Artificial Intelligence in Banking

IFF Training

IFF Training

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About the course

Banking is at the leading edge of the digital and AI transformation. Owing to the complex processes, heavy compliance burdens as well as richness in data, there is no reason why your bank can’t realise the rewards of artificial decision-making.

During this training programme, you’ll learn that banks have to discover, understand and embrace AI to stay competitive. You’ll examine best practice from various angles of the AI value chain and identify what is applicable for your own organisation.

There are only a few banks running advanced end-to-end data and AI infrastructures. With the right training, yours can be one of them. After this course, you’ll be perfectly placed to derive value from employing AI in your business operations.

You will learn about:

  • Designing an AI infrastructure for your bank
  • Building multi-stakeholder AI partnerships
  • The latest cutting-edge business applications of AI within front and back offices
  • Key regulations, basic compliance and the elementary legal framework for AI in banking
  • The potential dangers as well as ethical and social aspects of AI

Practical sessions include:

  • Make an assessment – how does your organisation handle data today and what would be your wish for a future data culture?
  • How would you set up an AI project at your company? What elements would you buy on the market and which ones develop in-house? Which team in your organisation would do it?
  • How to employ anomaly detection in your bank. Market vendors and inhouse strategies
  • Design an AI project for your organisation and roadmap the development and implementation. What obstacles do you face?
  • Identify and structure your own case on Big Data and AI in your organisation

Agenda Summary

Understanding the Ongoing Digital and Analytics Transformation in Banking

  • What is happening and why is it happening now?
  • Drivers: data access, mobile computing, payment technology, algorithms
  • Maybe-drivers: blockchain
  • ICT and AI as a General Purpose Technology
  • New players emerge rapidly in banking
    • FinTech startups
    • Technology giants
    • Neo banks
    • Mobile Network Operators
  • So why do banks seem unable to replicate it? The banking innovation paradox

Focus on the Data

Why is data access key to all AI powered applications? What are technological, legal and cultural obstacles on the way to an AI driven bank?

  • Data is the key ingredient to power algorithms
  • The Internet-of-Things generates even more data than the human internet, with growing tendency
  • Legacy IT is hindering banks to adopt new technology quickly
  • Regulators are actually fostering digital transformation in banks
  • Cultural obstacles, legacy processes and routines are the most effective road blocker from faster AI adoption in banks

Group work Make an assessment – how does your organisation handle data today and what would be your wish for a future data culture?

Design an AI Infrastructure and Governance Process Within Your Bank: The New Paradigm of Data and AI Integration

  • How do we define AI for this training?
  • The data science value chain: how is the maths part of AI done today?
  • Data science automation: let your data scientist focus on the really important matters
  • APIs and Microservices: the smart way out of monolithic data and analytics systems
  • Continuous integration, delivery and improvement of AI

Group work How would you set up an AI project at your company? What elements would you buy on the market and which ones develop in-house? Which team in your organisation would do it?

Anomaly Detection in Banking: Transactions, Behaviour and More

  • Anomaly detection: one of the most versatile applications of algorithms around
  • What are successful use cases for anomaly detection in banking?
    • Transaction monitoring
    • Credit card fraud detection
    • AML / KYC
    • Behavioural analytics, customer segmentation
    • Investment

Group work How to employ anomaly detection in your bank. Market vendors and inhouse strategies

Big Data and Risk Technology

  • How to do it
  • Social media credit scoring and profiling
  • Sesame Credit
  • GDPR

Demo Anomaly detection revisited

Emerging Risks

  • Sigma Ratings, NetGuardians
  • Payments: YouPay, RiskIdent, BehavioSec

Demos Environmental and climate risks and risk dashboards

How to Make an AI Strategy Deliver Productive Value

  • Investment planning: assessing the business value of AI projects
  • The Proof-of-Concept trap: data science vs. software development in your bank’s IT
  • Digital ecosystems: Platformification and the Alibaba Strategy

Group work

Design an AI project for your organisation and roadmap the development and implementation. What obstacles do you face?

Organisational, Talent and Ethical Aspects of AI Integration into Banks

  • Managing data science and AI: shape and organisation for a data-driven culture and as an attractive workplace
  • Regulatory aspects of AI
  • Ethical aspects of AI in banking: fair lending show-case
  • Enabling your workforce
    • Hybrid models
    • Machines and humans working together

Myth Buster Session: Key Terms Around AI and How They Relate to Banking

  • Descriptive, analytic, predictive, prescriptive applications
  • Machine learning and online learning
  • Narrow vs. general AI
  • Singularity
  • Supervised / unsupervised / reinforcement learning
  • Deep learning and neural networks
  • Alternatives to Machine Learning
    • Causal reasoning
    • Expert systems
    • Knowledge based systems
    • Boltzmann Machine

Key AI Cases in Finance in Front, Middle and Back Office

  • Overview front/middle/back office AI applications
  • From automation to AI
    • Example AML
  • Voice and chat banking
    • Alexa, Siri, Cortana, Google Assistant
    • Monese, Kasisto, Finn AI
  • AI in ESG reporting quality control
  • Natural language generation in Finance

Group discussion Identify and structure your own case on Big Data and AI in your organisation

Robo-Advisory Implementation and Regulation Around the Globe

  • Overview of robo-advisors and AI-enabled banking channels
  • 1st gen of robo-advisors: Hybrid Model
  • 2nd gen: micro savings
  • Market Forecast Advisors
  • AlpacaJapan
  • AlgoDynamix
  • Regulation of robo-advisors: Algorithm assurance

From Integrated Data to Applications: Pay-per-use Financing, Why and How

  • Banks begin to realise IIoT potential with dedicated financing products
  • Digital Twins of business - and production - processes as enablers for data-driven banking services
  • Commerzbank example
  • Siemens Financial Services example

Steps to Set-up a Pay-per-use Financing Model at Your Company

  • Big Data Analytics specialised to the treatment of broadband, noisy industrial data
  • Regulatory framework (BaFin / FINMA) regarding the development of financial products

Case study

How would you develop pay-per-use financing at your company? What other x-per-use models can you imagine in your bank?

Open Banking: AI Enabled Applications

  • Overview on open banking
  • AI enabled application in retail banking

Big Data and AI in Investment Management

  • 3 Edge from Boston: AI propelled predictions, with symbolic narratives
  • 2iQ research from Frankfurt: Behavioral Quantitative Finance
  • Alternative data

Group work Finalise your own case on Big Data and AI in your organisation

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...

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Artificial Intelligence in Banking at IFF Training

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