scholarly journals Framework to predict NPA/Willful defaults in corporate loans: a big data approach

2019 ◽  
Vol 9 (5) ◽  
pp. 3786
Author(s):  
Girija Attigeri ◽  
Manohara Pai M M ◽  
Radhika M Pai
Keyword(s):  
Big Data ◽  
Banking System ◽  
Synthetic Data ◽  
Significant Rise ◽  
Classification Model ◽  
Critical Parameters ◽  
Financial Health ◽  
Data Platform ◽  
Systematic Identification

Growth and development of the economy is dependent on the banking system. Bad loans which are Non-Performing Assets (NPA) are the measure for assessing the financial health of the bank. It is very important to control NPA as it affects the profitability, and deteriorates the quality of assets of the bank. It is observed that there is a significant rise in the number of willful defaulters. Hence systematic identification, awareness and assessment of parameters is essential for early prediction of willful default behavior. The main objective of the paper is to identify exhaustive list of parameters essential for predicting whether the loan will become NPA and thereby willful default. This process includes understanding of existing system to check NPAs and identifying the critical parameters. Also propose a framework for NPA/Willful default identification. The framework classifies the data comprising of structured and unstructured parameters as NPA/Willful default or not. In order to select the best classification model in the framework an experimentation is conducted on loan dataset on big data platform. Since the loan data is structured, unstructured component is incorporated by generating synthetic data. The results indicate that neural network model gives best accuracy and hence considered in the framework.

Control method of spray curing system for cement concrete members based on the AdaBoost.M1 algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wei Yuan ◽  
Renfeng Yang ◽  
Jianyou Yu ◽  
Qunrong Zeng ◽  
Zechen Yao
Keyword(s):  
Machine Learning ◽  
Control Method ◽  
Current Control ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Critical Parameters ◽  
Cement Concrete ◽  
Content Type ◽  
Concrete Members

Purpose Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of members is vulnerable to random variation environment factors and anthropogenic interferences. This paper aims to introduce the machine learning algorithm into the spray curing system to optimize its control method to improve the spray curing quality of members. Design/methodology/approach The critical parameters affecting the spray curing quality of members were collected through experiments, such as the temperature and humidity of the member's surface, the temperature, humidity and wind speed of the environment. The C4.5 algorithm was used as a weak classifier algorithm, and the AdaBoost.M1 algorithm was used to cascade multiple weak classifiers to form a robust classifier according to the collected data. Findings The results showed that the model constructed by the AdaBoost.M1 algorithm had achieved higher accuracy and robustness among the two algorithms. Based on the classification model built by the AdaBoost.M1 algorithm, the spray curing system can cause automatic decision-making spray switching according to the member's real-time curing state and environment. Originality/value With the classification model constructed by the AdaBoost.M1 algorithm, the spray curing system can overcome the disadvantages that external factors greatly influence the current control method of the spray curing system, and the intelligent control of the spray curing system was realized to a certain extent. This paper provides a reference for applying machine learning algorithms in the intellectual transformation of bridge construction equipment.

scholarly journals Research and methodological basis for ensuring the financial security of banks in Ukraine

2018 ◽  
Vol 13 (4) ◽  
pp. 143-152
Author(s):  
Ievgen Balatskyi ◽  
Vita Andrieieva ◽  
Olesia Solodovnik ◽  
Volodymyr Lypchanskyi
Keyword(s):  
Banking Sector ◽  
Banking System ◽  
Financial Security ◽  
Financial Health ◽  
Growth Vector ◽  
Instrumental System ◽  
Dynamic Resource ◽  
Methodological Approaches ◽  
Strategic Perspective

In an unstable financial and economic, political and social context, a number of Ukrainian banks demonstrate downtrends in their profitability, liquidity, and solvency rates. With that, the financial health and the growth vector of the whole banking sector of the country in the strategic perspective precisely depends on the quality of measures taken to ensure the financial security of a banking institution.The article analyzes the concept of financial security of a bank from the standpoint of protective, dynamic, resource-functional, competitive and systemic approaches. The authors substantiate that the financial security of the banking system as a whole and that of an individual bank is an integral part of the financial security of the country. In addition, three methodological approaches to assessing the financial security of the country's banking sector are identified and described, namely instrumental, system and target-oriented and criterial ones. Based on theoretical and methodological analysis of scientific works and methodological approaches, a system of research and methodological support for the bank's financial security is proposed.

