ANLY482 AY2017-18 T2 Group 05 Project Overview
| Background | Data Source | Methodology |
|---|
Contents
Project Introduction and Background
Founded by a group of visionary financial technology payment experts in 2011, Red Dot Payment (RDP) has grown into a trusted online payment company providing premium payment solutions and expertise to the brightest merchants across Asia Pacific. Bringing best-in-class practices, RDP is the trusted FinTech partner of banks, merchants, payment schemes, payment gateways, non-banking financial institutions, security and fraud management system providers.
In 2016, RDP grew to handle millions of transactions across the globe, managed by a dedicated 40-person strong operation - with headquarters in Singapore, and offices in Thailand and Indonesia. Its products and services include - RDP connect, InstanCollect, InstanPay, InstanPromo and InstanToken.
The following depicts RDP’s business model:
Business Problem and Motivation
While RDP handles millions of transactions across the globe, the company has not yet been able to derive any conclusive analysis from its transaction data. There is untapped potential that is unexplored here, as they operate online and thus have the advantage of easily collecting large amounts of valuable data about their merchants as well as their customers. In addition, there are many data points captured previously with little insights derived, resulting in business decisions being driven mainly by intuition. Thus, by providing our team with their existing data, RDP hopes to gather a deeper understanding of their data. Moving forward, RDP hopes to make more informed decisions utilising the patterns we identify from the data.
After meeting with our client to provide updates and generate insights for them, these are the following areas that our client is interested in:
Industry Strategy: RDP is interested to find if our data analysis can provide insights as to which markets or industries they should target in retaining or recruiting new merchants
Visual Dashboard: The creation of a visual dashboard to showcase our data analysis will be well anticipated by RDP
Meetings with Project Coordinator, Project Client and Team
Our team aims to meet our Project Coordinator, Professor Kam Tin Seong on average of at least once every week in order to ensure that we are progressing on the right track. We also met our client/project sponsor once a month to update them on our progress and validate our analysis, while our internal team meetings are held at least twice a week. All minutes can be found under "Project Documentation".
Project Objectives
Previously, we have identified the following objectives in our proposal:
1. To observe and develop meaningful insights from RDP’s datasets by performing exploratory data analysis.
2. To develop a visual dashboard. This will aid RDP in understanding some of their current business issues, as well as benchmark metrics and attributes that the company may not be currently analysing.
3. To formulate recommendations to improve their future business activities based on our findings.
Moving forward, on top of the above project objectives, we have further added on new objectives we aim to achieve in our study in terms of exploratory data analysis:
Objective 1: Identify relationship between approved/rejected transaction rates and (i) the month of the year, (ii) day of the week and (iii) time of the day.
Objective 2: Identify star and laggard merchants in terms of approved transactions
Scope of Project
| Task | Description |
|---|---|
| Gather Requirements | Confirm and gather sponsor requirements |
| Initial Research and Preparation | Conduct preliminary data exploration and define project objectives and scope |
| Project Proposal | Prepare project proposal and Wikipage |
| Data Exploration and Preparation | Ensuring that data is clean and can be analysed using analytical software; We also have to transform data (e.g. remove outliers and recode variables for analytical software to read); and conduct exploratory data analysis |
| Model Building | Through research findings and experience, we will attach and test suitable models to our data. We have used Interactive Binning and Time Series data analysis to generate insights |
| Project Revision (Mid-Term) | Assisted by RDP through obtaining feedback during our sponsor meeting |
| Mid-term Preparation | Prepare mid-term report, presentation and Wikipage |
| Model Validation and Refinement | Perform Time Series Clustering; Conduct independent sample t-tests (e.g. Ensure the results are similar when attached to different years of study) and refine analysis of data |
| Insights and Recommendations | Create visualisation from analysis results and formulate recommendations for our client |
| Project Revision | Assisted by RDP through obtaining feedback during our client meeting; Align our final deliverables with client requirements |
| Final Preparation | Prepare abstract and full paper, final Wikipage update and final presentation with necessary deliverables |
