A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

A New Paradigm in Islamic Housing: Non-Bank Islamic Mortgage

An increasing demand for a sharia-compliant housing has resulted in a new initiative where the mortgage can presently be directly handled by the developer without involving the bank. This is called non-bank Islamic mortgage. This paper is aimed at portraying the consumer’s profile of non-bank Islamic mortgage and the issues of this practice. We disseminated questionnaires to the respondents in several cities in West Java who bought the house-using non-bank Islamic mortgage scheme. Subsequently, we synthesized the answers regarding their profiles and issues of the scheme for the betterment in the future. One of the issues of this scheme is the higher fraud because there is no rigorous credit scoring as conducted by the bank. The research of non-bank Islamic mortgage is still scant so this paper is expected to shed the light by contributing to the literature of Islamic home financing.DOI: 10.15408/aiq.v10i2.727

EXAMINING BANKS’ FAILURE IN THE MENA REGION

This paper investigates whether or not banks in the MENA region are susceptible to failures. Two z score models are investigated in predicting the health of ninety banks across ten countries. Using discriminant and regression analysis, one can determine which ratios are statistically significant in predicting the health of the selected banks and which zone they belong to safe, grey, or distressed zone. The study spans the years 2006 to 2016. The goal of this study is to compare two z scores to assess if banks within MENA are subject to failure. According to the findings of this study, the Z score developed by El Ansary may be a better way for emerging economies to measure the indicators that rigger banks\u27 risk level

Effectiveness of machine learning algorithms as a tool to meat traceability system. A case study to classify Spanish Mediterranean lamb carcasses

Establishing the traceability of meat products has been a major focus of food science in recent decades. In this context, recent advances in food nutritional biomarker identification and improvements in statistical technology have allowed for more accurate identification and classification of food products. Moreover, artificial intelligence has now provided a new opportunity for optimizing existing methods to identify animal products. This study presents a comparative analysis of the effectiveness of different machine learning algorithms based on raw data from analyses of organoleptic, sensory and nutritional meat traits to differentiate categories of commercial lamb from an indigenous Spanish breed (Mallorquina breed) obtained from the following production systems: suckling lambs; light lambs from grazing; and light lambs from grazing supplemented with grain. Six machine learning algorithms were evaluated: Artificial Neural Network (ANN), Decision Tree, K-Nearest Neighbours (KNN), Naive Bayes, Multinomial Logistic Regression, and Support Vector Machine (SVM). For each algorithm, we tested three datasets, namely organoleptic traits and sensorial traits (CIELAB colour, water holding capacity, Warner-Bratzler shear force, volatile compounds and trained tasters), and nutritional traits (proximate composition and fatty acid profile). We also tested a combination of all three datasets. All the data were combined into a dataset with 144 variables resulting from the meat characterization, which included 11,232 event records. The ANN algorithm stood out for its high score with each of the three datasets used. In fact, we obtained an overall accuracy of 0.88, 0.83, and 0.88 for the organoleptic-sensory, nutritional, and combined datasets, respectively. The effectiveness of using the SVM algorithm to assign categories of lambs according to its production system performed better with nutritional traits and the full characterization, with performances equal to those obtained with ANN. The KNN algorithm showed the worst performance, with overall accuracies of 0.54 or lower for each of the datasets used. The results of this study demonstrate that machine learning is a useful tool for classifying commercial lamb carcasses. In fact, the ANN and SVM algorithms could be proposed as tools for differentiating categories of lamb production based on the organoleptic, sensory and nutritional characteristics of Mediterranean light lambs' meat. However, in order to improve the traceability methods of lamb meat production systems as a guarantee for consumers and to improve the learning processes used by these algorithms, more studies along these lines with other lamb breeds are required.This research has been financed by the Institute for Agricultural and Fisheries Research and Training (IRFAP) of the Government of the Balearic Islands (PRJ201502671-0781), the Spanish National Institute of Agricultural and Food Research and Technology and the European Social Fund (FPI2014-00013). Particular gratefulness to PhD Oliva Polvillo Polo (CITIUS, University of Seville’s Centre for Research) for contributing her knowledge in chromatography analysisinfo:eu-repo/semantics/publishedVersio

Determinants of default in P2P lending

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans'' data collected from Lending Club (N = 24, 449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower''s debt level

Credit scoring: Does XGboost outperform logistic regression?A test on Italian SMEs

The old-fashioned logistic regression is still the most used method for credit scoring. Recent developments have evolved new instruments coming from the machine learning approach, including random forests. In this paper, we tested the efficiency of logistic regression and XGBoost methods for default forecasting on a sample of 35,535 cases from 7 different business sectors of Italian SMEs, on a set of 28 banking variables and 55 balance sheet ratios for verifying which approach is better supporting the lending decisions. With this aim, we developed an efficiency index for measuring each model's capability to correctly select good borrowers, balancing the different effects of refusing the loan to a good customer and lending to a defaulter. Also, we computed the balancing spread to quantify the different models' efficiency in terms of credit costs for the borrower firms. Results show that different sectors report different results. However, generally speaking, the two methods report similar capabilities, while the cutoff setting can make a substantial difference in the actual use of those models for lending decisions

