Mitigating Financial Fraud Using Data Science - “A Case Study on Credit Card Frauds”

AbstractA challenge in conservation is the gap between knowledge generated by researchers and the information being used to inform conservation practice. This gap, widely known as the research-implementation gap, can limit the effectiveness of conservation practice. One way to address this is to design conservation tools that are easy for practitioners to use. Here, we implement data science methods to develop a tool to aid in conservation of pollinators in British Columbia. Specifically, in collaboration with Pollinator Partnership Canada, we jointly develop an interactive web app, the goal of which is two-fold: (i) to allow end users to easily find and interact with the data collected by researchers on pollinators in British Columbia (prior to development of this app, data were buried in supplements from individual research publications) and (ii) employ up to date statistical tools in order to analyse phenological coverage of a set of plants. Previously, these tools required high programming competency in order to access. Our app provides an example of one way that we can make the products of academic research more accessible to conservation practitioners. We also provide the source code to allow other developers to develop similar apps suitable for their data.

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Critical Data Studies and Data Science in Higher Education

Seminar.net ◽

10.7577/seminar.4397 ◽

2021 ◽

Vol 17 (2) ◽

Author(s):

Dan Verständig

Keyword(s):

Higher Education ◽

Data Science ◽

Media Theory ◽

Digital Data ◽

Science Methods ◽

Data Literacy ◽

Critical Data ◽

Data Practices ◽

Using Data

This paper discusses an explorative approach on strengthening critical data literacy using data science methods and a theoretical framing intersecting educational science and media theory. The goal is to path a way from data-driven to data-discursive perspectives of data and datafication in higher education. Therefore, the paper focuses on a case study, a higher education course project in 2019 and 2020 on education and data science, based on problem-based learning. The paper closes with a discussion on the challenges on strengthening data literacy in higher education, offering insights into data practices and the pitfalls of working with and reflecting on digital data.

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Using Data Science To Drive Water Utility Decisions: A Case Study In A Medium-Sized Utility

Proceedings of the Water Environment Federation ◽

10.2175/193864718823773210 ◽

2018 ◽

Vol 2018 (1) ◽

pp. 724-732

Author(s):

Janani Mohanakrishnan ◽

Christine Boyle ◽

James G Poff

Keyword(s):

Data Science ◽

Water Utility ◽

Using Data

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Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

Global Knowledge Memory and Communication ◽

10.1108/gkmc-01-2021-0006 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Haider Ilyas ◽

Ahmed Anwar ◽

Ussama Yaqub ◽

Zamil Alzamil ◽

Deniz Appelbaum

Keyword(s):

Data Science ◽

Supervised Machine Learning ◽

Content Type ◽

The Public ◽

Public Sentiment ◽

The People ◽

The Usa ◽

Using Data ◽

The Uk

Purpose This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

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People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition

Sustainability ◽

10.3390/su11133520 ◽

2019 ◽

Vol 11 (13) ◽

pp. 3520 ◽

Cited By ~ 3

Author(s):

Sabina-Cristiana Necula ◽

Cătălin Strîmbei

Keyword(s):

Semantic Web ◽

Human Resource ◽

Data Science ◽

Support Vector ◽

K Nearest Neighbors ◽

Semantic Web Technologies ◽

Tree Algorithms ◽

Using Data ◽

Talent Acquisition

The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.

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Consumer Credit Risk Analysis using Data Mining Clustering and Business Intelligence Solutions

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.e2122.039520 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1349-1358

Keyword(s):

Credit Card ◽

Credit Cards ◽

Fraud Detection ◽

Machine Learning Algorithms ◽

Electronic Transactions ◽

Good Classification ◽

Credit Risk Analysis ◽

Using Data ◽

Consumer Credit Risk

In the recent years, the scale of online transaction has increased considerably. Subsequently, this has also increased the number of fraud cases, causing billions of dollars losses each year worldwide. Therefore, it has become mandatory to implement mechanisms that are able to assist in fraud detection. In this work, the use of Ensemble Genetic Algorithm is proposed to identify frauds in electronic transactions, more specifically in online credit card operations. A case study, using the dataset containing transactions made by credit cards in September 2013 by European cardholders, is used. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The presented algorithm achieves good performance in fraud detection as compared to the other machine learning algorithms. The results show that the proposed algorithm achieved good classification effectiveness in all tested instances.

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Using Data Science and Artificial Intelligence to Enable Technology-Driven Offshore Opportunities

Volume 1: Offshore Technology ◽

10.1115/omae2020-19195 ◽

2020 ◽

Author(s):

Subrat Nanda ◽

Meenakshi A. Bhatia

Keyword(s):

Asset Management ◽

Data Science ◽

Operational Experience ◽

Combine Data ◽

Optimal Resource Allocation ◽

Equipment Reliability ◽

Recent Developments ◽

Optimal Resource ◽

Using Data

Abstract Recent developments in data science are enabling new opportunities for marine and offshore operators to adopt a more effective asset management strategy. The crux of this strategy is to combine data analytics with maintenance records and operational experience to reduce unplanned downtime. This case study focuses on the assimilation and utilization of diverse and mostly unstructured data, which up until now was largely untapped in the marine and offshore industries. The information extracted from such sources is used to identify key trends in equipment reliability and to improve the understanding of assets’ conditions. Such insights are particularly useful for marine and offshore operators in making critical decisions relating to machinery: optimal resource allocation; proactive planning for planned maintenance events and maximizing overall asset availability. From a Classification Society’s perspective such as American Bureau of Shipping (ABS), this allows operators and/or owners to derive Class-based benefits like Condition-Based Maintenance (CBM).

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