IS484 IS Project Experience (FinTech)
Course Description:
- This is an SMU-X course designed in collaboration with CitiVentures Innovation Lab. Citibank will supply a minimum of 5 projects ideas to select from.
- Students will form teams of 5 or 6, and select one of the Citibank project ideas to work on. Project selections do not need to be unique, meaning multiple teams can select the same project idea.
- Each student project team will be assigned to a Citibank sponsor and an SMU faculty supervisor.
- Citibank will provide project scope and management for student teams to have practical industry learning experiences.
- Student teams will have weekly check in meetings, either virtually or physically, with their Citibank sponsor.
- Citibank will specify the technologies to be used, including; development tools/languages, OS, database, 3rd party libraries, target deployment environment e.g. cloud environment.
Project Timeline:
- Week 1 - Attend orientation session, where teams will be formed (if you have not already formed a team), and projects are selected from a set of predefined projects provided by CitiVentures.
- Week 8 - Midterm presentation and demo
- Week 15 - Final presentation and demo
Project deliverable:
- Student project teams will be expected to develop a working software application prototype, to be delivered to Citibank at the end of the course.
- A formal, in-person midterm and final presentation will be facilitated by Citibank.
Citibank Projects
- Projects to be selected/assigned to project teams during Week 1 orientation session.
| Item | Project Description | Project Deliverables | Project Sponsor/Stakeholders |
|---|---|---|---|
| 1 | Private Banking Client Dashboard - Citi Private Bank (CPB) Investment Counsellors and Advisors provide frequent consultation to HNWI and UHWNI (high and ultra-high net-worth individuals) on how to manage their Investment portfolios. In order to perform their job they need high speed access to a client's positions, real-time market data and publicly available sentiment on the portfolio's constituents. The portfolio is usually composed of capital market securities and various funds (hedge, mutual, real estate, private equity). The work would entail coming up with Investment solutions (capital market products and alternative funds) for the consumption of qualified Investment Counsellors and Advisors. Careful thought needs to be put into providing an enriching UX / UI and leveraging machine / deep learning capability to provide robust recommendations. The users will use the information to pro-actively and also reactively service CPB's HNWI and UHNWI clients. |
A working dashboard that provides a real-time view of a client's position. The view should be contextual based on the type of holdings (Cash/Liabilities, Equity, Fixed Income, Derivatives and Alternative Investments). The view would give an instrument and profitability analysis based on market data (Bloomberg / Reuters). Furthermore, there will be a recommendation engine that looks at a client's current / past positions and suggests trade-able ideas to the advisor based on upcoming announcements, trending public sentiment and client's personal interests. |
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| 2 | Predictive Analysis of Risk Utilization - Citi's institutional clients place millions of orders on any given trading day through its electronic execution platforms. As orders come in through Citi's systems, they are evaluated against several risk parameters(such as credit limits) before the order is sent to the market. While currently, breaches in these parameters can be identified the moment the orders are placed, the next gen evolution of this risk management system requires predictive analytics of such breach events. This will enable Citi's clients and client facing officials to prevent regulatory violations, navigate trading disruptions by proactively take measures to prevent such breaches by allocating funds/ changing their trading strategy etc. | Students executing this project will be expected arrive at a machine learning solution to predict imminent movement of the risk parameters based on historical trading patterns. The solution should be able to take data feed for supplemental information (Triple witching dates, FTSE/MSCI rebalancing, other events that affect the market such as the Coronavirus threat) to more accurately predict exceptional scenarios. Tasks:
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