Group08 Report

 Visualizing Future of Crowd Funding with Yu’e Bao

PROPOSAL

POSTER

APPLICATION

REPORT

Yu’e Bao (余额宝) is an investment product offered by Alipay (支付宝), a mobile and online payment platform established by China’s multinational conglomerate Alibaba Group. In June 2013, Alibaba Group launched Yu’e Bao, in collaboration with Tianhong Asset Management Co., Ltd., to form the first internet fund in China. Since then, Yu’e Bao has become the nation’s largest money market fund and, by Feb 2018, has US$251 billion under its management. In Chinese, Yu’e Bao represents “Leftover Treasure”. Alipay users can deposit their extra cash, for example, leftover from online shopping, into this investment product. The money will be invested via a money market fund with no minimum amount or exit charges, with interest paid on a daily basis. While major banks offer 0.35% annual interest on deposits, Yu’e Bao may offer user 6% interest with the convenience and freedom to deposit and withdraw anytime via Alipay mobile app. Thus, Yu’e Bao became extremely popular in China.

Using various data visualization methodologies and techniques, coupled with user transaction level survival analysis and time-series clustering, this project aims to build an interactive tool on R Shiny framework, so as to unearth the underlying treasures of associations between Yu’e Bao’s user profiles, cash flow behaviour, time and other financial factors. This will let us understand more on how people in China invest their money through Yu'e Bao and gain insights that will be valuable to internet money market fund industry.

This report is separated into 9 sections. After this introduction, we will discuss our motivation and objective of this project in section 2. We have also reviewed some related literatures and explained our corresponding critics in section 3 of this report. Description of the dataset and our data preparation process will be covered in section 4 and 5 respectively, followed by the application introduction, installation and user guide in section 6. Next, in section 7, we will provide detailed explanation to the data exploration, analysis and insights we have gained through the Yu'e Bao dashboard. Finally, we will conclude the report by highlighting some of the key challenges faced throughout the project and possible future works to this project in section 8 and 9 respectively.

Motivation and Objectives

The dataset used in this project is released by a competition (The Purchase and Redemption Forecasts-Challenge the Baseline Competition) organized by Alibaba Cloud, TIANCHI Aliyun . The competition challenges its participants to train models to predict future cash flow of Yu’e Bao users, based on historical financial data from the government, Yu’e Bao and its user, and their user profiles. The results can aid Ant Financial Services Group, Alibaba Group’s affiliate company operating Alipay, in its business of processing cash inflow and outflow. Hence, most of the works done on this dataset are focused only on achieving the best score for predictive modelling. There is no works published at the time of this project with other data analysis or insights.

In view of this, we have chosen to provide an alternate analytical approach to the dataset by building a Shiny App with interactive features, and employing the data visualization methodologies, to visualize the data and its insights interactively. We also want to perform additional analysis of survival analysis and time-series clustering, and to generate dynamically visualizations of the analytical results. This visualization platform is built with RStudio, R programming language with rich libraries. Our final objectives aim to:

1. Provide interactive visualization and enable users to explore the dataset in various dimensions by different chart type and to gain corresponding insights
2. Dynamically generate different customer segmentation to analyze customer deposit and withdraw behaviour, enable users to explore and visualize the different of different Yu'e Bao user behaviour in different customer segments
3. Provide interactive visualization for time clustering and survival analysis, and enable users to perform the analysis with different input parameters

Literature Review

Despite the popularity of internet crowdfunding in China, there is little scholarly research in this area. Shen Lin Bing’ research study (2018) reviews the history of marketplace lending globally, with China as the emphasis, and further explores industries development and driving forces of China. A small part of the article draws relation to Yu'e Bao as an innovating financial investment service with reference to the visualization figure on the left. The figure tries to plot the Yield Rate of Yu’E Bao, Interest Rate for Current Deposit of Banks, and Fund Share of Yu’E Bao from May 2013 to May 2017. Yu'e Bao Big Data Report posted by big data forum (2014) provides the financial summary of Yu'e Bao and it's customer demographics in general. The webpage content provides a comical and simple summary of the objective. In the report posted by SINA Financial (2014) , by plotting Yu'e Bao total count of deposit and withdraw transactions from 2013-2014, it is concluded that average daily withdraw transactions count is three times higher than deposit.

All the analysis and visualization mentioned above is not interactive, though they provided a general summary of Yu'e Bao customer behaviour, there was little or no detailed analysis on the relationship between customer profile and their behaviour. With growing proportion of funds flowing to such investment tool, a centralize and interactive data visualization platform to analyze Yu’e Bao customer segmentation and behaviour will be very helpful for the healthy growth of its ecosystem.

References

Banner image credit to: China Money Network