Big Data Credit Report in Credit Risk Management of Consumer Finance

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Abstract

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.

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Smart Vehicular System based on the Internet of Things

Midde Ranjit Reddy, K. Srinivasa, B. Eswara Reddy (2018)

With the advent of smart city and its implications, different components of the smart city such as traffic, pollution monitoring, infotainment and others need to be considered as well for enabling the complete smartness. One of the major components of the smart city is regulation of traffic and its standards for smooth vehicular movement and it depends on the road conditions and environment. Road conditions provide valuable information for developing assistive systems for the vehicles and to be provided and trained with different road conditions for better accuracy and finally enabling the smartness around the system. With the increasing vehicular population around the globe and in developing countries like India, assistive systems based on Internet of Things (IoT) play a major role in developing smart systems for the vehicles. Once the population of the vehicles increases, assistive systems need a smart mechanism for parking as well. The proposed Internet of things (IoT) based system provides the user with different conditions of the road and a smart parking solution.

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A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks

Daniele Petrone, Vito Latora (2018)

The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties. The systemic risk is measured by statistics of the loss distribution, while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the European Global Systemically Important Banks. For a certain range of the banks’ capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses. This is the opposite of the diversification benefits postulated by standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability.