A New Credit Scoring Model For Vehicle Leasing Company

Usaha kecil dan menengah menjadi salah satu bisnis yang terdampak akibat penyebaran virus corona. Situasi pandemi di Indonesia menyebabkan penderitaan besar pada perusahaan-perusahaan ini. Untuk mencegah kerugian di masa pandemi saat ini. PT XYZ memutuskan untuk membuat model penilaian kredit untuk memprediksi risiko dari calon pelanggan mereka. Model akan terdiri dari dua jenis. Yang pertama adalah penilaian atau kartu skor sistem pakar. Data yang diperoleh dari sistem pakar nantinya akan dimasukkan ke dalam machine learning menggunakan metode statistik untuk mendapatkan model credit scoring. Kerangka kerja CRISP-DM akan digunakan untuk memandu proses pembuatan untuk memastikan keluaran model yang andal

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

Credit scoring using the clustered support vector machine

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally

Using genetic algorithms for credit scoring system maintenance functions

ABSTRAC

ASSESSMENTS OF CREDITWORTHINESS OF CRAFTS IN CROATIA

The main purpose of the paper is to analyse financial reporting practice in the craft sector and to assess the quality of the financial and qualitative data available. For this purpose, the credit scoring as methodology was used. Here, evaluation of the information produces model which is enabling the crafts’ creditworthiness assessment. In terms of empirical research, the importance of qualitative data is confirmed. Due to the fact that crafts in majority are oriented towards local markets, the model has potential to be applied on local level management in financial institutions and within the crafts’ suppliers. Overall, this promotes better inclusion of the crafts within their business environment

Investigation into the Predictive Capability of Macro-Economic Features in Modelling Credit Risk for Small Medium Enterprises

This research project investigates the predictive capability of macro-economic features in modelling credit risk for small medium enterprises (SME/SMEs). There have been indications that there is strong correlation between economic growth and the size of the SME sector in an economy. However, since the financial crisis and consequent policies and regulations, SMEs have been hampered in attempts to access credit. It has also been noted that while there is a substantial amount of credit risk literature, there is little research on how macro-economic factors affect credit risk. Being able to improve credit scoring by even a small amount can have a very positive effect on a financial institution\u27s profits, reputation and ability to support the economy. Typically, in the credit scoring process two methods of scoring are carried out, application scoring model and behavioural scoring model. These models for predicting customers who are likely to default usually rely upon financial, demographic and transactional data as the predictive inputs. This research investigates the use of a much coarser source of data at a macro-economic level by a low level and high level regions in Ireland. Features such as level of employment/unemployment, education attainment, consumer spending trends and default levels by different banking products will be evaluated as part of the research project. In the course of this research, techniques and methods are established for evaluating the usefulness of macro-economic features. These are subsequently introduced into the predictive models to be evaluated. It was found that while employing coarse classification and subsequently choosing the macro-economic features with the highest information value in the predictive model, the accuracy across all performance measures improved significantly. This has proven that macro-economic features have the potential to be used in modelling credit risk for SMEs in the future