scholarly journals Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1820
Author(s):  
Ekaterina V. Orlova
Keyword(s):  
Interest Rates ◽  
Additional Data ◽  
Risk Level ◽  
Gradient Boosting ◽  
Cluster Number ◽  
New Approach ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
The Stability ◽  
Digital Footprints

This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k-means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

Predictive Credit Risk Analytics Using Borrowers' Digital Footprint and Methods of Statistical Machine Learning

2021 ◽  
Vol 12 (7) ◽  
pp. 358-372
Author(s):  
E. V. Orlova ◽  
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Risk Profile ◽  
Classification Model ◽  
Gradient Boosting ◽  
Suggested Approach ◽  
Digital Footprint ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
Boosting Method

The article considers the problem of reducing the banks credit risks associated with the insolvency of borrowers — individuals using financial, socio-economic factors and additional data about borrowers digital footprint. A critical analysis of existing approaches, methods and models in this area has been carried out and a number of significant shortcomings identified that limit their application. There is no comprehensive approach to identifying a borrowers creditworthiness based on information, including data from social networks and search engines. The new methodological approach for assessing the borrowers risk profile based on the phased processing of quantitative and qualitative data and modeling using methods of statistical analysis and machine learning is proposed. Machine learning methods are supposed to solve clustering and classification problems. They allow to automatically determine the data structure and make decisions through flexible and local training on the data. The method of hierarchical clustering and the k-means method are used to identify similar social, anthropometric and financial indicators, as well as indicators characterizing the digital footprint of borrowers, and to determine the borrowers risk profile over group. The obtained homogeneous groups of borrowers with a unique risk profile are further used for detailed data analysis in the predictive classification model. The classification model is based on the stochastic gradient boosting method to predict the risk profile of a potencial borrower. The suggested approach for individuals creditworthiness assessing will reduce the banks credit risks, increase its stability and profitability. The implementation results are of practical importance. Comparative analysis of the effectiveness of the existing and the proposed methodology for assessing credit risk showed that the new methodology provides predictive ana­lytics of heterogeneous information about a potential borrower and the accuracy of analytics is higher. The proposed techniques are the core for the decision support system for justification of individuals credit conditions, minimizing the aggregate credit risks.

scholarly journals Pear Flower Cluster Quantification Using RGB Drone Imagery

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 407 ◽  
Author(s):  
Yasmin Vanbrabant ◽  
Stephanie Delalieux ◽  
Laurent Tits ◽  
Klaas Pauly ◽  
Joke Vandermaesen ◽  
...  
Keyword(s):  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Production ◽  
Gradient Boosting ◽  
Cluster Number ◽  
Stochastic Gradient Boosting ◽  
Density Based Clustering ◽  
Object Based ◽  
Dense Point ◽  
Top View

High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red–Green–Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%.

Host-Guest Interactions on Electrode Surfaces for Immobilization of Molecular Catalysts

2020 ◽  
Author(s):  
Laurent Sévery ◽  
Jacek Szczerbiński ◽  
Mert Taskin ◽  
Isik Tuncay ◽  
Fernanda Brandalise Nunes ◽  
...  
Keyword(s):  
Aqueous Media ◽  
Heterogeneous Systems ◽  
Catalytic Systems ◽  
New Approach ◽  
Molecular Systems ◽  
Electrode Surfaces ◽  
Catalytic Surfaces ◽  
Oxide Electrodes ◽  
The Stability ◽  
Molecular Catalysts

The strategy of anchoring molecular catalysts on electrode surfaces combines the high selectivity and activity of molecular systems with the practicality of heterogeneous systems. The stability of molecular catalysts is, however, far less than that of traditional heterogeneous electrocatalysts, and therefore a method to easily replace anchored molecular catalysts that have degraded could make such electrosynthetic systems more attractive. Here, we apply a non-covalent “click” chemistry approach to reversibly bind molecular electrocatalysts to electrode surfaces via host-guest complexation with surface-anchored cyclodextrins. The host-guest interaction is remarkably strong and allows the flow of electrons between the electrode and the guest catalyst. Electrosynthesis in both organic and aqueous media was demonstrated on metal oxide electrodes, with stability on the order of hours. The catalytic surfaces can be recycled by controlled release of the guest from the host cavities and readsorption of fresh guest. This strategy represents a new approach to practical molecular-based catalytic systems.

