How machine learning helps P2P lending industries, both fiat and crypto markets
Recently, among various markets, marketplace lending has grown by leaps and bounds with the help of artificial intelligence (AI) or machine learning (ML), two buzzwords in the technology market. AI and ML have enabled this sector to automate many processes, reduce operating costs, and bring more convenient conditions for both lenders and borrowers. In the media, AI and ML have been used interchangeably, but the two concepts are quite different. Before delving into the subject of how ML helps P2P lending industries, let us first briefly look into the concepts of AI and ML to get rid of any confusion that may arise.
AI is a broad concept in any context that in ways of behaving to handle a certain task, we think, there exists human intelligence involved in the process. So that, the concept of AI is more about human aspirations to transfer capabilities that humans possess to machines. Ways of transferring such capabilities consist of a considerable measure of technology advances, and ML is just one among those technological innovations. Hence, ML can be understood as a branch of AI, and is defined as a strategy for allowing a computer programme to learn from historical data and improve through experience.
ML has had a profound impact on the P2P lending sector. Below are several significant benefits from the use of ML in addressing issues in this industry:
1. Defaulter detection
A challenging issue worth mentioning first in the P2P lending sector is defaulters and bad loans. An early deduction of defaulters can significantly decrease the credit risks, which, in turn, not only reduces the total number of actual defaulter cases but also increases the quality of the P2P service. Hence, financial institutions are motivated to identify and eliminate as many defaulters and bad loans as possible.
The previous method to reduce the risk of loan defaulters was to employ manpower spending countless hours to validate and approve each application manually. However, ML with its predictive analysis, can help lenders sooner identify the borrowers who are likely to default. This problem is categorised as Abnormal Detection, which has recently been addressed with many successful solutions in ML.
2. Accelerating the loaning process
2.1. Reducing manpower
Among other business processes, money lending might be the most time-consuming and tiresome task to accomplish as it is very complicated. ML comes in handy here by adding automation to some tasks in the process such as screening applications or categorizing promising customers. ML not only speeds up the process, it may also increase the transparency in making decisions as it is built from a mathematical model, thereby guaranteeing there is no bias for either the lender or the borrower. As more parts in the workflow are automated, the space for human error is being reduced, and the time periods for handling mammoth workloads in the loaning process are shortening.
2.2. Using natural language processing
Any lending process involves a lot of documentation, the manual handling of which requires a lot of time and is prone to errors. ML can automate this task by using natural language processing which has recently become a very hot topic in the ML research community. It helps computers communicate with humans in their own language by its built-in capabilities such as reading text, hearing speech, interpreting and converting between speech and text, and detecting sentiment. The research in this field has been developed for most languages, this can provide crucial support to the automation of the P2P lending model on a global scale.
2.3. Reducing Operating Costs
A financial institution in the P2P lending industry incurs operating costs from various activities such as managing connections or collecting information from borrowers and lenders. To alleviate the operating cost, ML can build a chatbot to accumulate and consolidate data from clients. A chatbot is an application with many integrated ML techniques which can simulate a conversation with a human using natural language by means of either text or voice. For example, chatbots have been widely used to answer frequently asked questions by many FinTech firms to reduce customer service costs.
Conclusion
The advent of ML has undoubtedly made a significant change in the way the lending sector operates today. Its integration in the P2P industry has been more and more reinforced by a continuation of improvement coming from various research studies. While the traditional form of lending has many barriers like retaining formalities or demanding huge manpower, which limits the expansion of this industry on a global scale; the new form of lending which includes advanced ML techniques can remove those barriers and thus pave the way for the P2P lending model to expand into a much larger market.
Written by Lamb Le
About the author: Lamb Le is a PhD candidate at the Computational Optimisation and Learning (COL), University of Nottingham. He has M.Sc. degree in Computer Science at Myongji University, Kyongkido, Republic of Korea, 2015. His research interests consist of data mining, machine learning, evolutionary algorithms and natural language processing.
P2PGO Tech. company is grateful to have Lamb Le as an expertise advisor in AI to support the R&D of the project.
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