Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks

The focus of this paper is on the development of data mining algorithm for developing of predictive loan risk model for Nigerian banks. The model classifies and predicts the risk involved in granting loans to customers as either good or bad loan by collecting data based on J48 decision tree, BayesNet and Naïve Bayes algorithms for a period of ten (10) years (2010 2019) from using structured questionnaire. The formulation and simulation of the predictive model were carried out using Waikato Environment for Knowledge Analysis (WEKA) software. The performance of the three algorithms for predicting loan risk was done based on accuracy and error rate metrics. The study revealed that J48 decision tree model is the most efficient of all the three models.

Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection

Fraud detection is one of the core issues of loan risk control, which aims to detect fraudulent loan applications and safeguard the property of both individuals and organizations. Because of its close relevance to the security of financial operations, fraud detection has received widespread attention from industry. In recent years, with the rapid development of artificial intelligence technology, an automatic feature engineering method that can help to generate features has been applied to fraud detection with good results. However, in car loan fraud detection, the existing methods do not satisfy the requirements because of overreliance on behavioral features. To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. Experiments are developed based on actual car loan credit database to evaluate the performance of the proposed scheme. Compared with traditional automatic feature engineering methods, the number of features and training time are reduced by 92.5% and 54.3%, respectively, whereas accuracy is improved by 23%. The experiment demonstrates that our scheme effectively improved the existing automatic feature engineering car loan fraud detection methods.

The Impacts of New Financial Instrument Accounting Standards on Chinese Commercial Banks

On March 31, 2017, the Ministry of Finance revised and issued three new financial instrument accounting standards including Accounting Standards for Enterprises No.22- Recognition and Measurement of Financial Instruments. The banks of China’s A and H stocks have implemented the new standards since January 1, 2018. From January 1, 2021, the scope of implementation of the standards covers all non-listed commercial banks. The new financial instrument standards have undergone great changes in the classification and impairment treatment of financial assets, which is bound to have a profound impact on Chinese commercial banks. This article analyzes the impacts of new standards on Chinese commercial banks from the aspects of financial asset classification and measurement, impairment, credit risk management, profit and earnings management. Finally, the paper puts forward several suggestions and measures on the system and model construction, credit policy and post-loan risk management and talent training, in order to facilitate banks smooth the transition to the new standards.

Rice crop insurance in Indonesia: adaptation to climate and farm production support

Indonesia’s Rice Crop Insurance (AUTP) scheme has successfully protected farmers from significant crop losses due to natural disasters. However, the current amount of AUTP’s compensation is still unable to accommodate farmer’s financing costs (i.e., unpaid micro-loan and its interests) to support crops production. This results in higher micro-loan risk and hinder the sustainability of farming enterprises. In this regard, the existing People’s Business Credit (KUR) should supposedly be accessible as a micro-loan source to help farmers fund their farms. This study has two objectives: (1) formulate an integration scheme between KUR and AUTP, and (2) determine the appropriate insurance premiums to meet the farmer’s operational and financing costs. This research used 100,000 Monte Carlo Simulations using lognormal distributions with assumptions based on the results of focus group discussion and in-depth interviews with farmer groups, the local Agriculture Service, and micro-loan distributors, as well as the data from the Ministry of Agriculture from the period of 2018–2020. Additionally, Individual Area Yield Index (I-AYI) policy and loss ratio is used to determine and evaluate the new integrated crop insurance premiums. The study revealed that the farmers expect affordable, accessible, and beneficial insurance products with premium subsidies bundled with KUR. Therefore, the government should develop an integration of crop insurance with KUR and determine the affordable premium calculations along with the insurance companies. Based on the simulation results, the total pure premium is estimated around IDR 1 million for a minimum KUR loan of IDR 8 million (suitable for farming costs).

Research on Influence of Attribute Frame Effect on Loan Decision of Undergraduate and Risk Assessment Model of Undergraduate Loan Behavior

The article uses virtual lending scenarios to study the influence of attribute frame effect on undergraduates’ loan decisions. The results show that undergraduates have attribute frame effects in the three major areas of electronic products, life entertainment, and learning and training. There is a significant difference between the positive frame and the negative frame; that is, they are more inclined to make loan decisions under the positive frame. According to the research results, the article designs a loan risk assessment model based on Kohonen neural network and conducts simulation experiments. The experimental results show that the model’ classification accuracy is 72.65%.

Factors Affecting Campus Loans in Western China

Campus loans have become a part of the lives of Chinese college students. While such loans are convenient for students, they can also create considerable difficulties. In the context of unbalanced economic development between Western and Eastern China, this study aimed to understand the factors affecting the campus loan behaviors of college students in Western China. A sample of 568 undergraduate and graduate students from four universities in Western China was taken as the research object. Binary logistic regression and orderly logistic regression were used to study campus loan consumption factors. Students without state-subsidized loans were found to have stronger campus loan consumption intention and higher loan amounts, and recreational consumption was the main loan purpose. The factors affecting campus loan consumption included students’ family structure, parents’ education level, peer students’ consumption status, grade level, relationship status, and ability to assess loan risk. Based on the findings, suggestions are made for managing campus loan behavior from the perspectives of the individual, family, school, and government. This study can provide guidance for standardizing campus loans and adjusting college students’ consumption attitudes and behaviors.