The biggest factor in human progress is our ability to coordinate. Economists have been discussing how humans coordinate since Adam Smith’s “the invisible hand” of the market. Markets are one way to coordinate; a decentralized way that reduces all our preference choices to one number: Price. But markets are not the only way. The second way is a completely centralized hierarchical structure: The firm. Markets and firms coexist and coordination mechanisms shift back and forth depending on what is more effective at any time or in any country. For example, Amazon, one of the biggest disrupters across a huge number of industries is almost completely centralized with all information flowing up to Jeff Bezos.

The Financial Disruption course argues that technology is changing the way we coordinate. It documents how boundaries between firms and markets are shifting, disrupting entire industries in the process. It discusses the technologies (the use of smartphones, cryptography and AI) that are accelerating the rate of change of this disruption. It classifies human jobs that are relatively safe from automation from jobs that are in danger of disappearing.

Read the interview   ‘Upskill to stay competitive’ with disruption Professor Raghavendra Rau.

The Financial Disruption course argues that technology is changing the way we coordinate. It documents how boundaries between firms and markets are shifting, disrupting entire industries in the process.

We provide a safe learning environment: read the latest coronavirus updates for our in-class programs.

How you will benefit

• • Understand why finance is being disrupted by technology
  • Understand what areas of your own firm might be at risk from automation
  • Brainstorm on ways to effectively implement the disruptive effects of technology into your own firm
  • Understand when and why you might need blockchains
  • Understand when AI is most likely to be effective

Program length
3 days

Day 1: Automation and Transformation

Morning:

Information problems in firms

• Imperfect information, asymmetric information, and behavioral biases
• Digitalization and information flows within firms and markets
• Organizing data in firms
• Developing ontologies to compare preferences
• Matching preferences across multiple dimensions
• Capturing multi-dimensional preferences

Case study: AccorHotels – A digital transformation

The case discusses the strategic response of industry incumbents to the challenges coming from digital disruptors. It serves as a basis to discuss the opportunities and challenges of industry incumbents to transform themselves and better compete in an increasingly digital business environment.

Afternoon:

Case study: Transformation at ING (A): Agile

This case discusses the “agile” transformation of ING bank in the Netherlands, a reorganization of work which had been critical to respond to and exceed rapidly changing customer expectations. The case provides an opportunity to discuss when agile organizations are successful across countries. How fast should organizations roll out the transformation? How could they build on the experience acquired so far to improve their methodology?

Day 2: Distributed Ledgers and Blockchains

Morning:

Distributed Ledgers: Bitcoin, Blockchain and Beyond

• What is bitcoin?
• What is a distributed ledger? When is it useful?
• When are blockchains useful?
• Mining blocks in class
• Understanding smart contracts
• The problems with poor smart contracting

Afternoon

Case study: Dianrong: Marketplace lending, Blockchain, and the new Finance in China

Dianrong is one of the largest P2P lending platforms in China. It is in the process of rebranding itself as a wealth management platform and is developing capacities to aid its transition. It announced the development of blockchain technology for supply chain finance.

Day 3: AI and Machine Learning

Morning:

• How are AI systems created?
• Do AI systems help understanding employees and customers better?
• Can deep learning help detect fraud?
• The problems of AI systems
• The dark side of technology

Afternoon:

Case study: Noodle Analytics – AI for the enterprise (Stanford SM-301)

Noodle Analytics is a firm that provides AI capabilities to Fortune 1000 type companies under a SaaS-type business model. The case discusses how Noodle set out to develop a scalable business to serve the AI needs of its customers.

This training will be beneficial to any mid- to senior level managers who would like to develop an integrated view of how technology is disrupting different industries.