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 -8 - Form teams. Review the below set of predefined projects provided by CitiVentures. Fill up the signup sheet at the below link, listing your preferred projects.
• Week -6 - FT Track Coordinator will finalize the matching of teams to projects.
• Week -4 - Submit your project proposals to your Track Coordinator(s). For mixed-track teams, both track coordinators need to review your proposal.
• Week -2 - Your Track Coordinator(s) will confirm that the project has sufficient scope to fulfill your respective track requirements for IS Project Experience.
• Week 1 - Attend orientation session to meet your CitiVentures sponsors. Start the project.
• Week 8 - Midterm presentation and demo
• Week 15 - Final presentation and demo

• Projects to be selected/assigned to project teams during Week 1 orientation session.
• Project teams can sign up for their preferred project at the below link.

AY2020/21 Term 1
https://docs.google.com/spreadsheets/d/1IDAhC4JiK3RuKnIDQMG5UjJ6I1IiImo81Lu13wAuUxE/edit?usp=sharing
AY2020/21 Term 2
https://docs.google.com/spreadsheets/d/1IDAhC4JiK3RuKnIDQMG5UjJ6I1IiImo81Lu13wAuUxE/edit#gid=1043528005

Project deliverable:

1. Student project teams will be expected to develop a working software application prototype, to be delivered to Citibank at the end of the course.
2. A formal, in-person midterm and final presentation will be facilitated by Citibank.

IS484 Project Wiki

Project teams to maintain their documentation here:
IS484 Project Wiki Home Page

Citibank Projects

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). 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.	Kashif Awan, Private Bank Capital Markets APAC Technology Head kashif.awan@citi.com
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: Understand Citi's current data model for storing historical data. Build adapters to funnel data to a central data pool to run analytics on the data. Analyze and find inflection data points and patterns. Build supplemental data feed to establish market sentiments in the sytem and use that to augment their prediction models. Build a user interface/ data conduit that can be used by Citi clients/ users to be notified of any breaches if found.	Sudeep Kumar, Global Exchange Connectivity & Asia Cash Equities Technology Lead sudeep1.kumar@citi.com
3	Customer Mailing Address Analysis - Addresses of people and businesses contain important information about them. More data about the locations of those addresses is required to get some insight from addresses. For example the population, geographic and economic indicators, crime rates etc. can be helpful. We need to collect such information about countries and cities to make the addresses usable in models and other analytics.	A solution or program which can accomplish the following: Collect information about countries from IMF data. Collect information about cities from DBPedia data. Build schedules to keep the above data fresh, as new data is available. Make this data available to lookup by country and Citi names to be used by models and analytics queries. Generate an embedding of countries and an embedding of cities, to be used as features in models. Unstructured addresses (where country, city are not marked separately, but part of large address text) need to be parsed before lookup. Make this information available by joining the addresses of people and businesses and collected data by countries and cities as join keys. Measure how much the model performance improves, after using this additional information.	Yuqian Song, Head of APAC/EMEA Data Services and Head of Global Advanced Analytics Technology Solutions yuqian.song@citi.com
4	Marketing Audience Segmentation - Citibank sends merchants’ offers to the relevant customers. For example customers who often buy sports gear should get sports related offers and foodies should get offers from the restaurants. This requires accurate segmentation of customers as well as merchants. 3rd party data can be used to improve marketing audience segmentation.	A solution or program which can accomplish the following: Acquire 3rd party e.g. Statista, Euromonitor and map the brand mentions in the transactions, with brand master list in acquired data. Use brand category-hierarchy to segment the customers for their buying habits, using customer transaction history. Use brand category-hierarchy to segment merchants by categories of products and services sold and offers made. Use the category based segments for a broader match between customers and merchants.	Yuqian Song, Head of APAC/EMEA Data Services and Head of Global Advanced Analytics Technology Solutions yuqian.song@citi.com
5	TBD - Description.	Project Deliverables.	Sponsor Name, Sponsor Role sponsor@citi.com

Archived Projects (no longer available)

Item	Project Description	Project Deliverables	Project Sponsor/Stakeholders
X	Machine Learning Model Performance - Machine learning models are being trained based on historical data. But in the commercial world, change is expected rapidly which may mark the model biased to the new data as well as scaled old data. Before the model is retained, there are immediate needs to understand what are the leverages that can be applied to interfere with the old model output to achieve the accuracy rate, then capture the business opportunity in a very short turnaround time. When models are unable to digest new data, they will generate inaccurate recommendations and predictions to the business, resulting in missing the opportunities for increased revenue.	A solution or program which can accomplish the following: Detect the root cause of low accuracy with a given model input, model output and model binary. Generate corrective recommendations to increase accuracy without re-building the model. Perform regression testing with recommendations, to demonstrate the expected accuracy. The program is expected to be able to analyse any supervisory learning model for the given input and output.	Yuqian Song, Head of APAC/EMEA Data Services and Head of Global Advanced Analytics Technology Solutions yuqian.song@citi.com
X	Robo-Advisor - Student defined project. A robo-advisor that will; classify customers based on their investment experience and risk appetite, recommend a portfolio of investments to customers, provide visualizations / analysis of the customer's investment portfolio, and provide a budgeting and savings dashboard as an extension or the above.	A solution or program which can accomplish the following: Customer Classification (via chat) Portfolio Selection (recommendation to customer) Visualization (portfolio analysis) Personal Finance Dashboard (extension on top of the above)	Ravinder Rao, Senior Vice President, GCT Data & Analytics ravinder.rao@citi.com