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Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Scoring cards"

1

Bugera, Vladimir, Hiroshi Konno, and Stanislav Uryasev. "Credit cards scoring with quadratic utility functions." Journal of Multi-Criteria Decision Analysis 11, no. 4-5 (July 2002): 197–211. http://dx.doi.org/10.1002/mcda.327.

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2

Бобков, Сергей Петрович, Станислав Вадимович Суворов, Артем Игоревич Орлов, and Егор Алексеевич Пивнев. "USING MACHINE LEARNING METHODS TO ASSESS RISKS WHEN IMPLEMENTING A NEW CREDIT PRODUCT." «Izvestia vyssih uchebnyh zavedenij. Seria «Ekonomika, finansy i upravlenie proizvodstvom», no. 4 (46) (December 29, 2020): 59–63. http://dx.doi.org/10.6060/ivecofin.2020464.509.

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Анотація:
The article discusses the issues of assessing the creditworthiness of individuals using credit scoring. This rating system is an effective approach to determining the level of risk for a specific customer segment. This is especially true of the situation when a credit institution launches a new credit product. The main idea proposed in the article is that new customer scoring cards are created on the basis of existing cards by mathematical data processing. The novelty of the method lies in the fact that the scoring is done based on a dedicated subset of customer data stored in the corporate storage. The approach helps to make a decision on granting a loan and can be recommended for use in lending
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3

Бакун, Сабіна Антонівна, and Петро Іванович Бідюк. "The Method of Construction Scoring Cards Using SAS Platform." Research Bulletin of the National Technical University of Ukraine "Kyiv Politechnic Institute", no. 2 (May 17, 2016): 23. http://dx.doi.org/10.20535/1810-0546.2016.2.67487.

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4

Agarwal, Sumit, Paige Marta Skiba, and Jeremy Tobacman. "Payday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles?" American Economic Review 99, no. 2 (April 1, 2009): 412–17. http://dx.doi.org/10.1257/aer.99.2.412.

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5

Lane, Peter L., Amado Alejandro Báez, Thomas Brabson, David D. Burmeister, and John J. Kelly. "Effectiveness of a Glasgow Coma Scale Instructional Video for EMS Providers." Prehospital and Disaster Medicine 17, no. 3 (September 2002): 142–46. http://dx.doi.org/10.1017/s1049023x00000364.

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AbstractIntroduction:The Glasgow Coma Scale (GCS) is the standard measure used to quantify the level of consciousness of patients who have sustained head injuries. Rapid and accurate GCS scoring is essential.Objective:To evaluate the effectiveness of a GCS teaching video shown to prehospital emergency medical services (EMS) providers.Methods:Participants and setting—United States, Mid-Atlantic region EMS providers. Intervention—Each participant scored all of the three components of the GCS for each of four scenarios provided before and after viewing a video-tape recording containing four scenarios. Design—Before-and-after single (Phase I) and parallel Cohort (Phase II). Analysis— Proportions of correct scores were compared using chi-square, and relative risk was calculated to measure the strength of the association.Results:75 participants were included in Phase I. In Phase II, 46 participants participated in a parallel cohort design: 20 used GCS reference cards and 26 did not use the cards. Before observing the instructional video, only 14.7% score all of the scenarios correctly, where as after viewing the video, 64.0% scored the scenarios results were observed after viewing the video for those who used the GCS cards (p = 0.001; RR = 2.0; 95% CI = 1.29 to 3.10) than for those not using the cards (p <0.0001; RR = 10.0; 95% CI = 2.60 to 38.50).Conclusions:Post-video viewing scores were better than those observed before the video presentation. Ongoing evaluations include analysis of longterm skill retention and scoring accuracy in the clinical environment.
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6

Gupte, Aakanksha, and Dr Gayatri Doctor. "Aadhar Enabled Public Distribution System (AEPDS), Beneficiary Survey and Assessment Framework." Computer Science & Engineering: An International Journal 11, no. 6 (December 31, 2021): 1–14. http://dx.doi.org/10.5121/cseij.2021.11601.

