Towards a data-driven technology roadmap for the bank of the future: Exploring big data analytics to support technology roadmapping

Maja, Mogabe Marcia; Letaba, Petrus

doi:10.1016/j.ssaho.2022.100270

Cited by 19 publications

(17 citation statements)

References 19 publications

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“…Since a bank usually has a huge amount of information about its customers that it can work with, the task has really great potential in practice. Recent studies [5,6] highlight that banks are looking for more ways to leverage product trends, market dynamics, customer behavior, and new technologies through rich data analytics.…”

Section: Issn 2664-9969mentioning

confidence: 99%

Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients

Bulhakova

Ulianovska

Kostenko

et al. 2022

TAPR

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The object of the research is modern online services and machine learning libraries for predicting the probability of the bank client's consent to the provision of the proposed services. One of the most problematic areas is the high unpredictability of the result in the field of banking marketing using the most common technique of introducing new services for clients – the so-called cold calling. Therefore, the question of assessing the probability and predicting the behavior of a potential client when promoting new banking services and services using cold calling is particularly relevant. In the course of the study, libraries of machine learning methods and data analysis of the Python programming language were used. A program was developed to build a model for predicting the behavior of bank customers using data processing methods using gradient boosting, regularization of gradient boosting, random forest algorithm and recurrent neural networks. Analogous models were built using cloud machine learning services Azure ML, BigML and the Auto-sklearn library. Data analysis and prediction models built using Python language libraries have a fairly high quality – an average of 94.5 %. Using the Azure ML cloud service, a predictive model with an accuracy of 88.6 % was built. The BigML machine learning service made it possible to build a model with an accuracy of 88.8 %. Machine learning methods from the Auto-sklearn library made it possible to obtain a model with a higher quality – 94.9 %. This is due to the fact that the proposed libraries of the Python programming language allow better customization of data processing methods and machine learning to obtain more accurate models than free cloud services that do not provide such capabilities. Thanks to this, it is possible to obtain a predictive model of the behavior of bank customers with a fairly high degree of accuracy. It is worth noting that in order to make a prediction (forecast), it is necessary to study the context of the task, process the data, build various machine learning algorithms, evaluate the quality of the models and choose the best of them.

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Section: Issn 2664-9969mentioning

confidence: 99%

Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients

Bulhakova

Ulianovska

Kostenko

et al. 2022

TAPR

View full text Add to dashboard Cite

show abstract

“…The resulting characteristics of BD require tools and techniques to harvest and analyze both unstructured and structured data that exceed the capabilities of traditional data processing systems. Big Data has characteristics that can analyze unstructured and structured data that can process quickly and traditional realtime data [31]. Fig.…”

Section: Big Data Analyticsmentioning

confidence: 99%

A Framework for Illegal Online Loan Risk Using WordCloud and Big Data Analytics

Mambang¹,

Marleny²,

Hidayat³

et al. 2022

Int. J. Adv. Sci. Eng. Inf. Technol.

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Information technology has provided many conveniences in community activities. Big data on the Internet benefits many things, especially risk information that occurs without being careful in conducting transactions and credit activities online. This study analyzes online credit risk using ten variables related to online credit risk. The data set used in this paper is sourced from the Internet by using keywords that have been determined using the Uniform Resource Locator (URL) from different websites. The research method used in this study is an experimental method by classifying word variables related to online credit risk through data collection, initial data processing with a word cloud generator, and data analysis with python programming, then evaluation and validation of results. Variables analyzed such high loan interest, small loan ceiling, personal data in the App, old approval, the collector is coming, administrative costs, not yet registered with the OJK, unofficial loan institutions, consumer data protection, and cost transparency. Data collection techniques by means of questionnaires were carried out to online loan money borrowers to explore more in-depth information. The results of the analysis that has been carried out with the python programming language using the pandas, matplotlib, and seaborn libraries produce the Small Loan Ceiling variable, which greatly influences the consumer data protection variable with a value of 0.99. An in-depth analysis of these variables found that credit with a ceiling is ineffective.

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“…In many industries, people and organizations seek efficient and effective ways of processing and analyzing large volumes of data to support their decision-making efforts [ 4 , 5 , 6 , 7 ]. Traditional decision-making methods, which rely on personal knowledge, experience, and wisdom, are limited in dealing with big data effectively and are prone to bias and errors [ 8 , 9 ]. DDDM uses models and algorithms to process and analyze data sources for reliable decision support [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…This approach has been widely applied in various industries, including medical diagnosis [ 12 ], financial risk prediction [ 13 , 14 ], public affairs governance [ 15 ], landslide susceptibility prediction [ 16 , 17 ], autonomous driving [ 18 ], and the safe operation of wastewater treatment processes [ 19 , 20 ], among others [ 21 , 22 , 23 ]. DDDM helps reduce the limitations of traditional decision-making methods, resulting in more accurate predictions and better decision support [ 1 , 6 , 9 , 11 ]. This capability of DDDM can result in the improved efficiency and reduced risks for various industries.…”

Section: Introductionmentioning

confidence: 99%

Towards Data-Driven Decision-Making in the Korean Film Industry: An XAI Model for Box Office Analysis Using Dimension Reduction, Clustering, and Classification

Leem

et al. 2023

Entropy

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The Korean film market has been rapidly growing, and the importance of explainable artificial intelligence (XAI) in the film industry is also increasing. In this highly competitive market, where producing a movie incurs substantial costs, it is crucial for film industry professionals to make informed decisions. To assist these professionals, we propose DRECE (short for Dimension REduction, Clustering, and classification for Explainable artificial intelligence), an XAI-powered box office classification and trend analysis model that provides valuable insights and data-driven decision-making opportunities for the Korean film industry. The DRECE framework starts with transforming multi-dimensional data into two dimensions through dimensionality reduction techniques, grouping similar data points through K-means clustering, and classifying movie clusters through machine-learning models. The XAI techniques used in the model make the decision-making process transparent, providing valuable insights for film industry professionals to improve the box office performance and maximize profits. With DRECE, the Korean film market can be understood in new and exciting ways, and decision-makers can make informed decisions to achieve success.

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Towards a data-driven technology roadmap for the bank of the future: Exploring big data analytics to support technology roadmapping

Cited by 19 publications

References 19 publications

Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients

Consideration of the possibilities of applying machine learning methods for data analysis when promoting services to bank's clients

A Framework for Illegal Online Loan Risk Using WordCloud and Big Data Analytics

Towards Data-Driven Decision-Making in the Korean Film Industry: An XAI Model for Box Office Analysis Using Dimension Reduction, Clustering, and Classification

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