Utilizing Big Data Technology for Online Financial Risk Management

1. Overview

As social technology advances, the Internet is gradually impacting every facet of human existence. Internet finance has discreetly infiltrated the Chinese market during the last ten years, coinciding with the increasing encroachment of online retail platforms like Taobao and JD.com into people's lives. Numerous online financial services, including P2P, credit, online banking, and third-party payment, have progressively surfaced since its inception. Due to its great speed and ease, the number of people using the Internet for financial investments is also growing daily. Internet finance is a two-edged sword that has increased dangers while also fostering the growth of China's financial sector (Lyu & Zhao, 2019). There is an information imbalance between lenders and borrowers in online credit transactions. Small and medium-sized microbusinesses will have financial challenges as a result, and borrowing costs are still high (Liu, 2021). Lenders will also be taking on more risk. For instance, the borrower's credit report is not full, making it difficult to distinguish between good and poor clients, as well as to calculate the borrowing limit and other risks associated with the payback cycle. As a result, businesses confront challenging problems that both lenders and borrowers cannot control: business risks. This significantly impedes the growth of businesses. Thus, the development of efficient technology to manage financial risks associated with the internet is imperative. Big data technology is crucial to the management of financial risk. Large-scale, dynamic change, and great processing efficiency are features of big data. The primary sources of the data are visitors, e-commerce, and social media sites. The internet finance business may assist practitioners make choices by enabling them to extract information from diverse incomplete, fuzzy, and enormous data sets that has guiding relevance for current financial behavior via the application of big data analysis technologies (Wang & Wang, 2022). Nonetheless, there are several obstacles facing the big data risk management technologies of today, chief among them being the irrational feature data design (Dutta & Saha, 2021). This study creatively mixes multi-source heterogeneous data structures and random subspaces, and it suggests a big data-based financial risk management model based on the aforementioned analytical findings. Through faster identification of new clients and a higher loan review pass rate, the initiative seeks to help lending firms more broadly.