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Анотація:
Public Distribution System (PDS) has evolved as a system of managing scarcity through distribution of food grains at affordable prices. In 2015, Aadhar enabled Public Distribution System (AePDS) made linking AADHAR cards of the beneficiaries to the Ration Cards mandatory enabling the Fair Price Shops to use biometrics to authenticate the beneficiaries improving efficiency and transparency of the system. The study aims to access the application of AePDS at) w.r.t service provided to the beneficiaries, challenges and benefits of the system; infrastructure adopted for efficient implementation for the process of grains distribution in the context of Raigad District in Mumbai Metro Politian Region. A strategic framework and scoring system were developed to assess the system based on literature studies, analysis of existing scenario and structured stakeholder surveys conducted in the Raigad District. Hence, on the basis of the responses scoring was done, reasons were noted and recommendations were given for the challenges.
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7

Pinto, Mary Beth, Diane H. Parente, and Todd S. Palmer. "Materialism and Credit Card Use by College Students." Psychological Reports 86, no. 2 (April 2000): 643–52. http://dx.doi.org/10.2466/pr0.2000.86.2.643.

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Анотація:
Much has been written in the popular press on credit card use and spending patterns of American college students. The proliferation of credit cards and their ease of acquisition ensure that students today have more opportunities for making more credit purchases than any other generation of college students. Little is known about the relationship between students' attitudes towards materialism and their use of credit cards. A study was conducted at three college campuses in the northeastern part of the United States where a total of 1,022 students were surveyed. Students' attitudes toward use of credit and their credit card balances were evaluated relative to their scores on Richins and Dawson's Materialism Scale (1992). Our findings suggest no significant difference between those individuals scoring high versus low on the Materialism Scale in terms of the number of credit cards owned and the average balance owed. Individuals high on materialism, however, significantly differed in terms of their uses for credit cards and their general attitude toward their use.
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8

Kuznietsova, Natalia V. ,. "PRACTICAL USING OF SCORING CARDS’ DEVELOPMENT METHODOLOGY FOR AUTMOBILE LOANS RISKS ANALYSIS." ELECTRICAL AND COMPUTER SYSTEMS 24, no. 100 (March 28, 2017): 104–11. http://dx.doi.org/10.15276/eltecs.24.100.2017.13.

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9

Leichsenring, Falk. "The Role of Structure in the Assessment of Psychopathology." European Journal of Psychological Assessment 20, no. 4 (January 2004): 275–82. http://dx.doi.org/10.1027/1015-5759.20.4.275.

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Анотація:
Summary: This study investigated the role that the structure of a diagnostic instrument plays in the assessment of personality functioning. Empirical studies have shown that the cards of the Rorschach and Holtzman Inkblot Technique (HIT) vary significantly with regard to their structure. Thus, it was possible to investigate if cards of high vs. low structure tend to elicit specific diagnostically useful responses. For this purpose, samples of normals (n = 30), patients with neurotic disorders (n = 30), borderline patients (n = 30), acute schizophrenics (n = 25), and chronic schizophrenics (n = 25) were studied with the HIT. For each diagnostic group it was examined if cards of high vs. low structure tended to elicit more thought disordered responses, hostility, and anxiety according to the HIT scoring system. With regard to structure, two aspects were differentiated, structural vs. interpretative ambiguity of the HIT cards. In all nonschizophrenic groups, cards of high structural ambiguity elicited significantly less thought disordered responses. By contrast, cards of high interpretative ambiguity elicited more thought disordered responses, anxiety, and hostility in all groups except the chronic schizophrenics. The measures of structural vs. interpretative ambiguity of the HIT cards showed a negative correlation in all diagnostic groups. According to these results, both aspects of ambiguity and their interplay play an important role in the assessment of psychopathology, at least within the range of ambiguity represented by the inkblots of the HIT.
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10

Facchin, Alessio, Lavinia Giordano, Giovanni Brebbia, and Silvio Maffioletti. "Application, limits, scoring and improvements of Groffman Visual Tracing test." Scandinavian Journal of Optometry and Visual Science 13, no. 1 (July 31, 2020): 2–9. http://dx.doi.org/10.5384/sjovs.vol13i1p2-9.

