Analysis of Internet Financial Risk Control Model Based on Machine Learning Algorithms

On the basis of traditional credit risk control, this paper proposes the demand and direction of a new credit risk control strategy based on machine learning and relying on big data. First, on the basis of introducing the basic algorithmic principles of machine learning, we give reasons for choosing machine learning models and build a machine learning-based Internet consumer finance credit risk control strategy model to provide theoretical support for the empirical analysis later. Second, we take the test data of Internet consumer finance S company as the research sample and carry out empirical analysis according to the machine learning-based Internet consumer finance credit risk control strategy model. The comparison of the training results is based on the comprehensive consideration of training time, validation set accuracy, TPR evaluation indicators, and interpretability of the results; it verifies the advantages of the machine learning model in screening the key influencing factors that cause the overdue performance of credit customers. According to the optimized credit risk control strategy, corresponding strategy suggestions are provided for the credit risk control of S company. The research results show that the prediction effect of the classification model based on traditional linear regression is generally lower than that of the model based on the classification algorithm based on machine learning, and there is a complex nonlinear relationship between platform default and its related influencing factors. The accuracy of classification and early warning results of the random forest algorithm is relatively high, and the detection rate of the decision tree model is relatively high, but the cost is also the highest. In addition, the accuracy of the four types of early warning models is relatively stable, reaching an average of 80%. This paper proposes a machine learning-based Internet consumer finance credit risk control strategy model. Its system, timeliness, and risk prediction capabilities provide new ideas and suggestions for Internet consumer finance companies to design risk control strategies.

Are Credit-Based Internet Consumer Finance Platforms Sustainable? A Study on Continuous Use Intention of Chinese Users

Internet consumer finance platforms (ICFPs), as a new Internet financial model, have emerged and been widely adopted by Chinese as well as global online shoppers following the rapid growth of e-commerce. This emerging financial tool is, however, luring younger generations deep into debt. It jeopardizes the Sustainable Development Goal (SDG) 1—No poverty, bringing the sustainability of this consumer financial model into question. To aid the sustainable development of ICFP industry, this paper distinguishes two antecedents of continuous use intention for ICFP users: (1) satisfaction, as a sustainable determinant; and (2) impulsive buying, as a non-sustainable determinant. We found satisfaction (b = 0.452) has a larger positive effect on continuous use intention compared to impulsive buying (b = 0.229). Therefore, ICFPs should weigh heavier on technology innovation instead of seducing consumers’ impulsive buying behavior. We also found that credit limit misconception poses the largest impact (b = 0.483) on impulsive buying. In the near term, governments and ICFPs may initiate public programs to improve ICFP users’ financial literacy in order to restrain their unsustainable impulsive buying behavior and cultivate their sustainable satisfaction on ICFP technology and service. Market regulators may build up institutional frameworks to tighten the abuse of financial platforms on credit issuing power and better foster sustainable entrepreneurship in this new financial platform business.

Can Facing the Truth Improve Outcomes? Effects of Information in Consumer Finance

This article explores when consumers avoid learning information about their credit scores and how viewing one’s credit score impacts future credit scores.

A Law and Behavioural Economics Analysis of High-Cost Lending in New Zealand

The most interesting recent developments in economics have come from behavioural economics, which attempts to model how people will behave in real life situations. The insights offered by behavioural economics can also be applied to law. This article discusses law and behavioural economics generally, and then applies it to the New Zealand high-cost loan industry. The high-cost loan industry is responsible for a great deal of harm in New Zealand, and in 2019 Parliament passed sweeping amendments to the Credit Contracts and Consumer Finance Act 2003 to insulate consumers from that harm. Law and behavioural economics suggest alternative strategies to those amendments that can address the harms while retaining the benefits that high-cost loans can provide. This article designed an experimental survey to test one such strategy, and the results and implications for policy are discussed.

EFFICIENCY IN SANCTION AND DISBURSEMENTS OF LOANS: A STUDY OF BIJAPUR DISTRICT CENTRAL CO-OPERATIVE BANK VIJAYAPUR

Every nation has to move towards the constant progression of growth. The Co-Operative Banks as part of the banking system operating in Rural and Urban areas in reduced scales, caters the financial needs of Rural and Urban sector particularly agriculture and allied activities, trade and commerce and consumer finance. This paper aims to study the efficiency in sanction and disbursement made by Bijapur District Central Co-Operative Bank Ltd. Vijayapur, Karnataka during 2013-14 to 2017-18.The overall performance is efficient in sanction and disbursements of advances as the total advances shows increasing trend and the bank is disbursing loans to every talukas, all areas, and each category of loans. The Bank has managed efficiently to allocate the funds to Priority Sector as well as Non-Priority sectors; this helps the Bank to maintain high profitability by adhering the guidelines of RBI in sanction and disbursement of advances. The performance of the Bank can improve further by enhancing the loan facilities, improving the quality, introducing new products, and attracting the unreached customers.