A Hybrid Machine Learning Approach for Credit Scoring Using PCA and Logistic Regression

Credit scoring is one mechanism used by lenders to evaluate risk before extending credit to credit applicants. The method helps distinguish credit worthiness of good credit applicants from the bad credit applicants.  Credit scoring involves a set of decision models and with their underlying techniques helps aid lenders in issuing of consumer credit. Logistic regression (LR) is an adjustment of linear regression with flexibility on its preposition of data and is also able to handle qualitative indicators. The major shortcoming of Logistic regression model is the inability to deal with cooperative (over fitting) effect of the variables. PCA is a feature extraction model that is used to filter out irrelevant un-needed features and hence, it lowers model training time and costs and also increases model performance. This study evaluates the shortcomings of simple models and proposes to develop an efficient and robust machine learning technique combining Logistic and PCA models to evaluate firms in the deposit taking SACCO sector. To achieve this, experimental methodology is adopted.  The proposed hybrid model will be two staged. First stage will be to transform the original variables to get new uncorrelated variables. This will be done using Principal Component Analysis (PCA). Stage two is the use of LR on the principal component values to compute the credit scores. Inferences and conclusions were made based on the analysis of the collected data using Matlab.

Forecasting Financial Distress With Machine Learning – A Review

Purpose – Evaluate the various academic researches with multiple views on credit risk and artificial intelligence (AI) and their evolution.Theoretical framework – The study is divided as follows: Section 1 introduces the article. Section 2 deals with credit risk and its relationship with computational models and techniques. Section 3 presents the methodology. Section 4 addresses a discussion of the results and challenges on the topic. Finally, section 5 presents the conclusions.Design/methodology/approach – A systematic review of the literature was carried out without defining the time period and using the Web of Science and Scopus database.Findings – The application of computational technology in the scope of credit risk analysis has drawn attention in a unique way. It was found that the demand for identification and introduction of new variables, classifiers and more assertive methods is constant. The effort to improve the interpretation of data and models is intense.Research, Practical & Social implications – It contributes to the verification of the theory, providing information in relation to the most used methods and techniques, it brings a wide analysis to deepen the knowledge of the factors and variables on the theme. It categorizes the lines of research and provides a summary of the literature, which serves as a reference, in addition to suggesting future research.Originality/value – Research in the area of Artificial Intelligence and Machine Learning is recent and requires attention and investigation, thus, this study contributes to the opening of new views in order to deepen the work on this topic

Business Intelligence in Banking: a Literature Analysis from 2002 to 2013 using Text Mining and Latent Dirichlet Allocation

Abstract This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. * Corresponding author (S. Moro). Email addresses: [email protected] (Sérgio Miguel Carneiro Moro), [email protected] (Paulo Alexandre Ribeiro Cortez), [email protected] (Paulo Miguel Rasquinho Ferreira Rita) Preprint submitted to Expert Systems With Applications September 1, 2014 Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research

Precisión de modelos de predictibilidad de quiebra aplicados al sector transporte de Colombia: una comparación bajo los enfoques de análisis discriminante, regresión logística y redes neuronales.

El desarrollo de modelos de predictibilidad de quiebra para distintos sectores de la Economía ha sido un tema estudiado con base en distintas metodologías por parte de la investigación académica, siempre buscando los modelos que mayor precisión presenten a la hora de predecir la insolvencia de las empresas como unidades económicas de generación de empleo y de bienestar para la sociedad. La presente investigación realiza una comparación en la precisión de tres modelos de predictibilidad de quiebra generados a través de los enfoques de análisis discriminante, regresión logística y redes neuronales, aplicando matrices de confusión, curvas ROC e indicadores de área bajo la curva – AUC. Los modelos son generados con base en la información financiera reportada a la Superintendencia de Transporte entre las vigencias 2016 y 2019, por las empresas que están sujetas a su inspección, control y vigilancia. Para el estudio se selecciona el sector Transporte debido a su papel primordial para el país a nivel económico y social, siendo fundamental para la competitividad nacional y la optimización de las cadenas de suministro. Finalmente, con el propósito de proceder con un análisis inferencial de los niveles de precisión obtenidos a través de las tres metodologías, se realiza el mismo procedimiento para 200 modelos generados de manera aleatoria, concluyendo que el mayor nivel de precisión lo generan las Redes Neuronales, seguidas por la Regresión Logística y el Análisis Discriminante.Introducción ; Estado del arte ; Marco teórico ; Muestra y metodología ; Conclusiones ; BibliografíaMagíster en Finanzas CorporativasMaestrí