Bank loan analysis using customer usage data: A big data approach using Hadoop

Yadav, Shweta; Thakur, Sanjeev

doi:10.1109/tel-net.2017.8343582

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…Moreover, the detection of spam using data mining techniques also employs the use of machine learning [138]. In addition, Hadoop and machine learning algorithms are used by banks for analysis of loan data to check the reliability of lending organizations, thereby increasing profitability and innovation [139]. Recent studies in the field of construction, city, and property management specially reported that compatibility, interoperability, value, and reliability are critical factors of digital technology adoption and implementation [140][141][142][143][144].…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

116

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

116

View full text Add to dashboard Cite

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“…Research on entrepreneurial enterprises shows that the decisions of founding controllers play a key role in influencing the core strategic decisions of enterprises, and this influence will still exist for a long time after their departure. As a cognitive framework, the identity of founders not only affects their own views on things and related behaviors, but also affects the overall strategic direction and action results of enterprises more generally [7][8][9].…”

Section: Controller Heterogeneity and Corporate Digital Transformationmentioning

confidence: 99%

“…The regression results are shown in columns ( 6) and (7) of Table 8, where the regression coefficients of professional financial investment controllers and digital transformation of enterprises are significantly positive, while the regression coefficients of non-professional financial investment controllers and digital transformation of enterprises are significantly negative. This suggests that professional financial investment controllers will be more conducive to the digitalisation of enterprises than non-professional financial investment controllers.…”

Section: Analysis Of the Heterogeneity Of Financial Investment Contro...mentioning

confidence: 99%

An analysis method of enterprise digital transformation effect based on investor data of public companies

2023

AJBM

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In the context of the trend of Digital transformation of enterprises, this paper empirically examines the differences in the impact of founders and strategic investment controllers on Digital transformation of enterprises, taking private listed companies from 2007 to 2020 as research samples. The research results indicate that the founding controller is beneficial for listed companies to actively promote the digital process of the enterprise; However, both financial and industrial investment controllers will inhibit the Digital transformation of enterprises, and the inhibitory effect of financial investment controllers is stronger. Digital finance and control rights further strengthen the above regression results. Individual controllers, vertical industry controllers and professional financial investment controllers are conducive to the Digital transformation of enterprises. The mechanism research shows that the company's innovation input and output, and information transparency play a mediating role between the controller heterogeneity and the enterprise's Digital transformation. The research results of this paper enrich the research perspective and content of the relationship between corporate controllers and Digital transformation, and have certain reference significance for enterprises to improve corporate governance and actively promote Digital transformation.

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“…There are banks that have applied data mining technologies such as Hadoop, Spark for analyzing customer data. Shweta Yadav et al analyzed the credit risk and the loan performance on data of the "Lending Club", which is one of the biggest marketplaces for online credit in the Hadoop ecosystem [9].…”

Section: Introductionmentioning

confidence: 99%

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Hung¹,

Dat²

2020

IJMLC

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Customer clustering, the division of customers into different groups, is a classical problem. It is especially important in banking as it serves multiple purposes in marketing, risk management, etc. Therefore, it has attracted the use of many modern machine learning models and techniques. But currently, most of them are only making use of "static" customer information. This paper proposes a new approach for customer clustering in banking based on the customers' balance history. Basic Dynamic Time Warping (DTW) distance and Soft-DTW (SDTW) distance, an advanced form, are used to measure the difference between customers. To which, the two most popular strategies in time series clustering strategies, partitional and hierarchical, are applied which. In additional, some statistical features are given to prove the effectiveness of the proposed method.

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Bank loan analysis using customer usage data: A big data approach using Hadoop

Cited by 12 publications

References 12 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

An analysis method of enterprise digital transformation effect based on investor data of public companies

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

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