Classification of customer credit data for intelligent credit scoring system using fuzzy set and MC2 &amp;#x2014; Domain driven approach

Marikkannu, P.; Shanmugapriya, K.

doi:10.1109/icectech.2011.5941782

Cited by 7 publications

(3 citation statements)

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“…The main issue of credit scoring, which is a critical study topic in the banking sector, is anticipating bankrupts to acquire potentially profitable clients. The suggested technique divides consumers into five categories: best, good, satisfactory, bad, and worst (Marikkannu & Shanmugapriya, 2011).…”

Section: Introductionmentioning

confidence: 99%

Credit Scoring Active Telegram Channels Offering Stock Signals

Sohrabi

Jafarabad

Orfi

2022

IJFIFSA

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The impact of personal judgment on the assessment of an individual's financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant's total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as aThe methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

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Section: Introductionmentioning

confidence: 99%

Credit Scoring Active Telegram Channels Offering Stock Signals

Sohrabi

Jafarabad

Orfi

2022

IJFIFSA

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“…Credit scoring or credit risk assessment is an important research issue in the financial industry. The major challenge of credit scoring is to recruit the profitable customers by predicting the bankrupts [3].…”

Section: Introductionmentioning

confidence: 99%

Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company

Khakbaz¹,

Hajiheydari

Pourestarabadi³

2014

IJBER

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Today's competitive market leads industry to a serious fight. This fight has guided some companies to a sightless selling. Insurance companies lose lots of money each year because of not profitable and risky customers which are attracted blindly. Risky customers are one of the most important treats to insurance companies; therefore some of these companies adopt a credit scoring and risk assessment approach for identifying profitable and risky customers. One of the most preferable methods for risk assessment is data mining. In this article, authors would demonstrate a risk assessment problem in an Iranian leading insurance company. Car insurance customers of this company have been analyzed with six different data mining algorithms (C5, Classification and Regression Tree, Neural Networks, Logistic Regression, Bayesian Networks and Support Vector Machines) in two different approaches. One of these approaches is a direct approach in which the target field (risk) is predicted directly with data mining algorithms and then an ensemble model comprised from them. The other one is an indirect approach in which the target field would be divided in five fields, then five different ensemble models is comprised for each new target field. Afterwards the model with the highest confidence predicts the target fields for a test data record. At the end of this article the better results of indirect model would be shown.

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“…Ultimately, rating systems are a form of data scoring, also referred to as scoring models, a term commonly used within the data mining environment, which means filling in the outputs (Berry & Linoff, 2004). Scoring systems have been used in a range of academic fields, such as assessing an individual's repayment behaviour and calculates their risk of defaulting on a line of credit (please see Arsovski, Markoski, Pecev, Ratgeber & Petrov, 2014;Marikkannu & Shanmugapriya, 2011;Pedreschi, Giannotti, Guidotti, Monreale, Pappalardo, Ruggieri & Turini, 2018). A wide range of statistical and data mining techniques are applied to enable scoring to occur (please see Kitts, Freed & Vrieze, 2000;Langley, 1997;Grady, Schryver & Leuze, 1999).…”

Section: Ratings Frameworkmentioning

confidence: 99%

A Novel Framework for Constructing Sport-Based Rating Systems

Patel¹

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<p>This doctoral thesis examines the multivariate nature of sporting performances, expressed as performance on context specific tasks, to develop a novel framework for constructing sport-based rating systems, also referred to as scoring models. The intent of this framework is to produce reliable, robust, intuitive, and transparent ratings, regarded as meaningful, for performance prevalent in the sport player and team evaluation environment. In this thesis, Bracewell’s (2003) definition of a rating as an elegant form of dimension reduction is extended. Specifically, ratings are an elegant and excessive form of dimension reduction whereby a single numerical value provides an objective interpretation of performance. The data, provided by numerous vendors, is a summary of actions and performances completed by an individual during the evaluation period. A literature review of rating systems to measure performance, revealed a set of common methodologies, which were applied to produce a set of rating systems that were used as pilot studies to garner a set of learnings and limitations surrounding the current literature. By reviewing rating methodologies and developing rating systems a set of limitations and communalities surrounding the current literature were identified and used to develop a novel framework for constructing sport-based rating systems which output measures of both team and player-level performance. The proposed framework adopts a multi-objective ensembling strategy and implements five key communalities present within many rating methodologies. These communalities are the application of 1) dimension reduction and feature selection techniques, 2) feature engineering tasks, 3) a multi-objective framework, 4) time-based variables and 5) an ensembling procedure to produce an overall rating. An ensemble approach is adopted because it assumed that sporting performances are a function of the significant traits affecting performance. Therefore, performance is defined as performance=f(〖trait〗_1,…,〖trait〗_n). Moreover, the framework is a form of model stacking where information from multiple models is combined to generate a more informative model. Rating systems built using this approach provide a meaningful quantitative interpretation performance during an evaluation period. These ratings measure the quality of performance during a specific time-interval, known as the evaluation period. The framework introduces a methodical approach for constructing rating systems within the sporting domain, which produce meaningful ratings. Meaningful ratings must 1) yield good performance when data is drawn from a wide range of probability distributions that are largely unaffected by outliers, small departures from model assumptions and small sample sizes (robust), 2) be accurate and produce highly informative predictions which are well-calibrated and sharp (reliable), 3) be interpretable and easy to communicate and (transparent), and 4) relate to real-world observable outcomes (intuitive). The framework is developed to construct meaningful rating systems within the sporting industry to evaluate team and player performances. The approach was tested and validated by constructing both team and individual player-based rating systems within the cricketing context. The results of these systems were found to be meaningful, in that, they produced reliable, robust, transparent, and intuitive ratings. This ratings framework is not restricted within the sport of cricket to evaluate players and teams’ performances and is applicable in any sporting code where a summary of multivariate data is necessary to understand performance. Common model evaluation metrics were found to be limited and lacked applicability when evaluating the effectiveness of meaningful ratings, therefore a novel evaluation metric was developed. The constructed metric applies a distance and magnitude-based metrics derived from the spherical scoring rule methodology. The distance and magnitude-based spherical (DMS) metric applies an analytical hierarchy process to assess the effectiveness of meaningful sport-based ratings and accounts for forecasting difficulty on a time basis. The DMS performance metric quantifies elements of the decision-making process by 1) evaluating the distance between ratings reported by the modeller and the actual outcome or the modellers ‘true’ beliefs, 2) providing an indication of “good” ratings, 3) accounting for the context and the forecasting difficulty to which the ratings are being applied, and 4) capturing the introduction of any subjective human bias within sport-based rating systems. The DMS metric is shown to outperform conventional model evaluation metrics such as the log-loss, in specific sporting scenarios of varying difficulty.</p>

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Classification of customer credit data for intelligent credit scoring system using fuzzy set and MC2 — Domain driven approach

Cited by 7 publications

References 10 publications

Credit Scoring Active Telegram Channels Offering Stock Signals

Credit Scoring Active Telegram Channels Offering Stock Signals

Car Insurance Risk Assessment with Data Mining for an Iranian Leading Insurance Company

A Novel Framework for Constructing Sport-Based Rating Systems

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