FinTech Training Week

4 days 10-13 Dec 2018, Hong Kong Hong Kong $4,995.00 Download brochure Add to basket

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This course is made up of 2 modules:
Module 1 –  AI, Machine Learning & Big Data for Banks & Financial Institutions
Module 2 –  Blockchain - A Practical, Strategic & Commercial Guide


Booking onto both courses and save $2595.


We work with a series of expert instructors, please select the course location of interest to review the credentials of who will be delivering the programme.

Hong Kong
Mart Van De Ven

Mart van de Ven is a data scientist with nearly a decade of experience delivering data-driven solutions. As a consultant, he designed solutions for market segmentation, demand and churn prediction, and product recommenders. In the realm of finance, he's enabled traders at hedge funds to validate their hypotheses faster, developed the risk scoring algorithm powering a P2P lending platform, and built a FX spot-rate service for eCommerce. Before turning to consultancy in 2014, he participated in the development of a competitor intelligence system for Groupon, and built fraud detection and credit score systems based on social media data with DemystData.

Mart is a data practioner with extensive training experience. As General Assembly's lead data science instructor since 2012, he has been responsible for developing and delivering a data science curriculum tailored to the Asian market. He has conducted hundreds of workshops over the past four years and provided mentorship on 150+ data science projects.

Mart is also known for his contributions to the public debate on data-related issues. He was featured in the WSJ, HKEJ, and SCMP, 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. A Hong Kong resident since 2007, Mart is a Brazilian-Dutch national who holds a M.Phil in Linguistics from the University of Hong Kong and a M.Sc of Information & Knowledge Management from Loughborough University.


Hong Kong

4-5 Star Hotel in Hong Kong

All of our courses are held in 4 – 5 star hotels, chosen for their location, facilities and level of service. You can be assured of a comfortable, convenient learning environment throughout the duration of the course.

Due to the variation in delegate numbers, we will send confirmation of the venue to you approximately 2 weeks before the start of the course. Course fees include training facilities, documentation, lunches and refreshments for the duration of the programme. Delegates are responsible for arranging their own accommodation, however, a list of convenient hotels (many at specially negotiated rates) is available upon registration.

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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.

To find out more about running this course in-house:

Our Tailored Learning Offering

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 can offer any of our public courses delivered at your office or we can devise completely tailored solutions:

Read more about our offering or complete a call back request to speak to a learning specialist.



Day 1



  • 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


  • 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

Day 3 & 4


Blockchain: A Practical, Strategic & Commercial Guide


  • Background and Introductions

Course Structure

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

How organisations work and examples

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


  • 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 Bitcoin
  • Bitcoin 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
  • Regulatory 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

Where next for blockchain technology?

  • Vision and Opportunities
  • Barriers


Why us

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