Big Data Business Intelligence in Bank Risk Analysis

Rahman, Nayem; Iverson, Shane

doi:10.4018/ijbir.2015070104

Cited by 19 publications

(9 citation statements)

References 48 publications

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“…Hadoop can be used for a wide variety of purposes, such as real-time streaming and processing, log processing, developing recommendation systems, building a data warehousing environment, market campaign analysis, and fraud detection [91]. Consolidated data into a single platform provide improved data mining and business intelligence capabilities [100]. Hence Hypothesis H9: Hadoop's FL to consolidate data from various sources to a single place (HDFS) will have a positive effect on the PU of Hadoop.…”

Section: Hypothesis H4: Pe Is Positively Related To the Pu Of Hadoopmentioning

confidence: 99%

Exploring the Factors Influencing Big Data Technology Acceptance

Rahman

Daim

Başoğlu

2023

IEEE Trans. Eng. Manage.

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Big Data has received great attention in academic literature and industry papers. Most of the experiments and studies focused on publishing results of big data technologies development, machine learning algorithms, and data analytics. To the best of our knowledge, there is not yet any comprehensive empirical study in the academic literature on big data technology acceptance. The statistical results of this model provide a compelling explanation of the relationships among the antecedent variables and the dependent variables. The analysis of the structural model reveals that the hypothesis tests are significant for 8 out of 12 path relationships.

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Section: Hypothesis H4: Pe Is Positively Related To the Pu Of Hadoopmentioning

confidence: 99%

Exploring the Factors Influencing Big Data Technology Acceptance

Rahman

Daim

Başoğlu

2023

IEEE Trans. Eng. Manage.

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“…It is to be noted that huge, stored data requires a more sophisticated big data mining tools installed. The standard big data software can only analyze 5% of the entire bank's dataset at a time (Rahman & Iverson, 2015). Therefore, it is crucial for, in this case, banks to invest in an advanced data mining software to achieve an accurate analysis of bank's data and potential risks.…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data Applications the Banking Sector: A Bibliometric Analysis Approach

et al. 2021

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This study aims to review the existing literature on big data applications in banking using a bibliometric analysis approach. This approach describes citation rates, research outputs, and their implementations, along with current streams in the field and future research agenda. The articles were selected from 2012 to 2020 and sorted by the citation rate in results and analysis. We have discovered 60 papers related to big data in banking, although the applications of big data in the banking sector are growing rapidly, the number of research output in this field is limited. Several themes are extracted from the studies that are reviewed, analyzed, and presented in this report. This review covered the themes that include investment, profit, competition, credit risk analysis, banking crime, and fintech. This report also signifies the importance, use of big data, and its function in the banking and financial sector. This study has also discussed the future research scope in the banking industry’s big data analytics.

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“…These data consist of internal transactional data and external business related data, which get generated in many places including the social media sites. These data are stored in traditional data warehouses as well as Hadoop-based big data platforms [24,7]. Due to enormous volume of data knowledge workers need special tools, technologies and data mining algorithms or techniques to process, perform predictive analytics, and analyze the data.…”

Section: Prior Workmentioning

confidence: 99%

“…Due to enormous volume of data knowledge workers need special tools, technologies and data mining algorithms or techniques to process, perform predictive analytics, and analyze the data. It requires processing of data using extract-transform-load tools, business intelligence tools [25,23] and data mining tools [15,24] in order to make informed business decisions.…”

Section: Prior Workmentioning

confidence: 99%

A Decision Model for Data Mining Techniques

Dabab

Freiling

Rahman

et al. 2018

2018 Portland International Conference on Management of Engineering and Technology (PICMET)

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Data mining is the process of extracting useful information from very large data sources. Data mining techniques have proven to be very useful in many domains. However, there is no single algorithm or technique that works best across all types of datasets and problems, and it remains "an art" to decide what data mining technique to use for a specific situation. This paper surveys several data mining techniques that can be applied to different business problems, and presents a decision model in the form of a series of 15-20 questions that help identify the best approach or approaches to a specific problem at hand. For some sets of answers, a small number of techniques are dominant. The decision model is based on a review of the current literature, as well as expert experience. The fraud detection problem is adopted as a case study and applied the data mining techniques to draw the insights. We also discuss the applicability of specific techniques to common business in finance, marketing, and business operations.

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Big Data Business Intelligence in Bank Risk Analysis

Cited by 19 publications

References 48 publications

Exploring the Factors Influencing Big Data Technology Acceptance

Exploring the Factors Influencing Big Data Technology Acceptance

Big Data Applications the Banking Sector: A Bibliometric Analysis Approach

A Decision Model for Data Mining Techniques

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