The Accounting Analytics Capstone course is a compulsory capstone course for undergraduate students pursuing a 2nd Major in Accounting Data & Analytics. We showcase their completed projects below.
Driving sales growth by improving customer retention through effective tracking of buying patterns and evaluation of salesperson performance
The team was tasked to drive sales growth of Everyday Natural Fruit & Drink Pte Ltd by improving customer retention through effective tracking of buying patterns and evaluation of salesperson performance. They used RFM Analysis to rate Customer Health Score, YoY Seasonality Analysis to rate the Product Health Score, and Power BI to Forecast Sales through the Holt-Winters Additive Model.
Redefining Financial Storytelling via Interactive Visual Analytics In Power Bi
The team aimed to develop a Power BI dashboard to provide doctor-owners in Livingstone Health the financial clarity they need to drive business discussions and spot growth. The dashboard was designed to track expenses, profit breakdown, cashflow, revenue, and red flags.
To improve oversight and control over employee expense claims and automatically generate exception reports to flag potential anomalies
The current FairPrice Group claims management system presents opportunity for improvement. The team aimed to improve oversight and control over employee expense claims and automatically generate exception reports to flag potential anomalies. The team deployed various data analytics and AI tools for data exploration and transformation; anomalies detection analysis and investigate dashboard and statistical analysis.
Creating an effective monthly monitoring dashboard for FairPrice division to identify and flag high-risk claims that are non-compliant with SOPs
The team was tasked to create an effective monthly monitoring dashboard for the FairPrice division to identify and flag high-risk claims that are non-compliant with SOPs and perform employee expenditure analysis. They focused on detecting anomalies through different tests and quantifying their financial impact, as well as identifying employees with patterns of exceeding or maximising claim limits in amount and frequency.
Developing an Interactive Dashboard to Monitor Energy Performance of Various Industry Sectors
The team aimed to develop an interactive dashboard to track the energy performance of various industry sectors, to support the progress towards achieving Green Mark Certification. Their approach:
- Showcase clear benchmarks for different industry sectors based on Green Mark Certifications
- Highlight any anomalies or patterns
- Develop a website prototype featuring an interactive dashboard
Developing a Model to Make More Informed Decisions Related to Anomaly Detection
The team adopted a Three-Pronged approach for Data Preparation - Explicit Rules, K-Means Clustering and Random Forest, and combined the outputs to identify the common stocks flagged for anomalies across the selected period by all 3 models. They also developed a Tableau dashboard to provide a comprehensive analysis of each stock’s performance in terms of both selected market indicators and financial ratios.
Detecting Anomalies in Stock Data, Focusing on Companies Listed in the NASDAQ-100 index
The team utilised Machine Learning models like LongShort-Term Memory (LSTM) and ARIMA GARCH to predict stock price movement and built a Dashboard in PowerBI to present the analysis models, and to help with visualisation of the predictions.
Leveraging a Predictive Model to Estimate Box Office Performance to Aid Operations and Decision-Making
The team aimed to uncover the most influential factors (predictors) that drive box office performance in order to develop a predictive model to estimate box office for upcoming films, factoring in historical data and other relevant characteristics.
Box Office Forecasting & Movie Session Optimization
The team aimed to develop prediction models that give deeper insights to trends, forecast revenue through movie attributes and aids in optimising session scheduling. Their main approach involved identifying key factors affecting movie attendance, forecasting box office revenue and admissions using machine learning, and optimizing scheduling with goal
programming and data visualization.