Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

Bayesian neural network learning for repeat purchase modelling in direct marketing.

We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer\slash company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.Marketing; Companies; Models; Model; Problems; Neural networks; Networks; Variables; Credit;

An approach to securitisation in Europe NPLs- machine learning model field lab project Nova SBE | moody’s analytics

As a consulting project, we were proposed to develop a neural network (NN) to predict mortgage states in one year, based on the paper ‘Deep Learning for Mortgage Risk’ by Justin A. Sirignano, Apaar Sadhwani, Kay Giesecke (2018). We developed a neural network model with the aim of being able to capture the relationships between the different variables, with respect to each other and to the response variable (the loan status in 12 months), better than traditional classification methods, such as logistic regressions, which constitute the benchmark set. Data was provided by Moody’s, relating borrower, property and loan/financing characteristics for several mortgages over several periods in time (over 350 thousand mortgages). The purpose of our model is to predict the probabilities to transition to different states at a certain point in time. The best results were obtained with a 10 layer, 500 nodes per layer network. The model can identify a large portion of defaults. At the cost, however, of a general overestimation of the default rate over the years. The capability of identifying loans that will be in arrears is also acceptable, with, again, an overestimation of the verified rate. Variables relating to borrower characteristics and history as well as financing are found to be the most significant

A novel knowledge discovery based approach for supplier risk scoring with application in the HVAC industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis research has led to a novel methodology for assessment and quantification of supply risks in the supply chain. The research has built on advanced Knowledge Discovery techniques and has resulted to a software implementation to be able to do so. The methodology developed and presented here resembles the well-known consumer credit scoring methods as it leads to a similar metric, or score, for assessing a supplier’s reliability and risk of conducting business with that supplier. However, the focus is on a wide range of operational metrics rather than just financial, which credit scoring techniques typically focus on. The core of the methodology comprises the application of Knowledge Discovery techniques to extract the likelihood of possible risks from within a range of available datasets. In combination with cross-impact analysis, those datasets are examined for establish the inter-relationships and mutual connections among several factors that are likely contribute to risks associated with particular suppliers. This approach is called conjugation analysis. The resulting parameters become the inputs into a logistic regression which leads to a risk scoring model the outcome of the process is the standardized risk score which is analogous to the well-known consumer risk scoring model, better known as FICO score. The proposed methodology has been applied to an Air Conditioning manufacturing company. Two models have been developed. The first identifies the supply risks based on the data about purchase orders and selected risk factors. With this model the likelihoods of delivery failures, quality failures and cost failures are obtained. The second model built on the first one but also used the actual data about the performance of supplier to identify risks of conducting business with particular suppliers. Its target was to provide quantitative measures of an individual supplier’s risk level. The supplier risk scoring model is tested on the data acquired from the company for its performance analysis. The supplier risk scoring model achieved 86.2% accuracy, while the area under curve (AUC) was 0.863. The AUC curve is much higher than required model’s validity threshold value of 0.5. It represents developed model’s validity and reliability for future data. The numerical studies conducted with real-life datasets have demonstrated the effectiveness of the proposed methodology and system as well as its future potential for industrial adoption

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

Feature Selection For The Fuzzy Artmap Neural Network Using A Hybrid Genetic Algorithm And Tabu Search

Prestasi pengelas rangkaian neural amat bergantung kepada set data yang digunakan dalam process pembelajaran. The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning

Systemic acquired critique of credit card deception exposure through machine learning

Artigo publicado em revista científica internacionalA wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.info:eu-repo/semantics/publishedVersio

Supervised machine learning algorithms for the estimation of the probability of default in corporate credit risk

This thesis investigates the application of non-linear supervised machine learning algorithms for estimating Probability of Default (PD) of corporate clients. To achieve this, the thesis is separated into three different experiments: 1. The first experiment investigates a wrapper feature selection method and its application on the support vector machines (SVMs) and logistic regression (LR). The logistic regression model is the most popular approach used for estimating PD in a rich default portfolio. However, other alternatives to PD estimation are available. SVMs method is compared to the logistic regression model using the proposed feature selection method. 2. The second experiment investigates the application of artificial neural networks (ANNs) for estimating PD of corporate clients. In particular ANNs are regularized and trained both with classical and Bayesian approach. Furthermore, different network architectures are explored and specifically the Bayesian estimation and regularization is compared to the classical estimation and regularization. 3. The third experiment investigates the k-Nearest Neighbours algorithm (KNNs). This algorithm is trained using both Bayesian and classical methods. KNNs could be efficiently applied to estimating PD. In addition, other supervised machine learning algorithms such as Decision trees (DTs), Linear discriminant analysis (LDA) and Naive Bayes (NB) were applied and their performance summarized and compared to that of the SVMs, ANNs, KNNs and logistic regression. The contribution of this thesis to science is to provide efficient and at the same time applicable methods for estimating PD of corporate clients. This thesis contributes to the existing literature in a number of ways. 1. First, this research proposes an innovative feature selection method for SVMs. 2. Second, this research proposes an innovative Bayesian estimation methods to regularize ANNs. 3. Third, this research proposes an innovative Bayesian approaches to the estimation of KNNs. Nonetheless, the objective of the research is to promote the use of the Bayesian non-linear supervised machine learning methods that are currently not heavily applied in the industry for PD estimation of corporate clients