IS484 IS Project Experience (FinTech)
Contents
Course Description:
- This is an SMU-X course designed in collaboration with participating Banks, FinTechs, and other FIs, to serve as project sponsors. Collectively, industry sponsors will supply a minimum of 5 projects ideas to select from.
- Students will form teams of 5 or 6, and select one the 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 sponsor/mentor and an SMU faculty supervisor.
- Sponsors 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 sponsor.
- Sponsors will specify the technologies to be used, including; development tools/languages, OS, database, 3rd party libraries, target deployment environment e.g. cloud environment.
- Student project teams will be expected to develop a working software application prototype, to be delivered to the sponsor at the end of the course.
Course Prerequisites:
1. Software Project Management (IS212) is a pre-requisite or a co-requisite.
2. Any two (2) track courses from the track that you are declaring for your project. One of these courses can be a co-requisite.
Project Timeline:
| Activities | Timeline | Term 1/ Term 2 | Action By |
|---|---|---|---|
| Project Sourcing and Registration | Week -14 to Week -10 | Form teams. Review the below set of predefined projects provided by Citibank, OCBC, NETS, UBS, and others. Fill up the Project Team Signup Sheet at the below link, listing your preferred projects. FT Track Coordinator will finalize the matching of teams to projects. | Students |
| Project Matching | Week -10 | FT Track Coordinator will finalize the matching of teams to projects. | FT Track Coordinator |
| Proposal | Due before the start of Week -8 | Submit your project proposals to your Track Coordinator(s). For mixed-track teams, both track coordinators need to review your proposal. | Students |
| Decision on Proposal | Week -4 | Your Track Coordinator(s) will confirm that the project has sufficient scope to fulfill your respective track requirements for IS Project Experience. | Track Coordinator, Students, (Optional: Sponsor) |
| Start of Project | Week 1 | Supervisor - Teams | Student |
| Midterm | Week 8 | Presentation | Students, Supervisor, Reviewer (Optional: Sponsor, Track Coordinator) |
| Finals | Week 14 to Week 16 | Presentation | Students, Supervisor, Reviewer (Optional: Sponsor, Track Coordinator) |
Project Team Signup Sheet:
AY2024/25 Term 2
https://docs.google.com/spreadsheets/d/1q-2qNkXGcjPxybU52s-1cazP5k4zhHTYRn7SKxz5Hjg/edit?gid=0#gid=0
Current Projects - FY2024/25 Term 2
ID, Term, and BA Status |
Sponsor / Business Vertical |
Project Description |
Project Scope |
Project Stakeholders |
Project #1 FY2024/25
Fulfills BA |
OCBC - Consumer Banking |
Fast Data Acquisition for Real-time
Analytics The key challenges that should be address are Low latency: Data needs to be streamed in near real-time without impacting the performance of the source transaction system. Data consistency: The data arriving at the ODS remains consistent with the source system, especially during high transaction volumes. Scalability: The ingestion framework must scale to handle increasing transaction volumes during peak hours, like flash sales or promotions etc. High Availability: The framework should tolerate failures and be resilient with high-availability. |
Project Inputs: • A low latency real-time ingestion framework using CDC and should support CRUD operations to be consistent with the source database. • Analytics dashboard that provides insights as • Customer activity and behaviour • Pending or failed payments • Total transaction volume per minute/hour • Documentation with detailed setup instructions for configuring the ingestion with no-coding and just using configuration changes. |
Project Coordinator: Lim Wei Ming |
Project #2 FY2024/25 |
OCBC - Front Office Relationship Managers |
Front Office Dashboard / Client meeting prep support The goal of this project is to leverage GenAI Technologies to develop a dashboard and client meeting preparation support system. The system will gather relevant information such as to-do lists, insights that clients would appreciate, follow-ups from previous meetings, outstanding document deficiencies, and areas requiring client feedback. All of this information will be consolidated into a meeting preparation pack, making it easier for professionals to prepare for client meetings efficiently. Front office staff often struggle to gather and organize all the necessary information for client meetings. This leads to inefficiencies and potential oversights. Therefore, there is a need for a system that can streamline the process of gathering, organizing, and presenting important information to professionals before client meetings. |
