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1. INTRODUCTION
1.1 Background & Motivation
According to the "The World Bank" survey, there are still 2 billion people in the world who still have no access to banking services. Therefore, Inclusive Finance is also regarded as a potential area of financial industry.1
The United Nations defines Inclusive Finance as a financial system that serves all sectors of society in an effective and comprehensive manner in “Financial Inclusion Systems”. Mainly includes four aspects: First, families and businesses get a wider range of financial services at reasonable cost. Second, stable financial institutions require tight internal control, market supervision. Third, the financial industry achieves sustainable development and ensures long-term financial services. Fourth, to enhance the competitiveness of financial services to provide consumers with a variety of options.2
The initial basic forms of Inclusive Finance are microcredit and microfinance.
Over the last two decades, microfinance has been receiving increasing global acceptance as a social inclusion mechanism and a poverty-alleviating development strategy. In the definition used throughout this paper, microfinance is related to access to financial services by low-income people, mainly those who are often excluded from the traditional financial system (Lavoie et al., 2011).
Among several definitions of microcredit found in the literature, we adopted that which refers to programs that extend small loans to poor people for self-employment projects that generate income (Waller and Woodworth, 2001). Therefore, microcredit is an important and integral part of microfinance.
Peer-to-peer Lending (P2P Lending) is a one of the microfinance service that
1 UFA2020 Overview: Universal Financial Access by 2020 : http://www.worldbank.org/en/topic/financialinclusion/overview
2 NPF Commentary May 28,2015 : https://www.npf.org.tw/printfriendly/15076
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matches funding provider and demander through the electronic marketplace. Because of the cost eliminating of intermediaries by traditional financial, making the supply of funds and demand match more efficient. P2P Lending let fund supplier get higher income and demander need to pay less cost.
Student P2P Lending has actually been done for years. For instance, Chinese financial technology company Qudian Inc. (趣店)3 which is a financial technology company for 500 million non-credit card groups in China. This company's predecessor was 趣分期. 趣分期 is a financial services platform that provides college students with phased consumption, covering nearly 30 million college students across China.
Provide services such as staging and cash consumption, support for installments. 4 However, on September 5, 2016 趣 分 期 was halted by Chinese regulatory authorities because of the chaotic development in the past few years. Declared to the outside that they provide installments or lending cash services to school students and low-income groups. In fact, providing high interest rates on loans.
Generally, students who seek loan service are usually cannot effectively control their desire, at the same time there is no stable source of income. Therefore, making a more accurate credit score for student P2P Lending loans is more important than any other P2P Lending. On the other hand, although there are many literatures about P2P Lending in domestic and foreign, the literatures about P2P Lending which focus on student groups are still almost empty. It is still a field with much research value.
Artificial Neural Networks (ANN) is one of machine learning systems. ANN are now used in many fields. ANN have been studied for many years. These models of ANN are primarily intended to mimic the human nervous system. ANN are composed of many non-linear computing units (Neuron) and many links between these computing
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units. These computing units are usually operated in a parallel and decentralized way, so that a large amount of data can be processed at the same time. This design can be used to deal with a variety of applications that require a large amount of data operations.
Because of the computational complexity of deep learning, before the 1990s, deep learning was limited by hardware and each training was costed normally several weeks or months to complete. Under the era of big data, in order to meet these data computing, processing needs. In 2015, Google released its open source machine learning system, TensorFlow. Of course, TensorFlow is not the only one, there are lots of similarities.
But, the artificial intelligence program which google developed, AlphaGo has beaten Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicaps. It makes TensorFlow be the most famous and successful one.
TensorFlow is an interface that allows people to design machine learning algorithms easier and really execute such algorithms. It can operates at large scale and in heterogeneous environments, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards (Abadi et al., 2016).
TensorFlow describes computation, shared state, and the operations that mutate that state by dataflow graphs. It can be used in model setting, training, and testing for ANN or deep learning. It can map the nodes of a dataflow graph across many different hardware in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs. (Abadi et al., 2016). For relatively complex processing using in ANN, the GPU version is better than CPU version because of the parallel operation.
On the other hands, Lian (2017) using ANN model to detect malicious behaviors on the Internet, Wu (2017) derives an ANN mechanism in predicting the return of carry
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trade. Trying to implement an ANN model in creating credit score for student groups is a brand new research field and a topic with research value. Furthermore, because of TensorFlow and GPU, the implementation and training of ANN model is more feasible and timeliness.
1.2 Purpose
Based on the motivations and background mentioned above, the objective of this study is to develop an ANN model that is used to create a credit score for students who try to borrow money but do not have qualifications to access to banking services on a P2P Lending platform.
To achieve the objective, we collect the real data of students P2P loans from our website5, which is a student P2P Lending platform on Internet. Moreover, the ANN model is developed by the infrastructure of TensorFlow and GPU. Therefore, we can finish the training in few hours. After the training process, hoping that the effective ANN model is able to give the correct credit score for students. In the future, the more data used, the more accurate credit score will be created by the model.
This is an experimental research, which combines machine learning and P2P Lending. The ANN mechanism is refer to the mechanism developed by Wu (2017). This research is not the same as traditional credit method used in P2P Lending field. Hope this research could contribute in both ANN development and students P2P Lending.
This experiment is also designed to verify the timeliness and effectiveness of the derived ANN mechanism:
Effectiveness: In order to effectively determine whether to borrow money, we hope
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ANN model can give a credit score as accurate as possible.
Timeliness: Demanders on P2P Lending are often very urgent to need money, so we want to establish an ANN model in hours. In the other words, the training time of ANN model have to be finished in hours.
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P2P Lending is an electronic lending platform, belongs to a part of the electronic marketplaces. According to Investopedia, it is a form of financial lending that takes away the role of traditional financial institutions as intermediaries and uses platforms to tie in with borrowers and lenders of financial demand. With the appearance of the Internet, electronic marketplaces are becoming more important in matching supply and demand. Even in complex and insecure conditions, electronic marketplaces can promote economic activity, greatly reduce information and transaction costs, and may replace traditional intermediaries in this way. Authors like Sarkar, Butler, and Stein field (1998) or Hagel and Singer (1999) argue that electronic marketplaces may lead to new forms of intermediation (Berger & Gleisner, 2010).
Organizing from available data and 李坤霖 (2017) pointed out that according to the degree of P2P Lending risk exposure, we can divide the business model into the following three mode: Information intermediary mode, Platform guarantee mode and Third party guarantee mode.
Table 1: P2P platform mode (李坤霖, 2017)
P2P platform mode Platform features Example
company Information
intermediary mode
Platform is only responsible for the examine and match the demand & supply.
After the success of the match, investors need to take their own risk.
SoFi, Future Finance , 逗 派