scholarly journals Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Louis Ehwerhemuepha ◽  
Theodore Heyming ◽  
Rachel Marano ◽  
Mary Jane Piroutek ◽  
Antonio C. Arrieta ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severe Sepsis ◽  
Early Warning ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Warning System ◽  
Receiver Operator Characteristic Curve ◽  
Warning Signs ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

AbstractThis study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

A new approach to the stability analysis of thawing slopes

1980 ◽  
Vol 17 (4) ◽  
pp. 607-612 ◽  
Author(s):  
Luis E. Vallejo
Keyword(s):  
Stability Analysis ◽  
Water Content ◽  
Pore Water ◽  
Active Layer ◽  
Necessary Conditions ◽  
Mass Movements ◽  
New Approach ◽  
Pore Water Pressures ◽  
The Stability ◽  
Excess Pore

A new approach to the stability analysis of thawing slopes at shallow depths, taking into consideration their structure (this being a mixture of hard crumbs of soil and a fluid matrix), is presented. The new approach explains shallow mass movements such as skin flows and tongues of bimodal flows, which usually take place on very low slope inclinations independently of excess pore water pressures or increased water content in the active layer, which are necessary conditions in the methods available to date to explain these movements.

scholarly journals Le système monétaire européen : Le point de vue européen

2005 ◽  
Vol 12 (3) ◽  
pp. 533-547 ◽  
Author(s):  
Michel Lelart
Keyword(s):  
Interest Rates ◽  
International Monetary System ◽  
European Monetary System ◽  
The United States ◽  
European Monetary ◽  
International Capital Markets ◽  
Monetary System ◽  
American Interest ◽  
International Environment ◽  
The Stability

The evolution of the international monetary System prompted the nine members of the E.E.C. to establish a European Monetary System. The new statutes of the I.M.F. have in fact legalized the practice of flexible exchange rates and sanctioned the dollar's inconvertibility while eliminating the role of gold. Further, the increasing importance of the international capital markets fosters the unlimited expansion of international liquidities. it is in response to this context then that Europe seeks to create a zone of stability and to manage its own international tender in accordance with rules that it has set for itself. The author draws a positive conclusion as the System has operated without major problems so far. Nevertheless, difficulties remain: the international environment has not improved given the abrupt strengthening of the dollar and the increase in American interest rates. In addition, progress with regard to cooperation among the Nine remains slow and political change in France makes any prognosis respecting the future of the European Monetary System difficult. It was anticipated that the System would be Consolidated rapidly. It would in that event contribute more effectively to the stability of the international monetary System. It could, on the other hand, sharpen competition between Europe and the United States, between the Ecu and S.D.Rs. and between the European Monetary Fund and the International Monetary Fund.

Enhanced prediction of vulnerable Web components using Stochastic Gradient Boosting Trees

2019 ◽  
Vol 15 (2) ◽  
pp. 201-214 ◽  
Author(s):  
Mahmoud Elish
Keyword(s):  
Web Applications ◽  
Prediction Models ◽  
Stochastic Gradient ◽  
Gradient Boosting ◽  
Data Sets ◽  
Content Type ◽  
Stochastic Gradient Boosting ◽  
Security Inspection ◽  
Novel Model ◽  
Efficient Software

Purpose Effective and efficient software security inspection is crucial as the existence of vulnerabilities represents severe risks to software users. The purpose of this paper is to empirically evaluate the potential application of Stochastic Gradient Boosting Trees (SGBT) as a novel model for enhanced prediction of vulnerable Web components compared to common, popular and recent machine learning models. Design/methodology/approach An empirical study was conducted where the SGBT and 16 other prediction models have been trained, optimized and cross validated using vulnerability data sets from multiple versions of two open-source Web applications written in PHP. The prediction performance of these models have been evaluated and compared based on accuracy, precision, recall and F-measure. Findings The results indicate that the SGBT models offer improved prediction over the other 16 models and thus are more effective and reliable in predicting vulnerable Web components. Originality/value This paper proposed a novel application of SGBT for enhanced prediction of vulnerable Web components and showed its effectiveness.

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