Project Inputs: • Project sponsors will share sufficient context so students can understand how/where this UI brings value to users. • Components required for effective client meeting preparation, including to-do lists, insights, follow-ups, document deficiencies, reviews, and client feedback. • Data or resources necessary for testing, such as sample client meeting data, client feedbacks, digital channel access data and relevant documents. Project Deliverables: • Dashboard: Create a user-friendly dashboard that allows Front office to input and access information for client meeting preparation. • AI Integration: Utilize GenAI Technologies to enhance the system's capabilities, such as natural language processing for analyzing meeting notes and predictive analytics for generating insights. • Data Analysis: Implement AI models to analyze data inputs and generate valuable insights, such as identifying patterns in client feedback or predicting potential document deficiencies. • Meeting Preparation Pack: Consolidate all relevant information, including to-do lists, insights, follow-ups, document deficiencies, reviews, and areas requiring client feedback, into a comprehensive meeting preparation pack. |
Project Coordinator: Bryan Lee Cheng Hui Project Mentor: Amila Silva |
Project #3 FY2024/25 |
OCBC - Group Operations & Technology |
This project aims to explore the concept of Zero-Knowledge Rollups, an innovative technology that addresses two significant challenges in blockchain transactions: privacy and efficiency. In simpler terms, Zero-Knowledge Rollups are like a secret code that allows you to do more things securely and quickly without anyone else knowing the details.In the world of digital currencies and blockchain, it's crucial to ensure that transactions are both private and efficient. Zero-Knowledge Rollups offer a promising solution by bundling many transactions together and proving they are valid without revealing specific details about each individual transaction. This approach enables blockchain networks to handle a large number of transactions at once, making them faster and more scalable. This also prove that transactions are valid without exposing the specifics. As a practical demonstration, we are able to develop blockchain applications such as: 1. Enhancing Privacy and Scalability in Blockchain Transactions using Zero-Knowledge Rollups There is a need for an improved solution to address the privacy and scalability challenges facing blockchain transactions. Traditional blockchain systems, such as those used in cryptocurrencies, often struggle to handle a high volume of transactions while simultaneously ensuring the privacy of participants. Existing blockchain architectures suffer from limited scalability, resulting in congestion and increased transaction delay during peak usage periods. Moreover, transaction details are often visible to malicious actors, compromising user privacy and confidentiality. |
Project Inputs: • Project sponsors will share sufficient context so students can understand its use cases, discussing the benefits and limitations of implementing zero knowledge rollups and how it can benefit end users. • The project details, explanation of the zero knowledge rollups and other useful details will be shared. Project Deliverables: • Students will dive into the technical aspects of Zero-Knowledge proofs, learn about the challenges of implementing Rollup solutions, and examine real-world examples where this technology has been used successfully • Investigate where zero knowledge rollups can be applied in banking environment. Students will study relevant academic resources, examine existing Zero-Knowledge Rollup implementations • Create simulations or proof-of-concept prototypes to explore the practical aspects
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Project Coordinator: Ravindra Kumar Project Mentor: Jorden Seet |
Project #4 FY2024/25
Fulfills BA |
Citi - Exchange Traded & Cleared Derivatives |
Collateral Optimization for CCP Margin Calls Citi provides its clients with clearing services on several global Central Counterparty Clearing Houses (CCPs). Clients can post eligible currency (cash) & financial instruments (bonds, treasury notes, securities, commodity warrants, etc.) as collateral to cover margin calls. Citi, as a clearing member, will then utilize some of these assets to cover the corresponding margin calls with the CCP. Each CCP has specific requirements regarding the types of collateral it accepts, applying different haircuts and collateral fees based on asset class. Additionally, transaction costs are incurred when depositing, substituting or withdrawing collateral. Optimizing the allocation of available collateral across different CCPs can minimize costs and increase efficiency. This project aims to develop an algorithm that optimizes the allocation of available collateral to various CCPs based on eligibility, collateral costs, haircuts, and transaction costs, taking into account frequent changes in available collateral due to client activity. Citi’s clearing services require optimal collateral allocation to different CCPs in order to minimize costs and comply with eligibility requirements. The current process involves multiple variables such as collateral eligibility, haircut rates, collateral fees, and transaction costs. The objective of this project is to build a solution that optimizes collateral allocation for margin calls at each CCP while minimizing associated costs and fees. |