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Анотація:
The Groffman Visual Tracing (GVT) test is a psychometric oculomotor test comprising two cards with five contorted and intersected lines, and which is available for the clinical evaluation of ocular movements. The participant starts from the letter at the top, follows the line, and reports the corresponding number at the bottom of each line. The aim of this study is to evaluate two claims made when details of the GVT test were originally reported: whether it is a developmental test, and the feasibility of its application starting from primary school children up to adults. This was achieved by using the GVT test and a simplified version of it. In two consecutive experiments, we tested two groups of children and adults. In the first experiment, 75 children (1st, 3rd, and 5th grade) and 25 adults underwent the GVT test. In the second experiment, 115 children from 1st to 5th grade underwent a simplified version of the test. Total scoring, accuracy and time to complete the test were evaluated. In the first experiment, 24% of children in the 1st and 3rd grades did not follow any lines correctly due to the difficulty of the test. In the second experiment, all participants were able to perform the test with both cards, and the accuracy improved significantly with age (p<0.0001). The time required to follow the lines was found to decrease with age (p<0.0001), and the accuracy improves (p<0.0001) compared with the standard version. The standard version of the GVT test has proven to be too difficult for younger children and a modified version produced improved results. Children at or below the 5th grade should to be tested using the modified version. Older children and adults can be tested with the standard version. Specific norms based on execution times and accuracy should to be established.
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Більше джерел

Дисертації з теми "Scoring cards"

1

Hamilton, Robert. "[Credit] scoring : predicting, understanding and explaining consumer behaviour." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/13053.

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Анотація:
This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.
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2

Martinez, John Brett. "Credit card credit scoring and risk based lending at XYZ Credit Union." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1752.

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3

Norrie, James, and not supplied. "Improving results of project portfolio management in the public sector using a balanced strategic scoring model." RMIT University. Property, Construction and Project Management, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20070208.152804.

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Анотація:
This thesis suggests improvements, from a strategic perspective, to the practice of scoring projects in public sector organisations. It is argues that current approaches, notably project portfolio managing (PPM), are inadequate for many such organisations, and in fact prone to problems and failure. In particular, present scoring/prioritization approaches in such contexts, largely tend to focus on financial risk/return logic. It is argued that the end result of such a ranking approach is often a non-strategic portfolio project. To address these problems, the candidate proposed the refinement of the scoring approach for project portfolios via the incorporation of Kaplan & Norton's ideas in their Balanced Scorecard (BSC). BSC introduces, apart from purely financial considerations, other 'softer' perspectives (customer, internal business processes, learning and growth) which in combination place a more inclusive emphasis on the vision and strategy of the organisation. In this thesis, it is proposed that the combined PPM and BSC scoring approach amounts to more strategic project selection. Several case studies are conducted to illustrate the merits of the combined PPM/BSC logic. These include case studies in both private and public sector organisations.
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4

Islam, Md Samsul, Lin Zhou, and Fei Li. "Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring." Thesis, Blekinge Tekniska Högskola, Sektionen för management, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2099.

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Анотація:
Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification outcomes of this study that neural network gives slightly better results than discriminant analysis and logistic regression. It should be noted that it is not possible to draw a general conclusion that neural network holds better predictive ability than logistic regression and discriminant analysis, because this study covers only one dataset. Moreover, it is comprehensible that a “Bad Accepted” generates much higher costs than a “Good Rejected” and neural network acquires less amount of “Bad Accepted” than discriminant analysis and logistic regression. So, neural network achieves less cost of misclassification for the dataset used in this study. Furthermore, in the final section of this study, an optimization algorithm (Genetic Algorithm) is proposed in order to obtain better classification accuracy through the configurations of the neural network architecture. On the contrary, it is vital to note that the success of any predictive model largely depends on the predictor variables that are selected to use as the model inputs. But it is important to consider some points regarding predictor variables selection, for example, some specific variables are prohibited in some countries, variables all together should provide the highest predictive strength and variables may be judged through statistical analysis etc. This study also covers those concepts about input variables selection standards.
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5

Кузнєцова, Наталія Володимирівна. "Методи і моделі аналізу, оцінювання та прогнозування ризиків у фінансових системах". Doctoral thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/26340.

