Using machine learning to evaluate the influence of FinTech patents: The case of Taiwan’s financial industry

Chen, Ting‐Hsuan; Chang, Rong-Cih

doi:10.1016/j.cam.2020.113215

Cited by 35 publications

(15 citation statements)

References 11 publications

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“…While the increased IP activities of financial organizations have received some research attention [6], [9], [13], as have issues relating to the content and categorization of FinTech patents [3], [5], less attention has been given to the quality of patents in the finance sector in general, and specifically the FinTech industry. There are significant reasons, however, why patent quality could be an important aspect of IP strategies in the area of FinTech.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Financial Technology Patents Through the Development of a Patent Quality Index for Comparing Jurisdictions, Technical Domains, and Leading Organizations

Dehghani

Karavidas

Panourgias

et al. 2024

IEEE Trans. Eng. Manage.

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This article examines the issue of patent quality in the Financial Technology (FinTech) field and proposes a way of assessing patent quality through the development of a patent quality index based on key indicators proposed in the literature. The index uses a sample of 16 387 patents in the FinTech field registered over 20 years to assess the average quality of patents. To illustrate the utility of the index, the 1) top geographic jurisdictions, 2) top technical domains, and 3) leading organizations were analyzed to map out patterns of intellectual property registration and protection. This article provides significant insights on leading patent jurisdictions, illustrating the growing impact on FinTech of jurisdictions, such as the Republic of Korea, and the focus of patents within the USA in particular domains, such as payment protocols, e-commerce, and identification mechanisms. This article contributes to both theory and practice through the development and validation of a novel patent quality index, which has significant utility to multiple stakeholders and advances knowledge on assessing patent quality. Furthermore, by surfacing a positive association between the quality of an organization's FinTech patents and earnings, the article illustrates the value to organizations in developing high-quality patents in this field.

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Section: Introductionmentioning

confidence: 99%

Assessing the Quality of Financial Technology Patents Through the Development of a Patent Quality Index for Comparing Jurisdictions, Technical Domains, and Leading Organizations

Dehghani

Karavidas

Panourgias

et al. 2024

IEEE Trans. Eng. Manage.

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“…As one of the most important sources of government revenue, tax currently plays a vital role in the economy of any country [3]. By using various tax policies, governments can use tax tools and adjust their various economic policies to achieve their most important goals such as social justice, proper distribution of income, and elimination of the class gap between di erent classes of society, stabilization of prices, reduction of unemployment, economic prosperity, and increase of investment [4,5].…”

Section: Introductionmentioning

confidence: 99%

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Mojahedi

Sangar

Màsdàrí

2022

Mathematical Problems in Engineering

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This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively.

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“…Several applications of machine learning algorithms have been made for financial business [8][9][10], and for the automatic risk assessment in the financial environment [11,12], with good results. However, despite the efforts made by researchers, there is no one best machine learning algorithm for all classification problems, as stated in the "no free lunch" theorems [13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

Camacho-Urriolagoitia

López-Yáñez

Villuendas-Rey

et al. 2021

Applied Sciences

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The presence of machine learning, data mining and related disciplines is increasingly evident in everyday environments. The support for the applications of learning techniques in topics related to economic risk assessment, among other financial topics of interest, is relevant for us as human beings. The content of this paper consists of a proposal of a new supervised learning algorithm and its application in real world datasets related to finance, called D1-NN (Dynamic 1-Nearest Neighbor). The D1-NN performance is competitive against the main state of the art algorithms in solving finance-related problems. The effectiveness of the new D1-NN classifier was compared against five supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, classifier ensembles, and neural networks), with superior results overall.

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Using machine learning to evaluate the influence of FinTech patents: The case of Taiwan’s financial industry

Cited by 35 publications

References 11 publications

Assessing the Quality of Financial Technology Patents Through the Development of a Patent Quality Index for Comparing Jurisdictions, Technical Domains, and Leading Organizations

Assessing the Quality of Financial Technology Patents Through the Development of a Patent Quality Index for Comparing Jurisdictions, Technical Domains, and Leading Organizations

Towards Tax Evasion Detection Using Improved Particle Swarm Optimization Algorithm

Dynamic Nearest Neighbor: An Improved Machine Learning Classifier and Its Application in Finances

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