Enhanced Financial Business Competitiveness by Leveraging Technology and Innovation

The financial industry has developed rapidly with the emergence of financial technology companies. Moreover, the use of technology in the financial industry has developed rapidly. However, conventional financial companies face tough challenges in doing business. They have to adopt the technology to survive. The adoption of technology will increase the competitiveness of established financial companies against financial technology companies. The research aims to analyze the impact of technology on improving the work process of established financial companies. It examines the business model of established financial companies and financial technology companies. The work process is almost identical for all companies. The research applies a qualitative method. The chosen companies are multi-purpose financing for the purchase of two-wheeled and four-wheeled motorized vehicles. The total number of companies involved in the research is four companies. The result concludes that conventional consumer finance companies need to adopt technology into the work process to compete with financial technology companies. Technologies that can be used are Artificial Intelligence (AI), automation job allocation, chatbot, and face recognition. The work process is reviewed by digitizing, eliminating, and moving. Then, technology adoption will require a large investment. Hence, consumer finance companies can collaborate with technology companies, including financial technology companies.

A New Look at the Effects of the Interest Rate Ceiling in Arkansas

Arkansas has been a popular place to study the effects of rate ceilings because of its exceptionally low interest rate ceiling. This paper examines the effects of the Arkansas rate ceiling on credit use by risky nonprime Arkansas consumers, which are especially vulnerable to credit rationing because of the low ceiling. We compare the level and composition of consumer debt of nonprime consumers in Arkansas with that of prime Arkansas consumers and also nonprime consumers in the neighboring states. We find that nonprime Arkansas consumers are less likely to have consumer debt and, conditional on having debt, have lower, but not much lower, levels of consumer debt than prime Arkansas consumers and nonprime consumers in neighboring states. Types of credit used by nonprime Arkansas consumers tend to differ from those of our comparison groups. Notable is much lower use of consumer finance loans, traditionally an important source of credit for higher risk consumers. This finding suggests rate-based rationing of risky consumers. Also notable is lower use of bank credit despite federal preemption of the rate ceiling for banks. This result is consistent with banks’ traditional avoidance of risky lending.

Credit Risk Management of Consumer Finance Based on Big Data

In recent years, China’s consumer finance has developed rapidly, but the foundation is unstable, and the industry has serious problems of violent competition, excessive credit, and fraud. Therefore, we should attach great importance to the healthy development of consumer finance, especially the management of its credit risk. The application of big data credit investigation can provide early warning of potential risks and prevent the risk of excessive credit investigation. This paper starts with the definition of basic core concepts, such as traditional credit investigation, big data credit investigation, and consumer finance, analyzes the performance and causes of consumer finance credit risk, and combs in detail the relevant theories of the application of big data credit investigation in consumer finance credit risk management. The application of big data credit investigation has optimized the risk management process of consumer financial institutions, deepened the concept of Internet consumer finance, improved the risk management system, created a diversified credit information system, and strengthened the innovation of Internet consumer finance products and services. For example, credit scores provide the most intuitive quantification of consumer credit risk. For consumers with different levels of credit scores, different credit approval processes can be matched. For customers with high scores, the work process can be simplified without affecting the work results. It can reduce the workload of employees by 20% and increase the accuracy of customer credit risk prediction by 16%.

Penyelesaian Wanprestasi atas Dasar Force Majeure Akibat Pandemi Covid-19 dalam Perjanjian Pembiayaan Konsumen

Consumer finance is a financing institution that is officially still relatively new in Indonesia. Consumer financing agreements are agreements that arise in the day-to-day practice of the wider community. In a contractual relationship, the Covid-19 pandemic has become a point of debate between business actors who are bound by the contract. Debtors who have contractual obligations make the Covid-19 pandemic a basis for freeing themselves from their obligations to fulfill achievements. The purpose of this research is to find out the legal basis for the arrangement of consumer financing agreements in the perspective of contract law and to find out the consequences of force majeure for the parties to the consumer financing agreement in the event of a Covid-19 pandemic. The problems contained in this research are how the legal basis for the regulation of consumer financing agreements in the perspective of contract law and what are the consequences of force majeure for parties to the consumer finance agreement in the event of a corona virus outbreak. The method used is a normative research method by using a statutory approach and a conceptual approach. Based on the results of this study, it can be concluded that the Covid-19 pandemic currently occurring can only qualify as a forced condition which is not absolute or relative or it can be said that the debtor cannot make the reason for this pandemic as a reason for contract cancellation. This relative coercion is limited to delaying the implementation of the obligation to fulfill the achievement for a while, whereby the parties can negotiate the agreements that have been previously made.

Big Data Credit Report in Credit Risk Management of Consumer Finance

Traditional consumer finance is a modern financial service method that provides consumer loans to consumers of all classes. With the gradual improvement of China’s credit reporting system, big data credit reporting has effectively made up for the lack of traditional credit reporting and has been widely used in the consumer finance industry. In this context, the in-depth analysis of the specific application of big data credit reporting in the credit risk management of consumer finance and the strengthening of the research on the application of big data credit reporting in the credit risk management of consumer finance are urgently needed to be resolved in the economic and financial theoretical and practical circles’ problem. This article mainly studies the research on credit risk management of consumer finance by big data. The experimental results of this paper show that the model has a good forecasting ability, can distinguish between normal loan customers and default loan customers, and is suitable for practical personal credit risk control business. The prediction accuracy of the default model of the fusion model is 97.14%, and the default rate corresponding to the actual business is 2.86%. By combining the risk items such as the blacklist and gray list in the Internet finance industry, the bad debt rate and illegal usury can be well controlled to meet industry supervision.