Artificial Intelligence in Banking (In-House)

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

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

Big Data and AI in Investment Management

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

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

Emerging Risks

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

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

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