Project Inputs: Students will be provided with: 1. A file containing a list of available collateral. 2. A file that lists the margin calls required at each CCP. 3. A file containing static data regarding eligible associated haircut rates, fees, and transaction costs at different CCPs. All required data is in public domain. No Citi proprietary data is required nor will be shared for this project. Project Deliverables: 1. Optimization Algorithm: A solution to optimize the allocation of available collateral across multiple CCPs based on eligibility, costs, and transaction considerations. 2. User Interface: A basic user interface to input data and visualize the optimized collateral allocation across CCPs. 3. Collateral Movement Report: A user report that lists the optimal collateral allocation and the related asset movements. 4. Documentation: Detailed documentation explaining the methodology, logic behind the optimization algorithm, and any assumptions made. 5. Presentation: A final presentation demonstrating the optimization tool, its functionality, and potential real-world applications for Citi's operations. |
Project Coordinator: TBA Project Mentor: Nirav Parikh |
Project #5 FY2024/25 |
Citi - Markets & Trading |
Synthetic Market Generator for Algorithmic Trading One of the key challenges in training trading algorithms is the limited availability of real-world historical data specific to certain securities. This scarcity of data can lead to overfitting and suboptimal performance in machine learning models. Moreover, it is difficult to find data that accurately reflects specific market conditions, including varying volumes, trends, and volatility levels. This project aims to address these challenges by developing a synthetic market simulator capable of generating market data tailored to specific securities and market conditions. The simulator will be parameterized to model various patterns, volatility levels, and market trends, providing a flexible tool for creating synthetic datasets. These datasets can then be used for training and testing algorithmic trading strategies, avoiding the pitfalls of limited real-world data. Current trading models face the challenge of limited historical data for specific securities and market conditions. This project seeks to build a synthetic market data simulator that allows traders and researchers to generate customized market data based on chosen parameters such as volatility, trend strength, and volume. This will provide a larger and more diverse dataset to train trading algorithms, leading to better generalization and performance in various market environments. |
Project Inputs: Students will be provided with: 1. A representative set of market data for a given security (historical OHLCV data). 2. Parameters to tune the synthetic data generation, including market patterns, volatility, trends, and volume. All required data is in public domain. No Citi proprietary data is required nor will be shared for this project. Project Deliverables: 1. Synthetic Data Generator: A working model that simulates and outputs synthetic market data based on input parameters. 2. Parameterization: A set of controls to modify the synthetic data generation, including trend types, volatility levels, and other key market conditions. 3. Transaction Output: The output will be a file containing the generated market data, with fields such as Open, High, Low, Close, and Volume (OHLCV). 4. Documentation: Detailed documentation explaining how the synthetic data is generated, how to use the parameterization tools, and how the simulator can be applied in algorithm training. 5. Presentation: A demonstration of the synthetic market simulator, including use cases for improving trading algorithm performance. |
Project Coordinator: TBA Project Mentor: Nirav Parikh |
Project #6 FY2024/25 |
Citi - Investment Banking |
Deal Review Committee Using LLM Agents In investment banking, evaluating deals for new clients involves multiple dimensions of analysis. Banks must assess the market potential, competitive positioning, and risk profile of a client’s business, while estimating revenue and cost projections. Additionally, ensuring compliance with regulatory standards and aligning deals with the bank’s strategic objectives are crucial aspects of decision-making. This project proposes the development of a framework utilizing Large Language Model (LLM) agents to simulate a virtual committee of financial experts. Each LLM agent will be specialized in specific areas such as risk assessment (credit, market, operational, regulatory, reputational), revenue estimation, compliance, and strategic alignment. By simulating the expertise of real-world financial analysts and risk managers, this system will provide a comprehensive review of deal proposals for new clients. The goal is to enhance decision-making, mitigate risks, ensure regulatory compliance, and foster profitable client relationships, helping banks balance opportunity with risk for long-term sustainability and growth. Investment banks face challenges in evaluating deal proposals due to the need for multi-faceted analysis across revenue potential, risk assessment, regulatory compliance, and strategic fit. This project aims to develop a framework leveraging LLM agents to provide a holistic and expert-driven approach to deal review, improving both the efficiency and accuracy of decision- making processes. |