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Анотація:
Роботу виконано в Інституті прикладного системного аналізу Національного технічного університету України «Київський політехнічний інститут імені Ігоря Сікорського».
У дисертаційній роботі розроблено системну методологію аналізу та оцінювання фінансових ризиків, яка ґрунтується на принципах системного аналізу та менеджменту ризиків, а також запропонованих принципах адаптивного та динамічного менеджменту ризиків. Методологія включає: комбінований метод обробки неповних та втрачених даних, ймовірнісно-статистичний метод оцінювання ризику фінансових втрат, динамічний метод оцінювання ризиків, який передбачає побудову різних типів моделей виживання, метод структурно-параметричної адаптації, застосування скорингової карти до аналізу ризиків фінансових систем і нейро-нечіткий метод доповнення вибірки відхиленими заявками. Містить критерії урахування інформаційного ризику, оцінки якості даних, прогнозів та рішень, квадратичний критерій якості опрацювання ризику та інтегральну характеристику оцінювання ефективності методів менеджменту ризиків. Практична цінність одержаних результатів полягає у створенні розширеної інформаційної технології та інформаційної системи підтримки прийняття рішень на основі запропонованої системної методології.
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6

LIAO, JEN-CHIEN, and 廖仁傑. "Building model for credit scoring and credit rating of credit cards." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/26485252561720367196.

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Анотація:
博士
中原大學
商學博士學位學程
101
The government reinforces risk control for the respect of market mechanism. Bankers consider their costs and profits, and therefore they have to adopt differential interest rates (i.e. determining interest rates based on the level of risks) for both risk control and profits in order to minimize credit risks and maximize excess profits. However, the scoring model must allow the adjustment of credit scoring for different economic environments so that banks are able to play the role of loaning and media of investment financing in an atmosphere of competition and profit seeking. Still, banks are required to publish interest rate information on a regular basis to allow consumers to have choices to get in and out. This study is intended to show how a bank filters out important information value variables and establish credit score cards using a bank as the subject of study. Previous studies focused on building Logit regression models based on finite number of samples. The coefficients used in models and whether bias occur in subsequent statistics tests were rarely discussed in the studies and the assumption of no bias was often made. For this, bootstrapping method was introduced in the study of the credit rating in the bank selected to see if bias was produced in model, thus providing better accuracy for scientific verification model. For the considerations of the static scoring model built for evaluation of new credit card applications and subsequent transactions, dynamic model for behavior scoring (established using quantile regression) and the correlation of client’s probability of default in the economic fluctuation (using copula to evaluate the correlation of probability of default in two years), it is necessary to know whether the models are still applicable, how to convert probability of default into credit scores using linear transform and how to establish internal rating for differential interest rates. Also in order to ensure the stability and reliability of the credit scoring model, the credit rating has to be verified to build a method that meets the capital requirement of Basel Accord. The hope was to establish a credit card scoring system through internal rating in order to reflect clients’ risks, allow reasonable profits and prices that are affordable to consumers and create a win-win for banks and consumers.
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7

Kuo, Shu-Wei, and 郭淑薇. "A Study on Establishing a Cash-Advance Card Credit Scoring Model and the Card’s Risk Management." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/55290311413014716673.

