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Course details

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FinTech Training Week

Gain the expertise required to become a leader in FinTech & drive culture change
  • This course consists of two individually bookable modules

    Module 1 - AI, Machine Learning & Big Data for Banks (days 1 and 2)
    Module 2 - Blockchain - A Practical, Strategic & Commercial Guide (days 3 and 4)

    Both courses can be booked in their own right.

    Course overview


    Developments in the FinTech space are transforming financial services and blockchain and distributed ledger technology is at the forefront of this revolution. Both The Wall Street Journal and The Economist have described it as technology that could change the world.


    Blockchain offers banks, asset managers and other organisations the potential to achieve considerable cost savings, efficiencies and resilience in relation to their payment and payment execution systems. These are developments that no senior executive can afford to ignore. Gaining a foothold at this early stage is vital in shaping the technology strategies of the future.


    This course provides a grounded and organisationally relevant introduction to blockchain and related cryptocurrency technology and a detailed look at Initial Coin Offerings. Starting from first principles, the course approaches the technology from a number of different perspectives providing foundational knowledge that will enable delegates to return to their own organisations with a clear understanding of how this important technology impacts the bottom line.


    This course is a comprehensive guide to understanding and using blockchain technology, and through practical demonstrations and examples, will leave people with real sense of what’s possible.

  •  

    Day 1


    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

    Session I


    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

    Session II


    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 voutput of modeling into their business decisions and core strategy

    Session III


    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.

    Session IV


    Big Data Landscape

    • Overview of different technical components and solutions available for a Big Data Implementation
    • A guide to choosing the appropriate set of solutions based on the 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
    Session V
    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

    Review and Discussion

    Day 2


    Objectives

    • 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

    Session VI


    Communicating Business Objectives to Data Professionals

    • Once business problems have been identified - it is important to 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

    Session VII


    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

    Session VIII


    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


    Days 3 & 4
    Blockchain: A Practical, Strategic & Commercial Guide


    Introduction 

    Background and Introductions

    Course Structure
    • Why is blockchain so important?
    • How is blockchain used? Sector Examples
    • Market dynamics

    Context

    How organisations work and examples:

    • Front to back | Business process flows & making money | Goods & services
    • Technology architecture | Centralised vs. Distributed
    • Supply and purchase

    The emergence of cryptocurrencies (and the blockchain) 

    Money
    • What is money and how does it acquire value?
    • Banking and payments infrastructure
    • Central banking and regulation
    • The advent of the internet and the case for digital money

    History of Cryptocurrencies
    • The world pre-bitcoin
    • The challenge of digital money | Sending and Receiving Money Online
    • Bitcoin and why study it?
    • The emergence of blockchain from BitcoinBitcoin and Cryptocurrencies Today
    • Digital currencies - Bitcoin, Ether, Ripple, Dash, Litecoin, Zcash, Monero etc
    • Understanding Wallets, Sending and Receiving Bitcoin

    High Level - How blockchains and cryptocurrencies work?
    • Cryptographic primitives
    • The hash function | SHA 256 and examples
    • Digital signing
    • Public / private key infrastructure
    • The concept of identity and wallets
    • Transactions and Consensus Protocols
    • Digital Currency Trading Exercise

    The Blockchain Game - Compete to Mine A Digital Currency


    Decentralised Applications - Open Software and Smart Contracts
    • Ethereum and EOS
    • Smart Contracts
    • Using Smart Contracts

    Market Overview
    • Currency Segmentation
    • Market trends
    • Initial Coin Offerings and capital raising

    Digital Currency Trading Exercise
    • Introduction to digital currency trading and currency exchanges
    • Example trading indicators - MACD, Moving Averages, Relative Strength
    • Cyber security

    Corporate Structures
    • Digital currency companies
    • Governance
    • Distributed Autonomous Organisations

    Regulatory, Tax and Compliance
    • Government Perspectives
    • Regulalory Framework
    • Tax Treatment
    • Money Laundering - KYC and AML

    Workshop Session: Using blockchain and digital currency technology
    Opportunity Assessments
    • Proof of Concept
    • Blockchain Strategies
    • Commercial Perspectives - how do you engage a blockchain company?

    Blockchain Technologies By Sector and Function
    • Sector Review - Financial Services, Oil and Gas, Pharmaceuticals, Retail, Media
    • Functional Review - Provenance, Procurement, Payments, Sales and Identity

    Beyond Blockchain Technology

    • Challenges with Blockchain Technology
    • IOTA

    The Future
    • Where next for blockchain technology?
    • Vision and Opportunities
    • Barriers


  • 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

Instructor

  • 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 at least for you, that future arrives early.

    Biography

    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.