Project Inputs: Students will be provided with: 1. Training data from previous deal reviews, including analysis from financial analysts and risk managers. 2. Access to relevant market data, risk factors, and financial models. 3. Strategic guidelines and risk appetite documentation for new deals. Note: Anonymized Citi internal data (no client data) will be required for this project subject to approvals and potential NDA agreements. Project Deliverables: 1. LLM-Based Expert Agents: A framework of specialized LLM agents trained to simulate expert perspectives in areas such as revenue estimation, risk analysis, compliance, and strategic alignment. 2. Virtual Committee Decision Process: A mechanism for synthesizing the insights from different agents to form a comprehensive recommendation on deal approval and conditions. 3. Decision Support System: A tool that provides deal recommendations, approval conditions, and risk mitigation strategies based on the committee’s output. 4. Documentation: Comprehensive documentation outlining the design, methodology, and decision-making process of the virtual committee of LLM agents. 5. Presentation: A final presentation showcasing the framework, its decision-making process, and its potential impact on the bank's deal review process. |
Project Coordinator: TBA Project Mentor: Nirav Parikh |
Project #7 FY2024/25 Fulfills BA |
Revolut - Digital Investing |
Bespoke Robo Advisory Platform for Retail Users Create a digital investment platform that allows users to invest into through Revolut’s Robo-advisory solution coupled with the Users own inputs relating to Risk Appetite, preferred asset class mix, single name stocks and investment amount. The ask is to create a web app that: • Allows users to include more inputs before letting Robo advisory take over the investment of users funds. • List of investment instruments and asset classes available • Visualize the performance data using charts, tables etc. in a simple, uncluttered fashion. Robo-advisory is a useful solution / tool for beginning and intermediate investors who wise to utilize ”Robos” to optimize users funds and invest accordingly based on black box algorithms built by the Robo Advisory company. The Problem is that users have little or no say in which specific asset class, industries, or single name stocks should the investor have a preference in whilst offering the investor expertise of the Robo Advisory perform dynamic asset re-allocation as and when the need to do arises. So the idea is to create a platform which is a hybrid of a Robo-Advisor and a full manual trading platform |
Project Inputs: • Project sponsors will share sufficient context so students can understand how/where this platform brings value to users. • The mock raw data files, explanation of this data structure and other useful details will be available. Project Deliverables: • Working App that provides intuitive UI/UX. • This App should be a standalone application that can be easily incorporated in a larger application. Freedom to use visualization & analysis tools, technology of the team’s choice.
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Project Coordinator: [ TBA ]
Project Mentor: Abhinav Suryavanshi |
Project #8 FY2024/25 Fulfills BA |
Singapura Finance -Regulatory Compliance |
Customer Profiling Application Customers are on-boarded to the bank’s system after performing checks and validation for know your customer and anti-money laundering (KYC/AML) compliance. The solution should extract information about existing customers, run checks and document results. It will score each customer based on given parameters. The parameters may change over time, so flexibility to adjust the parameter will be required. The current approach to handling AML scoring and documenting of customer profile is manually done by staff. It is not consistent and prone to oversight and missing filed information. |
Project Inputs: • Project sponsors will share context so students can understand how/where this digitalization can add value to the organization. • The mock up data and parameter for scoring will be shared with explanation on the digital filing requirements. • Potential to use ML Tools to profile customer Project Deliverables: A solution or program which can accomplish the following: • Take in a customer information from the banking system • Profile the customer information using available search/information engine/service provider. Obtain the results • Provide data visualization of the results obtained • Score each customer profile • Store the results for historical review or audit review requirement. • Allow customization of the scoring • Easy search and identification of customer profile documents collated. • Allow for triggering of review on customer information based on score |
Project Coordinator: (TBA) Project Mentor: Winny Ho |
Project #9 FY2024/25
Fulfills BA |
Singapura Finance - Risk Management
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Consumer Loans Credit Scoring The bank implemented an online straight through loan application (Mortgage) using government provided information, with customers consent. The system does not identify nor prioritize customer profile, hence good/great customers are left together with the majority. Solution will use information obtained by the government data source and generate a credit score each application. An application may have more than one submission (Multiple owners). The solution should provide automated recommendation for improved loan rates for of better scoring customers. It should also document and recommend follow-up for lower scoring applications.