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Анотація:
碩士
國立中央大學
財務金融學系碩士在職專班
94
Abstract Cash-Advance Card is among the typical trendy products brought to the market by many financing businesses here in Taiwan in the last years to answer the needs arise following the change of consumer awareness. Indeed at the beginning stage the sales of cash-advance card creates unexpectedly fruitful margin to these financing businesses however, following the high level NPL (non-performing loan) ratio remaining unchanged as a result of excessive expansion of granting the credit line to the customers under the banks’ competing promotion policy, the financing institutions gradually find it inevitable to expose their shortfall in risk monitoring and controlling systems. In view of these, the present study aims to explore the said issue, through empirical methods, by locating the factors leading to the contract violation for most of the cash-advance card users. The study, by putting variables influencing the contract violation into two aspects: the static aspect, i.e. before-loan personality characteristics, and the dynamic aspect, i.e. after-loan banking credit data, tries to formulate and establish an optimal credit scoring model tailored to the unique cash-advance card products in Taiwan, on the ground of firstly conducting a customer personality characteristics analysis before-loan and secondly implementing a proactive customer credit status management. The study finds the empirical analysis results as the follows: 1. The variables affecting principal risks of cash-advance card customer’s contract violation: In personality characteristics aspect, the present study finds that the major factors affecting credit granting quality and available in the subscribing application form are the four items: income pattern, annual income, education level, application source route. In after-loan banking credit data aspect, the study finds five affecting factors, namely: finalized credit line, initial loan line, batch of short-term loan, credit line multiplication ratio, income contribution percentage. 2. Emphasis on After-loan Proactive Debt Management: This is the most frequently overlooked area in the existing literature according to the literature review, however, empirical approaches demonstrate that after-loan debt management of proactive type is definitely influential to the effectiveness of credit line risk management. The study especially puts focus on checking and verifying the “interim credit line granting” practice found uniquely exclusively in cash-advance card management and finds that there are correlations between customer’s overdue payment and the bank’s finalized credit line, initial loan line, batch of short-term loan, credit line multiplication ratio, and income contribution percentage. 3. Proposal on Continuing Consecutive Credit Risk Management: To the results found through empirical method, the risk management area shall not be confined to the loan seeking customer’s initial conditions or status and shall be well expanded to areas including “continuing consecutive management” and reviewing and modifying the decision making direction timely. The study believes that the financing businesses will not only realize their ideal of sustained operations but also serve as a stream of force contributing to the social stability. 4. Proposal on Further Releasing Credit Line Discretion to Financing Institutions: The study also finds that the governmental authorities and agencies influence the operation performance of financing businesses with their regulations, orders and policies. Fortunately enough the study finds that in recent years the authorities or agencies have been redirecting towards the management models that vest banks larger range of credit line discretion. This will definitely give the banks more room to optimize their continuing consecutive debt management and this, in turn, will add extra supportive forces for risk management domain to develop not only more wholesomely but also more efficiently. Key word: Cash-Advance Card ；Consumer’s Finance；Non-performing loan ratio；Risk Management；Proactive Debt Management
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8

Liu, Tai-Gu, and 劉泰谷. "The Building and Analysis of Credit Card Scoring Model." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76501101593995508650.

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Анотація:
碩士
世新大學
財務金融學系
92
In a period of tiny profit time, banks can’t make profits like before. In order to keep and increase profits, banks now promote a lot of preferential credit card programs to grab market ratio. But this action could possibly increase credit risk and cost for the banks. In the past, credit card approval was determined by the bank’s employees subjective judgment. But due to the rapid growth of the credit card business, banks now must learn how to use an objective scoring system for card authorization while increasing efficiency and reducing the bad debt ratio. Quite a few credit scoring models have been proposed to forecast a customers’ default rate. But they didn’t take into consideration a rejection inference in the development process, leading to a severse sample selection error, and the prediction power could be thus exaggerated. To correct this bias, this study integrates card holders and the rejected ones to build credit application scoring model, in addition to a credit management scoring model. The former be applied by banks to estimate credit card application’s default rate, and the latter help banks to management the existing quality credit card business.
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9

Ming-Chien, Lee, and 李明謙. "The Application Of Logistic Regression Model In Credit Card Scoring." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/89074160771372454288.

