Fintech and Artificial Intelligence

Training

Welcome to our virtual training on Fintech and Artificial Intelligence

This training is built up in a modular way – you can decide to take the entire course, or to pick individual modules. We are looking forward to working with you!

Zero-Touch Planning

Zero-Touch Planning

Swisscom AG, Berne, Switzerland

With Zero-Touch Planning, Swisscom has set itself the goal of making group-wide financial planning more robust and efficient through predictive analytics and automation. For this purpose, algorithmic processes are used to create predictions at the level of individual profit and loss positions based on historical developments, drivers for sales and cost development, as well as internal specialist knowledge. Christian was brought in as an external expert for data science, machine learning and automation to contribute to the development of zero-touch planning at Swisscom as lead data scientist. With his understanding of the sensible use of algorithms, the automation of data science applications, as well as the integration of specialist knowledge into algorithmic processes, Christian has made decisive progress in our team. Thanks to Christian’s support, Swisscom is now in a position to carry out monthly planning processes automatically and with data support.
Data Science program

Data Science program

NN Investment Partners, The Hague

Christian is a highly skilled and motivating trainer who delivered a great Data Science program for our portfolio managers.
Python for Marketing Teams

Python for Marketing Teams

Die Mobiliar, Berne

Christian is a very pleasant and competent course leader. With his calm and authentic manner, he gave us a very entertaining, interactive introduction to Python that was tailored to our needs. We are happy to recommend him.

Module 1: An introduction to FinTech and AI

Unit Content

  • FinTech – scope and interfaces to traditional Financial Services
  • What are key characteristics of FinTech, what has it in common with concepts such as InsureTech, LegalTech?
  • Where do FinTech trends emerge, what are global similarities and what are local differentiators?
  • What is the monetary market share of FinTech on all Financial Services, in which areas do we see and anticipate strongest growth?

FinTech technology

  • What are the working hypotheses of FinTech regarding technology accessibility, processes and legal foundation?
  • Is FinTech necessarily mobile?
  • How is FinTech related to and dependent on Big Data technology? Which of the HiFive criteria apply for FinTech applications?
  • In which parts of the value chain is FinTech related to Artificial Intelligence? Which concepts of AI is FinTech exploiting now and what is anticipated for the future?

Unit learning Aims and Objectives

  • Explain the scope of FinTech and which gaps of traditional Financial Services it fills
  • Review the landscape of FinTech companies around the world
  • Describe the relations of FinTech to traditional Financial Services
  • Describe the value chains of FinTech and where it makes use of Artificial Intelligence (AI)

Module 2: Digital transformation in banking

Unit Content

  • Data systems
    • How are traditional data systems in banks built up? Why is the traditional setup often limiting the implementation of modern FinTech concepts?
    • How are modern FinTech data systems structured and why? An overview on APIs, document data stores, graph databases
  • Digital tools
    • Web-services
    • Analytics environments
    • Robotic process automation
  • Digital processes
  • Why are tools alone not enough to create digital Financial Services
  • Digital process design
  • Digital culture
  • Data hygiene
  • Security first
  • Acknowledgement of uncertainty about future requirements

Unit learning Aims and Objectives

  • Explain what digital transformation constitutes and why it triggers the emergence of FinTech
  • Understand the obstacles traditional Financial Services face with digital transformation
  • Review the state of digital transformation within traditional Financial Services

Module 3: The fundamentals of AI in Fintech

Unit Content

  • Why now?
    • Computational power
    • Ubiquitous data availability
    • Modern algorithms
  • What is AI?
    • The AI Effect
    • Machine learning, expert systems and internet of things
    • Hardware implementation
    • Applications
  • Machine Learning
    • Basic working principles of machine learning
    • Linear vs. nonlinear models
    • Shallow vs. Deep learning
    • Frameworks and implementation
  • Limitations
    • Availability of high-quality data
    • Interpretability of machine learning model decisions
    • Correlation vs. causation
  • Anomaly detection
    • Why is anomaly detection one of the most important techniques in business?
    • Anomaly detection using Autoencoders
    • Anomaly detection in financial transactions
  • Natural language processing
    • Classifying financial transactions with NLP
    • Understanding banking contracts
    • Investment research with NLP
  • Recommender Engines
    • Amazon’s recommendations also work in FinTech
    • The mechanism of recommender engines

Unit learning Aims and Objectives

  • Describe scope and terms of Artificial Intelligence
  • Explain requirements and development of AI
  • Understand capabilities and limitations of AI
  • Describe key generic use cases for AI in FinTech