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Project Inputs: • Project sponsors will share context so students can understand how/where this digitalization can add value to the organization. • The mock up data and parameter for scoring will be shared with explanation on the digital filing requirements. Project Deliverables: An app that provides the following: • Take in a customer information submitted via online forms/government data. • Profile the customer information • Analyze the results obtained and score each application • Allow customization of the scoring using various data points available. • Provide data visualization of the results obtained • Results will be sent to back-room for processing or automated escalated actions. |
Project Coordinator: (TBA) Project Mentor: Cindy Ng |
Project #10 FY2024/25 |
Tiger Fund - Fund Management |
Using Artificial Intelligence for Effective Stock Screening This project focuses on using artificial intelligence (AI) to develop an effective stock screening tool that assists investors in identifying potential buying or selling opportunities in the U.S. stock market. With the vast amount of data generated daily, AI can automate the screening process by quickly analyzing stock trends, sector performance, and user-defined parameters to detect valuable market opportunities. The system will leverage machine learning algorithms and technical indicators to filter stocks based on investor preferences, such as undervaluation, technical patterns, or strong market trends. The goal is to streamline stock selection and improve decision-making efficiency for portfolio managers. Investors face a challenge in sorting through the immense quantity of stock market data to identify opportunities for profitable trading. The manual stock screening process is time-consuming and prone to human error, especially when considering various technical indicators and market conditions. This project aims to address these issues by developing an AI-powered stock screening system capable of efficiently analyzing stock data, detecting strong market trends, and automating the identification of potential buying or selling opportunities based on predefined criteria. |
Project Inputs: • Project sponsors will share sufficient context so students can understand how/where this project brings value to users. Project Deliverables: • AI-Powered Stock Screener Application: A functional application that can analyze large datasets and apply user-defined parameters to screen stocks. • Backtesting: To engage in backtesting of the model in order to ensure the reliability of the system • Market Trend Detection Module: A feature that detects strong market trends or significant changes in sector performance. • Sector Analysis Tool: A tool to conduct in-depth analysis of sectors to identify potential opportunities for buying recommendations. • Sentiment Analysis Engine: A system that scrapes and analyzes sentiment data from news articles, social media, and financial reports, and integrates it into the model. • User Interface for Stock Screening: An interactive interface where users can input their screening criteria and view stock recommendations in real-time. |
Project Coordinator: [TBA] Project Mentor: [TBA] |
Project #11 FY2024/25
Fulfills BA |
Tiger Fund - Fund Management |
Using Artificial Intelligence to Time the Market The project aims to build a machine learning-based system that assists portfolio managers with accurate market timing by predicting the prices of major Exchange-Traded Funds (ETFs) such as SPY (S&P 500 ETF) and TLT (Treasury Bond ETF). The core component of this system will be an AI model, potentially using a Long Short-Term Memory (LSTM) neural network, which will predict future prices based on a blend of economic, fundamental, sentiment, and technical data. The model will be designed to continuously learn and adapt to evolving financial environments, making real-time predictions. The data inputs will be aggregated from reliable financial sources like FRED, World Bank, Yahoo Finance, and sentiment analysis from news outlets, social media, and financial reports to develop a comprehensive model. Accurate market timing is one of the most challenging tasks for portfolio managers. Market prices fluctuate based on a wide range of factors, including economic indicators, fundamental analysis, market sentiment, and technical trends. Traditional financial models often fail to capture the complexity and rapid changes in market dynamics. This project seeks to bridge the gap by leveraging machine learning to forecast ETF prices more accurately and continuously adapt to changing market conditions. |