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Анотація:
碩士
輔仁大學
應用統計學研究所
90
Consumer credit nowadays play a major economical role in Taiwan. The volume of credit business has greatly expanded and the use of credit scoring through the evaluation of large credit portfolio becomes crucial to guard against any management risk in the credit industry. The objectives of this study is to devise a credit scoring system for credit granting decisions made by the issue bank of credit card markets. In the process of scoring, individual characteristics profiles are transformed into a score such that the score distributions derived from the two groups: accepted or rejected are separated as much as possible. The score is then a basis for the making decisions about granting credit, adjusting credit limits or targeting specific markets. Scorecards are usually built using the logistic regression method which estimates the relationship between the individual characteristics and the log of the odds (risk) so that the score point weights can be calculate directly from the regression coefficients. Standard exploratory binary analyses : cross-table analysis, association analysis, and chi-square automatic interaction detection (chaid), are performed to detect the significant variables and evaluate the data structure. Sampling design is on the basis of outcome results of the decision tree. It shows that the variables like gender, education level, martial status, job position, occupation, and age related with the response variable : good or bad credit of credit card holders. However, this credit granting decision is not based on the variables such as annual income, etc. We summarize the classification outcomes of logistic regression analysis and compare the performance of models by classification table. And various measures : Kolmogorov-Smirnov two-sample test statistic, divergence statistic, and Gini coefficient are defined and used to describe the relative discriminant power of a scoring system. For comparison purpose, both completely categorical model and mixed model (with both discrete and continuous covariates) are applied. The performance of the mixed model is slightly better than the completely categorical model.
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10

陳義先. "Combined Logit and ANN Models to Construct the Credit Card Scoring." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/40387013937288078237.

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Анотація:
碩士
真理大學
財經研究所
92
In Taiwan, the using amount of credit card and outstanding credit has increased rapidly and the competition of banks turns white-hot. The credit risk also increase. For these reasons, detecting probability of bad debt actively, identifying customers of higher profit return, and increasing customer’s loyalty, is the best direction of bank strategy. This paper applied Logit model and ANN models to construct credit scoring system. Results shows that sex,age,income, education, marriage ,occupation..variables have a significance to normal and default cards. In addition, female, older, high income, high education, marriaged, high title and VIP have a lower credit risk. In the last, we construct a credit scoring systems combined this two models. It will decrease the default risk of credit scoring.
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Книги з теми "Scoring cards"

1

Byers, Ann. First credit cards and credit smarts. New York: Rosen Pub., 2010.

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2

1672-1769, Hoyle Edmond, ed. The new Hoyle: Standard games including all modern card games : new laws of contract bridge and new scoring rules : chess, checkers, backgammon, Camelot, ping pong, bowling, billiards, pool, etc. Place of publication not identified]: [Literary Licensing], 2013.

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3

Editors, The Silver Lake. Credit Scores, Credit Cards: How Consumer Finance Works/How to Avoid Mistakes and Manage Your Accounts Well. Aberdeen: Silver Lake Pub., 2005.

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Scorching supercars. North Mankato, Minnesota: Capstone Press, 2015.

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5

(Editor), The Silver Lake, ed. Credit Scores, Credit Cards: How Consumer Finance Works: How to Avoid Mistakes and How to Manage Your Accounts Well. Silver Lake Publishing, 2005.

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6

Ainslie's Complete Hoyle: Rules, Strategies, Scoring, Bidding, Betting Systems. Fireside, 2003.

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7

Sheets, Massin Score. Scrabble Game Score Sheet: 120 Large Score Sheet Pad for Upto 4 Players Scoring Sheet for Scrabble Players Score Keeping Pads for Scrabble Puzzle Word Building Game Score Record Book for Scrabble Board Game Large Score Keeper Cards 8. 5 X 11. Independently Published, 2020.

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8

Withers, Jeremy. Futuristic Cars and Space Bicycles. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781789621754.001.0001.

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Futuristic Cars and Space Bicycles is the first book to examine the history of representations of the automobile and of marginalized transportation technologies such as the bicycle throughout the history of American science fiction. With chapters ranging from ones on the early science fiction of the pulp magazine era of the 1920s and 1930s, on up to chapters on the postcyberpunk of the 1990s and more recent science fiction media of the 2000s such as web television, zines, and comics, this book argues that science fiction by and large perceives the car as anything but a marvelous invention of modernity. Rather, the genre often scorns and ridicules the automobile and instead frequently promotes more sustainable, more benign, more restrained technologies of movement such as the bicycle.
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9

Publishing, Ob. MY Scattergories Scoresheet: MY Scattergories Score Sheet Keeper - My Scoring Pad for Scattergories Game- My Scattergories Score Game Record Book - My Game Record Notebook - My Score Card Book - 6 X 9 - 120 Pages. Independently Published, 2020.

