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AI, Machine Learning & Big Data for Banks & Financial Institutions

Understand the impact to financial institutions of AI and Big Data
  • With the promises of Big Data and AI, human intelligence and expertise have remained essential to creating value from data. Early big data initiatives in financial services often failed because they didn’t define their critical success factors in the context of their existing business. Which of your current successes would be further enhanced with data?  Can data be sourced across business functions? Do you have the right talents in-house? Are the right outcomes being measured?


    Devising and executing on a winning data strategy is as much about the data initiatives your business can support as the technology that could enable them. This workshop introduces participants to the Big Data Canvas, a methodology for ensuring that data strategies remain feasible while pursuing the most valuable outcomes.


    Course Objectives


    • Recognise the dynamics of big data, analytics and data science in various financial applications• Identify the Critical Success Factors for an organisation’s Big Data strategy• Shape your organisation’s big data strategy by leveraging data science best practices• Articulate your business requirements to data professionals




    This course combines presentations, classroom discussions, Q&A and case studies


    Who Should Attend


    All bank professionals, financial executives of financial institutes, Data analysts and IT officials


  • Objectives

    Understanding how machine learning and big data analytics shape decision making in Financial Services sector
    Identify the Critical Success Factors for an organisation’s Big Data strategy


    1. Foundations of Big Data & Machine Learning


    • Core Big Data and Machine Learning concepts
    • Big Data and Financial Analytics State-of-the-Art and Developments
    • Impact on the Financial Services sector
    • Types of problems in Finance that can be solved using Machine Learning / Big Data analytics
    • Machine Learning pipeline : From Data to Prediction

    2. Key aspects of a successful Big Data Strategy


    • The need for a Big Data Strategy
    • Strategic Opportunities and considerations of a Big Data Strategy
    • The six key aspects of a Big Data Strategy:

    • Data: This involves data governance, massive reorganization of data architectures, Privacy and regulatory compliance
    • Identification: This involves identifying business opportunities that could be harnessed by using Big Data / Machine
    • Learning techniques
    • Modeling: This involves determining how data models can improve performance and optimize business outcomes
    • Tools: This involves choosing the appropriate Big Data Infrastructure and Tools for managing and analysing data
    • Capability: This involves building a road map for assembling the right talent pool of the right size and mix
    • Adaptation: this involves adjusting the company culture and making sure that the frontline decision makers understand and incorporate the output of modeling into their business decisions and core strategy

    3. Framework for Big Data Projects: The Big Data Canvas

    • A framework for thinking about, embracing and acting on a Big Data Strategy
    • Acquiring data from the right sources, possibly involving massive reorganization of data from legacy IT systems and obtaining data from external data sources as well
    • Implementing a data-governance standard across the organization to enable access control of data to meet compliance and regulatory obligations, simultaneously ensuring that the data is available to data professionals
    • Identifying key business problems and determining how Big Data / Machine Learning can help optimize business outcomes
    • Ensuring that the appropriate infrastructure and tools are used for storing, transforming and modeling data, finally making the
    modelling outcomes available to frontline managers
    • Building capabilities by establishing the right talent pool with the right mix. This includes hiring/training Data Science Managers,
    Data Scientists, Data Developers, Software Developers and Business Analysts
    • Choosing the correct business metrics to indicate success/failure of a big data project
    • Arguably the most important action- swiftly adjusting company culture by making managers realize the value of Big Data and
    the importance of incorporating Big Data into the core strategy of the company

    Team Challenge I

    Plotting on the Big Data Canvas


    • Participants are challenged in teams to create a preliminary Big Data Strategy based on the first 3 sessions
    • Each team will be provided with an blank Big Data Canvas
    • Members of each team must brainstorm and complete the Big Data Canvas, encompassing all aspects of a Big Data Strategy discussed (Data, Modeling, Tools, Capability, Adaptation) and other aspects such as Costs, Value Add

    4. Big Data Landscape


    • Implementation
    • A guide to choosing the appropriate set of solutions based on the suite of business problems in the financial domain, the availability of skilled talent and company culture
    • This includes separating the wheat from the chaff in the big data vendor ecosystem in areas of Infrastructure, Machine Learning, Analytics, Applications, Data Sources and Training
    • Pros, Cons and considerations of choosing between
    • Proprietary Vendor Products and Open Source
    • in-house software solutions

    5. Big Data: Tactics and Best Practices to Overcome Organizational Hurdles


    • An overview of common organizational hurdles
    • Key to effective use of Big Data - Data Governance platform to allow Internal cross-functional integration, and fulfilling data privacy and regulatory compliance obligations via data access auditing
    • Means to provide seamless data access to data modellers and business insights to decision makers
    • Scalable IT Infrastructure and the lack thereof, stemming from the existence of legacy systems

    Team Challenge II

    Implementation Roadmaps and Contingency Plans


    • Participants are challenged in teams to test the robustness of their Big Data Canvas to implementation challenges
    • Team members have to devise an implementation roadmap with several contingency plans for their Big Data Strategy
    • A set of wildcards will be handed out, containing implementation challenges pertaining to their Big Data Canvas; teams must
    include solutions to these challenges in their contingency plans
    • A volunteer from each group presents key findings that resulted from the discussions