Project Inputs: • Project sponsors will share sufficient context so students can understand how/where this project brings value to users. Project Deliverables: • AI Prediction Model: A machine learning model trained to predict the prices of SPY and TLT based on technical, economic, fundamental, and sentiment data. An AI system capable of retraining itself as new data becomes available, allowing for adaptation to market changes. • Backtesting: To engage in backtesting of the model in order to ensure the reliability of the system • Sentiment Analysis Engine: A system that scrapes and analyzes sentiment data from news articles, social media, and financial reports, and integrates it into the model. • Risk Appetite Indicator: A composite indicator driven by sentiment analysis, measuring market risk aversion or appetite to inform predictions. • Data Pipeline: Web scraping and integration pipeline to pull continuous data from FRED, World Bank, Yahoo Finance, and other relevant sources. • Dashboard Interface: A user-friendly dashboard to display model predictions, risk appetite indicators, and relevant metrics. |
Project Coordinator: [TBA] Project Mentor: [TBA] |
Project #12 FY2024/25
Fulfills BA |
UBS - Equities |
News Screener For Relevant Investment Opportunities Bankers and Investment counselors (ICs) develop deep relationships with clients and provide them with relevant investment and opportunities advices based on client’s needs. The ask is to
create a tool that: Summarize these views to a digestible format for Client Advisor, Ensure each data point has a source link, that would enable the Client Advisor to verify that the subject is indeed relevant to preferences of their client. Freedom to use visualization & analysis tools, generative AI, APIs and technology of the team’s choice. However the solution should be hosted in an Azure Cloud. Example: Tools scans news from the ‘Financial Times’ for relevant sector ‘Real Estate’ and region ‘China’, Based on the news coverage, it identifies the current market trend and effected companies and instruments relevant to those companies and provides a relevant view to client advisors. Given current volatile world and lots of information being generated every moment, analysts require smart intelligent tools to sort through all those and provide clients with relevant investment advices on timely manner. The tool is to automated way to scan news, provide digest about the news in categories like sector, region and entity. Also to provide relevant sentiment of the news. |
Project Inputs: • Project sponsors will provide 5 entity names, and suggested data sources on which the output should be created • Project Sponsors will review at regular intervals the outputs to refine requirements and usability of output. • We will provide support on how to perform identity matching for the entities. • Team can use AI tools to discover digest of news (e.g. news is about ‘Real Estate’ sector.) Project Deliverables: • Working dashboard that provides a real view of relevant sectors and entities. • View provides contextual correlated sentiment assessment of entities. • View provides trigger notifications when significant activity threshold is breached (optional). • View provides collected historical information and perform system end to end risk and returns on entities. • Ability to collect data from different sources.
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Project Coordinator: Kumar, Ajith-A Project Mentor: Hossain, Mohammad-Jahangir |
Project #13 FY2024/25 |
UBS - Mobile banking |
Modern web application and Native Mobile application for Portfolio viewing of a banking customerThere are already a quite a lot of Banking asset viewing and portfolio viewing apps in the market currently. Having a great user experience for such apps are key for success of any business. User experience is garnered from Customer experience strategy, research and design. Understanding user behavior and human computer interaction techniques are key in designing and implementing next gen user experience application is key. In this project you will produce native iOS/Android mobile application and web application using latest technology which inculcates great customer experience design , user experience design and wireframes. There are already great deal of research materials on this subject, so need a both balanced academic view and already existing app view to come out with a great application to do portfolio viewing of a Banking clients assets. The backend may have mock data to begin with so not really expecting the app to work end to end. Key success criteria are to have a great visual and customer experience for these apps. This project aims to develop a banking portfolio viewing application which targets the upcoming generation of banking application consumers, ie. Gen Z and Millennials. Additionally, we will enhance the user experience of our application by implementing specific design considerations obtained through rigorous user research and analysis of human-computer interactions. |
Project Inputs: • Technology services mentor will provide insights on deep knowledge on the subject • Help students to formulate the solution ideation • Provide the expertise where necessary for the group to produce a industry standard solution Project Deliverables: Must Have: • Working Mobile application for asset viewing targeted at Gen-Z age group • Design and develop a prototype which has a high customer experience design and HCI. Nice To Have: • Fully working backend is not a requirement. Application can have a static data to power the application.
