scholarly journals Impact of Unusual Features in Credit Scoring Problem

2020 ◽  
Author(s):  
Luiz Felipe Vercosa ◽  
Rodrigo Lira ◽  
Rodrigo Monteiro ◽  
Kleber Silva ◽  
Jailson Magalhaes ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Demographic Data ◽  
Credit Scoring ◽  
Financial Data ◽  
Unusual Feature ◽  
The Real ◽  
Kolmogorov Smirnov ◽  
Small Improvement ◽  
Better Than

Standard features used for Credit Scoring includes mainly registration and financial data from customers. However, exploring new features is of great interest for financial companies, since slight improvements in the person score directly impact the company revenue. In this work, we categorize features from open credit scoring datasets and compare them with the features found in a real company dataset. The company dataset contains unusual feature groups such as historical, geolocation, web behavior, and demographic data. We performed bivariate tests using the Kolmogorov-Smirnov metric and features to assess the performance of the particular feature groups. We also generated a score of good payer by using AdaBoost, Multilayer Perceptron, and XGBoost algorithms. Then, we analyzed the results with different metrics and compared them with the real company results. Our main finding was that these features added a small improvement to current datasets. We also identified the most promising feature groups and noticed that the tuned XGBoost performed better than the company solution in three out of four deployed metrics.

A federated interpretable scorecard and its application in credit scoring

2021 ◽  
pp. 2142009
Author(s):  
Fanglan Zheng ◽  
Erihe ◽  
Kun Li ◽  
Jiang Tian ◽  
Xiaojia Xiang
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Credit Scoring ◽  
Training Session ◽  
Model Performance ◽  
Parameter Tuning ◽  
Kolmogorov Smirnov ◽  
Data Privacy Protection ◽  
Tuning Process ◽  
Positive Coefficients

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

scholarly journals A Real-Time Structure of Attitude Algorithm for High Dynamic Bodies

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Xingcheng Li ◽  
Shuangbiao Zhang
Keyword(s):  
Real Time ◽  
Time Structure ◽  
Flow Diagram ◽  
The Real ◽  
Time Problem ◽  
Conventional Structure ◽  
High Dynamic ◽  
Two Stages ◽  
Better Than ◽  
Attitude Matrix

To solve the real-time problem of attitude algorithm for high dynamic bodies, a real-time structure of attitude algorithm is developed by analyzing the conventional structure that has two stages, and a flow diagram of a real-time structure for a Matlab program is provided in detail. During the update of the attitude matrix, the real-time structure saves every element of attitude matrix in minor loop in real time and updates the next attitude matrix based on the previous matrix every subsample time. Thus, the real-time structure avoids lowering updating frequency, though the multisubsample algorithms are used. Simulation and analysis show that the real-time structure of attitude algorithm is better than the conventional structure due to short update time of attitude matrix and small drifting error, and it is more appropriate for high dynamic bodies.

Do innovative activities matter to Indian drug and pharmaceutical firms? An application to export performance

2017 ◽  
Vol 13 (4) ◽  
pp. 193-205
Author(s):  
Shilpi Tyagi ◽  
DK Nauriyal
Keyword(s):  
Pharmaceutical Industry ◽  
Rapid Growth ◽  
Export Performance ◽  
Innovative Activity ◽  
Financial Data ◽  
Pharmaceutical Companies ◽  
Firm Level ◽  
The Real ◽  
Pharmaceutical Firms ◽  
New Evidence

This paper analyzes the R&D and exports profile of Indian drug and pharmaceutical industry during the period 2000–2014. The present paper examines how R&D expenditure and patent impact export performance of the Indian drug and pharmaceutical firms. The study period from 2000 to 2014 has been characterized by a rapid growth in industry’s innovative activity, as part of the strategic shift, induced by the Patents (Amendment) Act, 2005. Using the real financial data for the top 91 publicly listed Indian domestic pharmaceutical companies, the study provides new evidence on firm-level export performance of the Indian drugs and pharmaceutical industry. Generalized Method of Movements estimator developed by Blundell and Bond is applied. The empirical findings of the study reveal that increased R&D intensity, higher patent count and firm’s size are important determinants of firm-level export performance.

Improved Tercom Based on Fading Factor

2011 ◽  
Vol 143-144 ◽  
pp. 770-774 ◽  
Author(s):  
Shou Lei Lu ◽  
Long Zhao ◽  
Chang Yun Zhang
Keyword(s):  
Correlation Function ◽  
Real Data ◽  
Angle Error ◽  
The Real ◽  
Velocity Error ◽  
Speed Error ◽  
Fading Factor ◽  
Yaw Angle ◽  
Better Than

In order to solve the problem of the traditional Tercom, which is sensitive to the speed error and yaw angle error, an improved Tercom approach using with fading factor is introduced. The basic idea of this approach is to estimate the navigation position by a novel correlation function. The correlation function is calculated by weighted historical measurements. Experiment results with the real data show that this approach performs better than the traditional Tercom with regard to overcoming velocity error and yaw angle error.

scholarly journals A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pranith Kumar Roy ◽  
Krishnendu Shaw
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Real Life ◽  
Financial Data ◽  
Loan Pricing ◽  
Scoring Model ◽  
Record Keeping ◽  
Return On Capital ◽  
Credit Scoring Model

AbstractSmall- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

scholarly journals Penerapan Strategi Pembelajaran Circuit Learning: Suatu Upaya dalam Meningkatkan Hasil Belajar Siswa

2019 ◽  
Vol 9 (2) ◽  
pp. 285
Author(s):  
Syahrial Syahrial
Keyword(s):  
Learning Outcomes ◽  
Learning Strategy ◽  
Alternative Hypothesis ◽  
Mathematics Learning ◽  
Control Group ◽  
Test Analysis ◽  
The Real ◽  
Randomized Control ◽  
Affective Domains ◽  
Better Than

This research is motivated by the low student mathematics learning outcomes. The influencing factors are inactive students and lack of communication between students and students. This study aims to determine the effect of the application of the circuit learning strategy to students' learning outcomes in the cognitive and effective domains. This type of research is pre-experimental and the research design used is randomized control group only design. Based on the final test of learning outcomes obtained an average of mathematics learning outcomes in the experimental class 79.3 and the average mathematics learning outcomes of the control class 70. The results of the t-test analysis obtained tcount = 3.89 and ttable = 1.667 at the real level of 0.05. It is concluded that tcount> ttable accepts an alternative hypothesis (H1) that is the mathematics learning outcomes of the experimental class students is better than the control class. Analysis of the data in the affective domain obtained zcount = 3.83 and ztable = 1.64 at the real level of 0.05 thus zcount> ztable, in other words Hi is accepted meaning that student learning activities in the experimental class are better than the control class. Based on data analysis in the cognitive and affective domains it can be concluded that there is an influence of the application of the circuit learning strategy to student mathematics learning outcomes.

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