    Day 2


    Recognition of the dynamics of big data, analytics and insights in various applications in banking
    Communication skills to convey your business requirements to data professionals

    Case Studies I

    Big Data: Applications in Finance Part I
    • Churn Prediction & Prevention
    • Loan Default Calculation/Prediction
    • Quantitative Trading
    • Sentiment Analysis
    • Natural Language Processing of news sources/social media

    Team Challenge III

    Contextualising Case Studies
    • Participants are challenged in teams to review the case studies and contextualize the examples to the their own organizations
    and challenges
    • Teams present their ideas as elevator pitches
    • The instructor evaluated of how these applications can positively affect their organizations

    6. Communicating Business Objectives to Data Professionals


    • Once business problems have been identified - it is important to effectively communicate these ideas to data scientists
    • Communication best practices and heuristics to communicate with professionals with a technical or quantitative background

    Team Challenge IV


    • Communicating Requirements to Data professionals
    • Participants must evaluate one or more business objectives discussed in the previous team challenges, and develop and
    communicate a strategy prompt in form of a short presentation

    Case Studies II

    Big Data: Applications in Finance Part II
    • Market/Customer Segmentation - Targeted Marketing
    Up selling financial products
    Cross selling financial products
    • Anomaly Detection
    Customer Fraud Detection - Anti Money Laundering or Theft
    Employee Fraud Detection - Rogue Traders
    • Risk Management and Control

    Team Challenge V


    Contextualising Case Studies
    • Participants are challenged in teams to review the case studies and contextualize the examples to the their own organizations
    and challenges
    • Teams present their ideas as elevator pitches
    • The instructor evaluated of how these applications can positively affect their organizations

    7. Financial Services of the Future: The Time to Harvest Your Data is Now


    • Identifying key technologies and developments in data which will impact the financial services in the next 3 years
    • The changing landscape when it comes to data architecture, governance, privacy, compliance and strategy

    8. Disruptive Fintech - The Competitive Advantage of Data


    • What risks do Fintech firms pose to traditional financial service providers and how can they meet their challenger?
    • Technologies such as Blockchain (a distributed public ledger for validating transactions) are challenging Financial institutions
    on a fundamental level
    • Creating value from the warehoused data provides banks with their unfair advantage. As Fintech firms challenge the legacy
    systems of larger financial institutions by building products using the latest technology - they can by no means challenge/
    compete with the years of collected data

    Review and Discussion


    • A summary of key points covered during the workshop
    • Guidance and resources on how to develop a deeper capacity for developing and executing on your Big Data Strategy
  • Our Tailored Learning Offering

    Do you have five or more people interested in attending this course? Do you want to tailor it to meet your company’s exact requirements? If you’d like to do either of these, we can bring this course to your company’s office. You could even save up to 50% on the cost of sending delegates to a public course and dramatically increase your ROI.

    If you want to run this course at a location convenient to you or if you want a completely customised learning solution, we can help.

    We produce learning solutions that are completely unique to your business. We’ll guide you through the whole process, from the initial consultancy to evaluating the success of the full learning experience. Our learning specialists ensure you get the maximum return on your training investment.

  • We have a combined experience of over 60 years providing learning solutions to the world’s major organisations and are privileged to have contributed to their success. We view our clients as partners and focus on understanding the needs of each organisation we work with to tailor learning solutions to specific requirements.

    We are proud of our record of customer satisfaction. Here is why you should choose us to help you achieve your goals and accelerate your career:

    • Quality – our clients consistently rate our performance ‘excellent’ or ‘outstanding’. Our average overall score awarded to us by our clients is nine out of ten.
    • Track record – we have delivered training solutions for 95% of worlds’ top 100 banks and have trained over 250,000 professionals.
    • Knowledge – our 150 strong team of industry specialist trainers are world leading financial leaders and commentators, ensuring our knowledge base is second to none.
    • Reliability – if we promise it, we deliver it. We have delivered over 20,000 events both in person and online, using simultaneous translation to delegates from over 180 countries.
    • Recognition – we are accredited by the British Accreditation Council and the CPD Certification Service. In an independent review by Feefo we scored 96% on service and 95% on product
This course can be run as an In-house or Tailored Learning programme


  • Mart Van De Ven

    The future of data is already here, it's just not evenly distributed - with my courses I aim to ensure that the at least for you, that future arrives early.


    BIO Mart van de Ven is a Principal at Droste, a Hong Kong-based data science consultancy. He heads a team which strategises for companies which are data rich but lack a concrete roadmap to capitalise on it. Droste employs advanced analytics and machine learning to help organisations realign data aspirations with business goals. Their work includes predictive maintenance systems for a utilities company, a credit-scoring algorithm based on social media data, and the computer vision technology driving a visual search company.Outside of Droste, Mart is also committed to furthering data adoption and data literacy across the city. He's designed data science curricula, conducted 100+ workshops, and provided mentorship on 200+ data science projects. Mart is known for his contributions to the public debate on data-related issues. He is a frequent guest speaker on data science (e.g. UBS, CLP, HKTDC), the co-founder of Open Data Hong Kong, a civic tech advocacy group, and leads Symbol & Key, the city's most visible platform for data scientists. His personal interests include speculative fiction, linguistics and, abstract strategy games.