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Частини книг з теми "Scoring cards"

1

Wang, Maoguang, and Hang Yang. "Research on Customer Credit Scoring Model Based on Bank Credit Card." In IFIP Advances in Information and Communication Technology, 232–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46931-3_22.

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2

Rogers, John C., Conway T. Rucks, and Shawne Swindler. "A Credit Scoring Model to Evaluate the Credit Worthiness of Credit Card Applicants." In Proceedings of the 1982 Academy of Marketing Science (AMS) Annual Conference, 585. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16946-0_177.

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3

Tran, Duc Quynh, Doan Dong Nguyen, Huu Hai Nguyen, and Quang Thuan Nguyen. "An Ensemble Learning Approach for Credit Scoring Problem: A Case Study of Taiwan Default Credit Card Dataset." In Modelling, Computation and Optimization in Information Systems and Management Sciences, 283–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92666-3_24.

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4

Kitchin, Rob. "Big Brother is Watching and Controlling You." In Data Lives, 161–68. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529215144.003.0020.

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This chapter examines how data-driven technologies are deployed as mass surveillance and social credit scoring in China and their threat to democracy. Over the last decade, China has put in place a state-sponsored system of mass automated surveillance. It has successfully managed to limit the Internet to state-approved websites, apps, and social media, corralling users into a monitored, non-anonymous environment and preventing access to overseas media and information. From December of 2019, all mobile phone users registering new SIM cards must agree to a facial recognition scan to prove their identity. The state has also facilitated the transition from anonymous cash to traceable digital transactions. Most significantly, the state has created a social credit scoring system that pulls together various forms of data into a historical archive and uses it to assign each citizen and company a set of scores that affects their lifestyles and ability to trade. On the one hand, this is about making the credit information publicly accessible, so that those who are deemed untrustworthy are publicly shamed and lose their reputation. On the other hand, it is about guilt-by-association and administering collective punishment. This sociality works to minimize protest and unrest and reinforce the logic of the system.
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5

López, Eduardo Emmanuel Rodríguez, Jean Sandro Chery, Teresita de Jesús Álvarez Robles, and Francisco Javier Álvarez Rodríguez. "Hedonic Utility Scale (HED/UT) Modified as a User Experience Evaluation Method of Performing Talkback Tutorial for Blind People." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 62–77. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-8539-8.ch005.

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Hedonic utility scale is a user experience (UX) evaluation method that, through a questionnaire, collects the hedonic and utilitarian dimensions of a product by rating items belonging to each dimension. In this chapter, it is proposed to adapt this method for its application with blind users using the Google TalkBack tutorial as a case study. Based on Nielsen's heuristics, five blind users rated the tutorial after completing each of its five tasks. To ensure inclusiveness in the adaptation of the method, this could be answered verbally and with the use of cards written in Braille, while, for questions of practicality in the evaluation, the number of items was reduced as well as changed the way of scoring (scale and equations) with respect to the original HED/UT. The scale of grades was ranked from 1 (very little) to 5 (quite), getting TalkBack scores between 4 and 5. The results show that the TalkBack tutorial is generally well accepted and well rated by users in both dimensions (hedonic and utility).
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6

Bose, Indranil, Cheng Pui Kan, Chi King Tsz, Lau Wai Ki, and Wong Cho Hung. "Data Mining for Credit Scoring." In Advances in Banking Technology and Management, 309–23. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-675-4.ch019.

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Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.
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7

Bose, Indranil, Cheng Pui Kan, Chi King Tsz, Lau Wai Ki, and Wong Cho Hung. "Data Mining for Credit Scoring." In Data Warehousing and Mining, 2449–63. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch148.

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Анотація:
Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.
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8

Shi, Yong, Yi Peng, Gang Kou, and Zhengxin Chen. "Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications." In Research and Trends in Data Mining Technologies and Applications, 242–75. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-271-8.ch009.

