Big data has a significant impact on Internet credit service providers as well. Initial results show that even people with bad financial status may be assessed, as well as those who have good credit. Credit rating agencies also rely heavily on big data. China's two official credit agencies, financial information of just 0.3 billion individuals, for instance. Others only have their name, date of birth, and address, therefore standard models are unlikely to provide good credit risk forecasts. This scenario makes it difficult for financial institutions to reach out to new customers.In this situation, big data is advantageous since it allows for infinite data access. Financial systems make use of transparent information methods to efficiently deal with credit risk. By combining the benefits of cloud computing and information technology, Market-based credit systems for both firms and individuals may be impacted by big data. With the use of mobile internet technology and cloud computing, non-internet-based traditional financial transactions may now use crystal pricing generation processes in cloud computing and big data.It establishes a good interaction between the regulatory authorities of the banking and securities sectors, in addition to providing information to both borrowers and lenders. Multi-dimensional variables arise when a firm has a huge data collection from many sources. However, maintaining large datasets is challenging; in certain cases, if huge datasets are not properly handled, They can appear to be a hindrance rather than a help. Data mining technology, which employs decision trees, neural networks, and Association regulations for managing a large volume of financial market data can help alleviate these issues. FinTech companies are more equipped than conventional financial institutions to handle large amounts of data because they can do it more consistently, efficiently, effectively, and at a lower cost. They can analyse and provide a greater number of customers with more in-depth services because of this. They can also benefit from systemic financial risk research and predictions. Individuals and small enterprises, on the other hand, may be unable to afford direct access to big data. Various information organisations, including professional consultancy businesses, relevant commercial agencies, and so on can use big data in this circumstance. In this paper, a survey is taken in An Effective Big Data Methodology for Large-Scale Data-Driven, Financial Risk Developing using Logical Regression and various methods.

Market-based credit systems, cloud computing, Big Data Methodology, Financial Risk, Logical Regression