9:00pm - 9:30pm 

9:30pm - 10:00pm

10:00 -

Machine Learning Workshop

Design Thinking & Journey Mapping Workshop

Hacking continues through the night

Prerequisites

• Basic understanding of blockchain and Ethereum DAPPs technology.
• Novice JavaScript and HTML skills.
• Familiarity with node.js and the npm package manager.
• Familiarity with Linux BASH.

Problem Statement

Data access at Schwab encompasses Authentication, Authorization, and Identity Management. Today, a central system controls how data access is issued, reviewed, and removed in a timely manner. This system is susceptible to common forms of cyber threats, and common availability constraints. Creating a new data access management system that is served by a distributed ledger will make these records of authority impervious to forgery, and extremely resilient to downtime.

Deliverable

Create an Ethereum distributed application (DAPP) that is a data access management system. The application should behave such that the users of the system are able to perform their data activities, such as requesting and approving data access, using a familiar web-based interface and process workflow.

Datasets

N/A

Resources

• Student must provide their own computer to develop on. Windows, Mac, or Linux will be supported. Modern hardware (within the last 5 years) is recommended.
• Newcomers may follow these introductory guides about blockchain, Ethereum, and DAPPs (1, 2, 3).
• Example sequence diagrams, use cases diagrams, and entity relationship diagrams that describe a basic data access management system will be provided to help students orient and ideate!

Key Use Case Assumptions

• Assignment of identities (Ethereum addresses) will be handled outside of this application.
• Students should use a local testnet. Creating the blockchain infrastructure is not required.

Prerequisites

• Understanding of AI based predictive models and Natural language processing
• Experience working with big data sets
• Marketing & Customer Engagement
• Customer Relationship Management

Problem Statement

Advisors lead their client’s investment strategy as a trusted partner - building strong, lasting relationships. There are nevertheless times when internal and external influencers can put the relationship at risk. Planned generational wealth transfer, private investment opportunities, or the passing of a client are just a few examples. How can advisors identify an at-risk relationship early to begin re-building that trust and loyalty before it’s too late?

Deliverable

• Provide risk exposure index for every client and identify the vectors for managing the risk
  • Build additional channels for information that can be feed into the system to identify clients more accurately
• Build a marketing campaign and customer engagement process that can be targeted to each risk group
  • Identify how customer relationships can be maintained and monitored

Datasets

• Provide a subset of Advisor services data for multiple clients
  • History of accounts and portfolio
  • Securities and their performance
  • Transaction history of all the trades
  • Activity related to contributions and withdrawals
• Provide Client Account Information
  • Age
  • Address
  • Account history - When they joined, left, came back to Schwab
  • Additional details known about clients
    • Industry they work in
    • Company Name
• Spouse or other owners of Account

Resources

• Technology
  
  • Analyze client’s portfolio performance, historical performance against industry standards
  • Compare other financial transaction that client might be doing outside of him portfolio investment
  • Build an engine that analyzes these datasets to provide risk ranking and potential risk vectors, estimated portfolio target for the future ex: 90% chance of meeting client’s goals
  • Engine should be capable of building prospective client profile
• Marketing & Business
  • Build a client questionnaire to determines client’s investment goals
  • Develop a marketing campaign
  • Targets different Risk groups ( Age, Marital Status, Job Industry, etc... )

Sample datasets

1. https://www.kaggle.com/finintelligence/nasdaq-financial-fundamentals
2. https://www.kaggle.com/c/two-sigma-financial-modeling/data
3. https://www.quandl.com/search?query=500%20one%20minute%20bars
4. https://toolbox.google.com/datasetsearch
5. Price Data for Portfolio Analysis https://www.kaggle.com/pap1996/price-data-for-portfolio-analysis
6. US Household Income Statistics +32,000 records, with granularity on a neighborhood scale (mean, median, Stdev) https://www.kaggle.com/goldenoakresearch/us-household-income-stats-geo-locations
7. Sharadar Core US Equities Bundle https://www.quandl.com/databases/SFA

Key Use Case Assumptions

• Experience with Python, Panda, Numpy or similar libraries

Prerequisites

Web or app development experience, basic knowledge of authentication, web APIs and databases.

Problem Statement

Passwords suck. We forget them, reset them endlessly, they get phished, stolen, reused. There must be a better way!

Deliverable

An application or website that allows users to access their personalized content via passwordless authentication.

Datasets

A simple spreadsheet with examples of user account data.

Resources

You can bring your own development machine (BYOD), or contact us if you need one set up.

Key Use Case Assumptions

Once authenticated, all the app needs to display to the authenticated user is their name and account balance. Anything extra is just gravy.