI companies and FinTech disruptors;
  • Align business objectives, data, and the technically possible with existing market needs and customer expectations;
  • Confidently communicate the business case for Machine Learning and AI to your key stakeholders;
  • Collaborate effectively with data science automation teams;
  • Drive more agile and intelligent customer engagement.

Benefits for the Organisation

  • Ensure the most efficient use of your data to drive innovation and competitive advantage;
  • Learn how to align data management, analytics and Machine Learning with existing business goals;
  • Implement new and proven methods for fraud detection, customer segmentation, sentiment analysis, pricing, predictive analysis, and more;
  • Learn how to manage and direct the impact of AI across different business units;
  • Effectively navigate issues around data privacy and sensitivity.

Programme Details

Module 1

Session 1: Current Overview of ML and AI in Finance

  • Overview of maturity levels of adoption and the different speeds at which innovation and AI are being implemented in the financial industry
  • Defining, differentiating and contextualising AI, probabilistic ML and Deep Learning
  • AI versus Cognitive Computing
  • Case studies in ML, AI and disruptive financial technology

Module 2

Session 2: Critical Use Cases of ML and AI for Finance

  • Innovation in the customer journey
  • Customer segmentation and activity profiling
  • Sentiment analysis
  • Predictive analysis and modelling
  • Operational automation

Module 3

Session 3: Trends in the Adoption and Deployment of ML and AI

  • What are the hurdles? (Data silos / Legacy systems / Credibility / Protectionism)
  • What are the enablers? (Client on-boarding / Data governance and data quality / Data science talent / Responsibility and control)

Module 4

Session 4: Regulatory and Technology Constraints

  • Data access, privacy and sensitivity
  • Compliance, regulation and regulatory ecosystems
  • Regulatory technology (RegTech)

Module 5

Session 5: AI Strategy

  • Change management, agility and collaborative approaches
  • Managing and unlocking resources
  • AI product lifecycles and life in production
  • Explainable Machine Learning and AI

Course Director

Petros is a FinTech consultant with over 25 years of professional experience in trading, product development and sales. As former head of VEGA Structured Finance GmbH in Stuttgart, Germany, he introduced default-free ABS to medium-sized corporates using data analytics. He has worked for Swiss Bank Corporation, Union Discount PLC in Zurich and London – where he was engaged in trading, selling and structuring FX, Equity Exotic and Fixed Income Derivative Products as well as their underlying cash instruments.

Petros is also a visiting lecturer at SKEMA Business School in Paris, ranked 6th world-wide by the Financial Times in 2018 for its M.Sc.

Academy, London

The Academy, London is a one-of-a-kind, customisable and inspirational learning space for Google customer education and collaboration in Victoria. Based at 123 Buckingham Palace Road and in walking distance from both the Victoria Underground and Coach Station; the Academy London’s purpose is to help deepen partnerships with Google's key clients and influencers. The Academy space helps customers to transform their business digitally, to accelerate growth, improve efficiency and create a sustainable competitive edge. Googlers host a wide variety of educational events in the space, including hackathons, workshops, masterclasses and conferences.