Credit Decision Support Based on Real Set of Cash Loans Using Integrated Machine Learning Algorithms

Abstract

One of the important research problems in the context of financial institutions is the assessment of credit risk and the decision to whether grant or refuse a loan. Recently, machine learning based methods are increasingly employed to solve such problems. However, the selection of appropriate feature selection technique, sampling mechanism, and/or classifiers for credit decision support is very challenging, and can affect the quality of the loan recommendations. To address this challenging task, this article examines the effectiveness of various data science techniques in issue of credit decision support. In particular, processing pipeline was designed, which consists of methods for data resampling, feature discretization, feature selection, and binary classification. We suggest building appropriate decision models leveraging pertinent methods for binary classification, feature selection, as well as data resampling and feature discretization. The selected models’ feasibility analysis was performed through rigorous experiments on real data describing the client’s ability for loan repayment. During experiments, we analyzed the impact of feature selection on the results of binary classification, and the impact of data resampling with feature discretization on the results of feature selection and binary classification. After experimental evaluation, we found that correlation-based feature selection technique and random forest classifier yield the superior performance in solving underlying problem.

Integrated Learning Model-Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients

“…Neurosurgical critically ill patients are mostly associated with stress hyperglycemia and negative nitrogen balance, and the body is in a hypermetabolic and hypercatabolic state after injury; early enteral nutrition support can improve the nutritional status of patients and enhance their immunity [ 1 ] but is prone to complications such as feeding intolerance and aspiration pneumonia [ 2 ]. To achieve better care outcomes, evidence-based care is derived from evidence-based medical theory by integrating the best available evidence-based medical evidence, the patient's actual situation, and the caregiver's skills and clinical experience [ 3 , 4 ].…”

Framework for multi-criteria assessment of classification models for the purposes of credit scoring

“…Taking into account the number of methodological approaches considered in each group, this gave 315 different scenarios and the same number of classification models supporting credit decisions, which we evaluated. The research description and results were published in the article [ 9 ]. The dataset on which the experiment was conducted describes anonymized data about loan repayment and borrowers.…”

“…It was divided in proportion 70/30% into training set (64,230 records) and testing set (27,529 records). Both datasets are attached to article [ 9 ] (stored in the Journal repository). These data were also used to build on the IPA of various classification models that are the subject of research in this study.…”

“…BD Poland experts submitted the values of the above-mentioned indicators for the study of 10 different classification models, which were created on the IPA platform using anonymized data on loan repayment and borrowers (see: collections from stage 1, attached to the article [ 9 ]). Based on their own experience and analysis of the proposed indicators, the experts evaluated and ranked the models in order to compare them with the ranking results obtained in the MCDA-based framework.…”

“…The main dilemma in the case of classification tasks is the selection of an appropriate algorithm adapter to the problem under consideration [ 9 ]. The formalized description of the algorithm selection problem proposed in 1976 by Rice [ 10 ] takes the form of abstract 5-element models composed of performance measures and the problem space, algorithms, features and criteria.…”