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Brain and behavioural asymmetries have been documented in various taxa. Many of these asymmetries involve preferential left and right eye use. However, measuring eye use through manual frame-by-frame analyses from video recordings is laborious and may lead to biases. Recent progress in technology has allowed the development of accurate tracking techniques for measuring animal behaviour. Amongst these techniques, DeepLabCut, a Python-based tracking toolbox using transfer learning with deep neural networks, offers the possibility to track different body parts with unprecedented accuracy. Exploiting the potentialities of DeepLabCut, we developed Visual Field Analysis, an additional open-source application for extracting eye use data. To our knowledge, this is the first application that can automatically quantify left–right preferences in eye use. Here we test the performance of our application in measuring preferential eye use in young domestic chicks. The comparison with manual scoring methods revealed a near perfect correlation in the measures of eye use obtained by Visual Field Analysis. With our application, eye use can be analysed reliably, objectively and at a fine scale in different experimental paradigms. [ABSTRACT FROM AUTHOR]

Abstract With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring.

The main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank's decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring. [ABSTRACT FROM AUTHOR]

With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Neural networks are considered as a mostly wide used technique in finance and business applications. Thus, the main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour with respect to two aspects: the probability of single and consecutive missed payments for credit card customers. The proposed model is based on the bidirectional Long-Short Term Memory (LSTM) model to give the probability of a missed payment during the next month for each customer. The model was trained on a real credit card dataset and the customer behavioural scores are analysed using classical measures such as accuracy, Area Under the Curve, Brier score, Kolmogorov–Smirnov test, and H-measure. Calibration analysis of the LSTM model scores showed that they can be considered as probabilities of missed payments. The LSTM model was compared to four traditional machine learning algorithms: support vector machine, random forest, multi-layer perceptron neural network, and logistic regression. Experimental results show that, compared with traditional methods, the consumer credit scoring method based on the LSTM neural network has significantly improved consumer credit scoring. [ABSTRACT FROM AUTHOR]

In South Africa, almost 50% of the people who take loans cannot afford it. Previously, lenders were able to make deductions from a borrower's payslip but this practice is no longer allowed. Consequently, lenders are now far more vulnerable to default particularly if these loans are no longer being backed by any form of meaningful collateral. The aim of this study is to investigate the predictive power of some of the more popular classification techniques currently in use with specific attention to predicting the propensity for a borrower who is 90 days or more in arrears on an unsecured loan to pay over a fixed window period at least 30% of the total amount due. Results show that these classification techniques perform best for predicting payment patterns over a future horizon period between 3 and 12 months. It is also found that generalized additive models (especially using a generalized extreme value link function), which have not been extensively explored within the credit scoring literature, outperformed all the other classifiers considered in this study. [ABSTRACT FROM AUTHOR]

Abstract: After credit has been granted, lenders use behavioural scoring to assess the likelihood of default occurring during some specific outcome period. This assessment is based on customers’ repayment performance over a given fixed period. Often the outcome period and fixed performance period are arbitrarily selected, causing instability in making predictions. Behavioural scoring has failed to receive the same attention from researchers as application scoring. The bias for application scoring research can be attributed, in part, to the large volume of data required for behavioural scoring studies. Furthermore, the commercial sensitivities associated with such a large pool of customer data often prohibits the publication of work in this area. This paper focuses on behavioural scoring and evaluates the contrasting effects of altering the performance period and outcome period using 7-years worth of data from the Irish market. The results of this work indicate that a 12-month performance period yields an easier prediction task when compared with other historical payment periods of varying lengths. This article also quantifies differences in the classification performance of logistic regression arising from different outcome periods length. Our findings show that the performance of a logistic regression classifier degrades significantly when the outcome window is extended beyond 6-month. Finally we consider different approaches to how the concept of default is defined. Typically whether the customer is identified as a default risk or not is set based on either (i) whether the account is in default at the end of the outcome period or (ii) at any time during the outcome period. This paper studies both approaches and finds that the latter approach resulted in an easier classification problem, that is, it gives the highest assurance that the classification will be correct. [Copyright &y& Elsevier]

There have been recent calls to develop protocols that collect unambiguous measures of behaviour using automatic techniques in conditioning experiments on magnetic orientation. Here, we describe an automated technique for recording the behaviour of Pekin ducks in a conditioning test that allows them to express unrestricted searching behaviour. Pekin ducks were trained to find hidden food in one corner of a square arena below which was placed a magnetic coil that produced a local magnetic anomaly. The trigeminal nerve was anaesthetised by injection of lignocaine hydrochloride 2-3 mm caudal to the medial canthus of each eye, medial to the globe, prior to the presentation of unrewarded tests. Lignocaine-treated ducks showed no initial preference for the magnetic anomaly whereas saline-treated control ducks showed a significant preference at the same age. A second experiment was undertaken in which the trigeminal nerve was surgically severed and 2-3 mm removed, and this surgery abolished the previously observed preference for the corner with the magnetic coil in a small number of ducks. These data show that Pekin ducks are able to detect and use magnetic stimuli to guide unrestricted search behaviour and are consistent with a hypothesis of magnetoreception involving a putative cluster of magnetite in the upper beak. [ABSTRACT FROM AUTHOR]

This paper discusses the use of dynamic modelling in consumer credit risk assessment. It surveys the approaches and objectives of behavioural scoring, customer scoring and profit scoring. It then investigates how Markov chain stochastic processes can be used to model the dynamics of the delinquency status and behavioural scores of consumers. It discusses the use of segmentation, mover–stayer models and the use of second‐ and third‐order models to improve the fit of such models. The alternative survival analysis proportional hazards approach to estimating when default occurs is considered. Comparisons are made between the ways credit risk is modelled in consumer lending and corporate lending. [ABSTRACT FROM PUBLISHER]