Discretionary Accruals in Italian Private Firms and Non-Linear Bank Loan Granting

2017 ◽  
pp. 83-99
Author(s):  
Elisabetta Mafrolla ◽  
Viola Nobili
Keyword(s):  
Financial Reporting ◽  
Banking System ◽  
Regulatory System ◽  
Discretionary Accruals ◽  
Bank Loans ◽  
Bank Loan ◽  
Banking Union ◽  
Private Firms ◽  
European Banking Union

This paper investigates whether and at what extent private firms reduce the quality of their accruals in order to signal a better portrait to the bank and obtain new or larger bank loans. We measure earnings discretionary accruals of a sample of Italian private firms, testing whether new and larger bank loans are associated with a higher (lower) quality of earnings in borrowers' financial reporting. We study bank loan levels and changes and how they impact discretionary accruals and found that, surprisingly, private firms' discretionary accruals are systematically positively affected by an increase in bank loans, although they are negatively affected by the credit worthiness rating assigned to the borrowers. We find that the monitoring role of the banking system with regard to the adoption of discretionary accruals is effective only when the loan is very large. This paper may have implications for policy-makers as it contributes to the understanding of the shortcomings of the banking regulatory system. This is an extremely relevant issue since the excessive amount of non-performing loans held by Italian banks recently threatened the stability of the European Banking Union as a whole.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Aulia Dwi Oktavia ◽  
Aam Alamudi ◽  
Budi Susetyo
Keyword(s):  
College Graduates ◽  
Influential Factors ◽  
Classification Model ◽  
Statistical Software ◽  
Waiting Period ◽  
Level Of Education ◽  
Economic Problems ◽  
Factors Influencing

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.

Simple Index to Assess the Calibration Quality of Safety Performance Functions Based on Multiple Goodness-of-Fit Metrics

2021 ◽  
pp. 036119812110088
Author(s):  
Raul E. Avelar ◽  
Karen Dixon ◽  
Boniphace Kutela ◽  
Sam Klump ◽  
Beth Wemple ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Synthetic Data ◽  
Calibration Procedure ◽  
Safety Performance ◽  
Absolute Deviation ◽  
Data Set ◽  
Safety Database ◽  
Simple Index ◽  
Safety Performance Functions

The calibration of safety performance functions (SPFs) is a mechanism included in the Highway Safety Manual (HSM) to adjust SPFs in the HSM for use in intended jurisdictions. Critically, the quality of the calibration procedure must be assessed before using the calibrated SPFs. Multiple resources to aid practitioners in calibrating SPFs have been developed in the years following the publication of the HSM 1st edition. Similarly, the literature suggests multiple ways to assess the goodness-of-fit (GOF) of a calibrated SPF to a data set from a given jurisdiction. This paper uses the calibration results of multiple intersection SPFs to a large Mississippi safety database to examine the relations between multiple GOF metrics. The goal is to develop a sensible single index that leverages the joint information from multiple GOF metrics to assess overall quality of calibration. A factor analysis applied to the calibration results revealed three underlying factors explaining 76% of the variability in the data. From these results, the authors developed an index and performed a sensitivity analysis. The key metrics were found to be, in descending order: the deviation of the cumulative residual (CURE) plot from the 95% confidence area, the mean absolute deviation, the modified R-squared, and the value of the calibration factor. This paper also presents comparisons between the index and alternative scoring strategies, as well as an effort to verify the results using synthetic data. The developed index is recommended to comprehensively assess the quality of the calibrated intersection SPFs.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

A monitoring framework for transparency and fairness in big data platform

2021 ◽  
Author(s):  
Karima Aslaoui Mokhtari ◽  
Salima Benbernou ◽  
Mourad Ouziri ◽  
Hakim Lahmar ◽  
Muhammad Younas
Keyword(s):  
Big Data ◽  
Data Platform ◽  
Monitoring Framework
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