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This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.
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9

Shi, Yong, Yi Peng, Gang Kou, and Zhengxin Chen. "Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications." In Data Mining Applications for Empowering Knowledge Societies, 1–25. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-657-0.ch001.

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Анотація:
This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.
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10

Shi, Yong, Yi Peng, Gang Kou, and Zhengxin Chen. "Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications." In Data Warehousing and Mining, 26–49. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch004.

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Анотація:
This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.
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Тези доповідей конференцій з теми "Scoring cards"

1

Shoombuatong, Watshara, Hui-Ling Huang, Jeerayut Chaijaruwanich, Phasit Charoenkwan, Hua-Chin Lee, and Shinn-Ying Ho. "Predicting protein crystallization using a simple scoring card method." In 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2013. http://dx.doi.org/10.1109/cibcb.2013.6595384.

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2

Yeh, Hui-Chung, Min-Li Yang, and Li-Chuen Lee. "An Empirical Study of Credit Scoring Model for Credit Card." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.138.

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3

Liu, Baichuan, Likun Lu, Qingtao Zeng, and Yeli Li. "Implementation of credit scoring card model based on logistic regression and lightgbm." In 2021 International Conference on Control Science and Electric Power Systems (CSEPS). IEEE, 2021. http://dx.doi.org/10.1109/cseps53726.2021.00042.

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4

Riyadi, Agung, and Hermansyah Hermansyah. "Card scoring as prognosis tool elderly quality of life in the city of Bengkulu." In Proceedings of the 1st International Conference on Inter-professional Health Collaboration (ICIHC 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icihc-18.2019.7.

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5

Li, Wei, and Jibiao Liao. "An Empirical Study on Credit Scoring Model for Credit Card by Using Data Mining Technology." In 2011 Seventh International Conference on Computational Intelligence and Security (CIS). IEEE, 2011. http://dx.doi.org/10.1109/cis.2011.283.

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6

Wang Qinghua, Xiong Xiaozhong, Tian Wenhao, and He Liang. "An early-warning model for supply chain risk based on the balanced scoring card and BP neural networks." In 2008 IEEE International Conference on Automation and Logistics (ICAL). IEEE, 2008. http://dx.doi.org/10.1109/ical.2008.4636296.

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7

Kochanczyk, Wojciech, and Vedang Chauhan. "Design of a Robotic Vehicle for ASME Student Design Competition 2021." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-72195.

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Анотація:
Abstract Robotics is a very active and diverse field with new applications found daily for emerging designs. This year ASME SDC emphasizes one such application with small RC robotic vehicles. These vehicles are primarily cargo carriers with the ability to harness wind and solar renewable energy sources to recharge their limited battery. Analog to modern electric battery-powered cars the robot utilizes a single AAA battery which was the biggest challenge of the competition. To satisfy all the requirements posed by the competition, a brand new vehicle platform was developed including both wind and solar charging capabilities. As part of the development process a new drive train, control system, wind turbine, and robot frame was developed. For performance maximization, all outsourced, as well as custom-designed components, were extensively researched and tested. Using test data and CAD software the final design was created including all the components selected during testing. This produced a successful prototype satisfying all competition requirements and being accepted to take part in SDC 2021. The maximum score achieved by the robot reached 0.4488 points using the given scoring matrix, however, it did not qualify for the final rounds of the competition. This project indicated several ideas that may improve the performance of small robots, such as the effective use of solar panels, the creation of systems with low power requirements, as well as the use of RC toys to develop more complex robotic vehicles.
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8

Cruz, Maria Nikki, Eliezer James Aguila, Maria Janeth Samson, and Jerald Gavin Lim. "Diagnostic accuracy of St. Luke’s – Lung Cancer Risk Prediction Scoring (SL - CaRPS), a novel tool in predicting malignancy among adult patients with CT – scan diagnosed pulmonary nodules." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.1607.

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Звіти організацій з теми "Scoring cards"

1

Agarwal, Sumit, Paige Skiba, and Jeremy Tobacman. Payday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles? Cambridge, MA: National Bureau of Economic Research, January 2009. http://dx.doi.org/10.3386/w14659.

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