Module 4: Open banking / PSD2

Unit Content

  • Open Banking regulation
    • PSD2 pillar 1 and 2
    • Open Banking applications
    • Data portability and open banking impact on competitive dynamics
    • Understanding churn, Market insights, Understanding ‘Lost Pipeline’
    • Build your own bank on ORCA
  • Open Banking security
    • EBA’s Regulatory Technology Standard (RTS)
    • Strong authentication technologies
    • PSD2 reference architecture
    • Biometric identification
    • Adapting to the General Data Protection Regulation

Unit learning Aims and Objectives

  • Explain the Open Banking / PSD2 directive
  • Describe implications of Open Banking on FinTech
  • Review FinTech case studies that employ Open Banking

Module 5: FinTech and AI in retail banking, wealth management and investment management

Unit Content

  • Payment services
    • The individual steps in the electronic payment process
    • How FinTech accesses, accelerates and automates these steps
    • The role of aggregation platforms (e.g. WeChat)
    • Technology for payment services
  • Robo advisory
    • The robo advisory business case
    • Live robo advisory examples and comparison of selected offers
  • Report generation
    • Introduction to NLP for speech generation
    • Live example of financial report generation
    • Applications of report generation
  • KYC / AML
    • AI techniques for automating Know Your Client (KYC) and Anti-Money Laundering (AML) processes
    • The client2vec algorithm
    • Credit risk management
  • End-to-end example for AI based credit scoring
    • Fraud screening
    • Credit card fraud screening
    • Transaction fraud screening
  • Portfolio management
    • How FinTech can provide competitive advantages in investing
    • Alternative data
    • Crowd investment
    • FinTech and factoring

Unit learning Aims and Objectives

  • Explain the fundamental process in retail banking
  • Describe where FinTech can add value in these processes
  • Explain the fundamental process in wealth management
  • Describe where FinTech can add value in these processes
  • Explain the fundamental process in investment management
  • Describe where FinTech can add value in these processes

Module 6: FinTech and Sustainable Finance

Unit Content

  • Overview sustainable finance
    • The UN SDGs and the Principles of Responsible Investment
    • Green finance instruments
    • FinTech for ESG risk assessment
  • DB ESG screening tool
    • Introduction to ESG reporting, opportunities for FinTech
    • Deep dive into DB Alpha Dig
  • TCFD scenario analysis
    • Introduction to climate risk reporting and the Task Force on Climate Related Financial Disclosures
    • How FinTech enables climate risk assessment
    • Deep dive into scenario analyses and stress testing

Unit learning Aims and Objectives

  • A primer on sustainable finance
  • What are processes where FinTech can add value
  • Climate risk examples

Module 7: Cryptocurrencies

Unit Content

  • Introduction to the Distributed Ledger Technology and overview of current cryptocurrencies
    • Bitcoin, Ether
    • The case study of Facebook’s Libra
  • Business cases for cryptocurrencies
    • Global trade finance
    • Payment services
  • Auditing cryptocurrencies
    • Cryptocurrency accounting practices
    • Technology for auditing cryptocurrencies

Unit learning Aims and Objectives

  • Explain Distributed Ledger Technology
  • Describe similarities and differences of important cryptocurrencies
  • Understand the role of cryptocurrencies in different applications

Module 8: Regulatory aspects of FinTech

  • Unit Content
    • Robo advisory regulation
    • Case study of two US robo advisory models
    • Robo advisory regulation around the world
  • FinTech and supervision
    • When does FinTech require a banking license?
    • The special case of microfinance applications
  • GDPR
    • The European GDPR as basic framework for data protection regulation concerning FinTechs

Unit learning Aims and Objectives

  • Explain which elements of FinTech fall under conventional banking regulation
  • Describe relations of FinTech to data privacy regulation, AML and KYC

Module 9: Ethics and AI for Financial Services applications

Unit Content

  • Ethical AI development in the FinTech space
    • Singapore’s FEAT principles
    • Europe’s ethical AI guidelines
    • The Algo.Rules
  • Interpretability of AI models
    • The difference of explainability and interpretability of AI models
    • Global interpretability
    • Local interpretability
    • FinTech related examples
  • Bias and de-biasing of data
    • Intentional and unintentional bias in data
    • How biased data lead to biased AI models
    • Examples of the bias problem
    • De-biasing of data

Unit learning Aims and Objectives

  • Explore ethical questions around the application of AI for FS
  • Review the position of global supervisory organizations
  • Examine AI interpretability and bias in data hands-on
  • Describe measures and approaches to secure responsible use of AI for FS