The Mechanics of Fintech and Artificial Intelligence

IFF Training

IFF Training

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About the course

There is no question the future of banking is digital in this highly competitive and fast-moving environment. Despite the complex processes, heavy compliance burdens as well as richness in data, there is no reason why your organisation can’t commercialise the rewards of FinTech and artificial decision-making.

You will learn how FinTech and AI can help you work more effectively and have a greater impact on your business. Understand the strategic implications of FinTech and AI on the financial industry, where technology offers competitive advantages and how cross-industry digital value chains can improve existing business models. Regardless of your role this course will teach you the business requirements on digital technology in financial services, giving you the confidence to lead meaningful conversations across your business.

HOW YOU WILL LEARN

  • A new module is released every two weeks
  • You can study the units online, save them to your computer or print them out
  • You set the pace for yourself
  • No need to travel or take time off work – cost effective
  • Apply the knowledge, skills and expertise to your work straight away

Course Content

UNIT 1 AN INTRODUCTION TO FINTECH AND AI

  • Explain the scope of FinTech and which gaps in 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)

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 2 DIGITAL TRANSFORMATION IN BANKING

  • 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

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 any why? An overview on APIs, document data stores, graph data base

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 3 THE FUNDAMENTALS OF AI IN FINTECH

  • 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

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 4 OPEN BANKING / PSD2

Learning Outcomes

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

Open Banking Regulation

  • PSD2 pillar 1 and 2

Open Banking Application

  • 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 5 FINTECH AND AI IN RETAIL BANKING, WEALTH MANAGEMENT AND INVESTMENT MANAGEMENT

  • 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

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 6 FINTECH AND SUSTAINABLE FINANCE

  • Explain the environmental, social and governmental component in sustainable finance
  • Understand sustainable finance from a risk management perspective
  • Describe how technology can add value above the status-quo for higher quality sustainable finance products

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

DAA TCFD Scenario Analysis

  • Introduction to climate risk reporting and the Task Force on Climate Related Financial Disclosures (TCFD)
  • How FinTech enables climate risk assessment
  • Deep dive into scenario analyses and stress testing

UNIT 7 CRYPTOCURRENCIES

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

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 8 REGULATORY ASPECTS OF FINTECH

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

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 9 ETHICS AND AI FOR FINANCIAL SERVICES APPLICATIONS

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

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

Experts

Christian Spindler

Christian Spindler is a recognised expert in climate risk analysis and management. After gaining both a PhD in Physics and an MBA, Christian Spindler has gained extensive experience in the data science and AI industries. He has also delivered a broad range of management consulting for data analyt...

Videos and materials

The Mechanics of Fintech and Artificial Intelligence at IFF Training

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Disclaimer

Coursalytics is an independent platform to find, compare, and book executive courses. Coursalytics is not endorsed by, sponsored by, or otherwise affiliated with any business school or university.

Full disclaimer.

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