Financial Analytics from Public Data

Burdick, Douglas; Evfimievski, Alexandre V.; Krishnamurthy, Rajasekar; Lewis, Neal; Popa, Lucian; Rickards, Scott; Williams, Peter

doi:10.1145/2630729.2630742

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Water savings derived from the use of vacuum toilets can augment the economic feasibility of source-separation alternatives, as the price of drinking water continues to rise. A water price of 2 €/m 3 was used for this research, and this price is based on the most recent literature. ,, The value of fertilizer (i.e., ammonium sulfate and struvite) was also included as income, , as wastewater byproducts should be accounted for as assets that can be sold or offered to the community to improve local agriculture which in turn helps reduce the manufacturing of fertilizers on a global scale. The energy required to produce fertilizers represents 50% of the total energy requirements of European agriculture, a figure that could be drastically reduced by extending the recovery of nutrients to all wastewater effluents.…”

Section: Resultsmentioning

confidence: 99%

The Economics of Wastewater Treatment Decentralization: A Techno-economic Evaluation

Garrido-Baserba

Vinardell

Molinos-Senante

et al. 2018

Environ. Sci. Technol.

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The existing wastewater treatment infrastructure has not adequately established an efficient and sustainable use of energy, water, and nutrients. A proposed scheme based on source separation and water-efficient use is compared to the current wastewater management paradigm (one largely based on activated sludge) using techno-economic terms. This paper explores the economic viability of adopting more sustainable management alternatives and expands the understanding of the economics of decentralization and source-separation. The feasibility of three different potential types of source-separation (with different levels of decentralization) are compared to the conventional centralized activated sludge process by using recognized economic assessment methodologies together with widely accepted modeling tools. The alternatives were evaluated for two common scenarios: new developments and retrofit due to the aging of existing infrastructures. The results prove that source-separated alternatives can be competitive options despite existing drawbacks (only when countable incomes are included), while the hybrid approach resulted in the least cost-effective solution. A detailed techno-economic evaluation of the costs of decentralization provides insight into the current constraints concerning the paradigm shift and the cost of existing technologic inertia.

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Section: Resultsmentioning

confidence: 99%

The Economics of Wastewater Treatment Decentralization: A Techno-economic Evaluation

Garrido-Baserba

Vinardell

Molinos-Senante

et al. 2018

Environ. Sci. Technol.

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“…The goal of financial analytics is to shape the strategy for business through reliable, factual insight rather than intuition. The chief financial officers traditionally relied on historical data and trends to forecast future performance [1]. However, they are changing their focus as they increasingly tap into technologies, such as advanced data analytics, machine learning and automation.…”

Section: Introductionmentioning

confidence: 99%

Emerging Approach for Detection of Financial Frauds Using Machine Learning

Bandyopadhyay¹,

Thakkar²,

Mukherjee³

et al. 2021

Preprint

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The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

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“…The aim of financial analytics is to provide better business strategies through more factual data that are reliable instead of relying on intuition. Traditionally, the chief officers followed historical data and trends and based on that future predictions were made [1]. It is needless to mention that companies are switching their focus from traditional data analysis into modern technologies like advanced data analytics coupled with machine learning and automation.…”

Section: Introductionmentioning

confidence: 99%

Emerging Approach for Detection of Financial Frauds Using Machine Learning

Mukherjee

Thakkar

Dutta

et al. 2021

AJRCoS

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The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

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Financial Analytics from Public Data

Cited by 8 publications

References 9 publications

The Economics of Wastewater Treatment Decentralization: A Techno-economic Evaluation

The Economics of Wastewater Treatment Decentralization: A Techno-economic Evaluation

Emerging Approach for Detection of Financial Frauds Using Machine Learning

Emerging Approach for Detection of Financial Frauds Using Machine Learning

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