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Project Coordinator: Kumar, Ajith-A Project Mentors: Sanghavi, Seema |
Project #14 FY2024/25 |
UBS - Regulatory Compliance |
FinRegScanner Banks have to adhere to multiple market and exchange related financial regulations. The compliance function has to be vigilant in identifying the regulations that are published by various countries, regions and by industry bodies and implement them on time. If a bank is not regulatory compliant, that could lead to various repercussions from financial penalties, reputational impact to even posing a risk to the financial system as a whole. Once a regulation is identified, few companies look out for vendor solutions and few companies build solutions in-house and this often requires a lot of coordination among various functions/teams to ensure that the regulation gets adhered to on time. Hence, it would great if technology can help to assist and simplify the regulatory project management and implementation. Right from identification of a financial regulation that the bank needs to adhere to, till the implementation, is very complex to manage, time intensive and costly affair. Build a solution that can act as an assistive tool to Compliance function of an organization to help detect, plan and manage regulatory impacts in a timely manner within the organization. |
Project Inputs: • Project Sponsors will provide information about regulatory data sources and will also give a brief overview of Financial Regulatory Landscape and relevant support wherever required • Project Sponsors will review the output at regular intervals to provide feedback and to refine requirements Project Deliverables: Must Have: 1) Identify upcoming regs and their regulatory deadline 2) Summarize the requirements for each impacted business division and able to query the regulatory text and get answers 3) Apply a chatbot feature to query the QnA 4) Provide a dashboard to visualize the key features of the regulations. Nice To Have: 1) Build a machine learning model to classify the regulation, identify the potential impacts for each business division 2) Provide a comparison with other regulations of similar nature
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Project Coordinator: Kumar, Ajith-A Project Mentors: Kumar, Phanindra; Kumar, Ajith-A |
Project #15 FY2024/25 |
UBS - Wealth Management |
Gamified Financial Literacy Application Financial Literacy is essential in wealth management as it enables individuals to make informed decisions about growing, protecting and preserving their assets. The ask is to create a gamified financial literacy app that teaches essential money management skills through interactive challenges, quizzes, activities, simulations and make financial education engaging and fun for all ages. Users earn badges and unlock new levels as they progress through next stages in their learning paths consisting of topics ranging from time value of money, insurance, emergency fund, asset allocation, budgeting, saving, investing etc. Financial Literacy is essential in wealth management as it enables individuals to make informed decisions about growing, protecting and preserving their assets. The ask is to create a gamified financial literacy app that teaches essential money management skills through interactive challenges, quizzes, activities, simulations and make financial education engaging and fun for all ages. Users earn badges and unlock new levels as they progress through next stages in their learning paths consisting of topics ranging from time value of money, insurance, emergency fund, asset allocation, budgeting, saving, investing etc. |
Project Inputs: • Project sponsors will share sufficient information regarding the various aspects of personal finance • Mentors will guide the students on building learning paths and scenario simulations • Mentors will review the progress at regular intervals to refine requirements and usability of the app. Project Deliverables: Must Have: • Fully functional responsive / mobile native application with at least 2-3 financial literacy learning paths. • Excellent user experience with security features implemented. • Real time analytics to track progress with personalized insights and badges • Admin dashboard showing the learning progress, and the badges earned by all the users. Nice To Have: • Integration with ChatGPT or other LLMs for personal finance scenario simulations |
Project Coordinator: Kumar, Ajith-A Project Mentors: Gopalan Ramakrishnan |
Archived Past Projects and Groups
AY2024/25 Term 1
https://docs.google.com/spreadsheets/d/1IDAhC4JiK3RuKnIDQMG5UjJ6I1IiImo81Lu13wAuUxE/edit#gid=491663198
Past Project Descriptions
https://docs.google.com/spreadsheets/d/1f7r2y1n6USWAYVTVyWkZkYxJt7HCoxlc-z3jAGzX7LQ/edit?gid=0#gid=0
