CREDIT SCORING Sample Clauses

CREDIT SCORING. Your new limit will be awarded at the sole discretion of NCBA, as determined by your credit score. Your credit score will be assessed based on various factors including, but not limited to, your use of M- Pesa, the Services and your repayment history.

CREDIT SCORING. 10.1 You acknowledge and agree that details of your name, address and payment record may be submitted to a credit reference agency, and data in relation to you will be processed by and on behalf of us in connection with the site-works to help us to make decisions about your ability to pay for the site-works and supply of gas and/or electricity to your site(s). If you want to see what information the credit reference agencies hold about you, please contact them directly.

CREDIT SCORING. In example 1 we analyze a dataset containing information on 1000 consumers’ credits from a South German bank with a generalized additive model. The aim is to predict the probability that a client with certain covariates or risk factors will not pay back his credit. Therefore the response variable is the binary variable creditability (named y in the dataset) with y = 0 for creditworthy clients and y = 1 for not creditworthy clients. As covariates we have two continuous variables and 5 categorial variables (in effect coding) which are described in Table 1. Variable Description account1 running account of the client with categories ”no running account” (account1=1), account2 ”good running account” (account2 = 1) and ”medium running account” (account1 = account2 = −1) payment payment of previous credits with categories ”good” (= 1) and ”bad” (= −1) intuse intended use with categories ”private” (= 1) and ”professional” (= −1) marstat marital status with categories ”married” (= 1) and ”living alone” (= −1) To analyze the data, we first define ggamm in the current session and store the data in a dataframe- object. > source("c:\\ggamm\\functions\\helpfunctions.r") > source("c:\\ggamm\\functions\\ggamm.r") > creditdata<-read.table("c:\\ggamm\\examples\\credit.raw",header=T) To make the names of the variables available directly, we attach the dataframe: > attach(creditdata) Now we combine the covariates that are to be modelled as P-splines and the categorial covariates in two different matrices: > smoothcovs<-cbind(duration,amount) > catcovs<-cbind(account1,account2,payment,intuse,marstat) Finally we call ggamm and store the estimation results in the object credit: > credit<-ggamm(dep=y,fix=catcovs,smooth=smoothcovs,family="binomial") As a result of this call, the following information is given on the screen: iteration: 1 relative changes in the regression coefficients: Inf relative changes in the variance parameters: 0.7858781 . iteration: 11 relative changes in the regression coefficients: 2e-006 relative changes in the variance parameters: 5.8e-006 variance of smooth1: 0.0045697637 variance of smooth2: 0.0156629498 beta0: -0.25677 beta1: -1.09241 beta2: 0.86104 beta3: -0.49619 beta4: -0.21911 beta5: -0.25864 In each iteration of the estimation process the relative changes in the parameters are computed and compared with the (possibly user-specified) value of eps. When the estimation process has converged some estimation results are given. Namely, these are the es...

CREDIT SCORING. 15.1 The Dealer’s Facility Limit and Momentum’s continued approval of the Dealer’s Requests will be determined by the Dealer’s credit score or by any other means as Momentum deems fit from time to time at its sole discretion. 15.2 The Dealer’s credit score will be assessed based on various metrics including the Dealer’s CRB records, bank and Mpesa Statements, the Dealer’s credit score and repayment history on the Dealer’s existing Facility Limit.

CREDIT SCORING. Your Kamilisha Limit and our continued approval of your Requests for a Facility will be determined by your credit score. Your credit score will be assessed based on various matters including the information obtained from your use of Airtel Services and the Airtel Money Service and repayment history on your existing Kamilisha Limit. Your Kamilisha Limit and our continued approval of your Requests for a Facility will be determined by your credit score. Your credit score will be assessed based on various parameters including the information obtained from your use of Airtel phone Services, Airtel Money Service, the Kamilisha loan repayment history and that of other lenders through the Credit Reference bureaus.

CREDIT SCORING. 12.1 The Customer’s Import Financing Facility Limit will be determined by the Customer’s credit score as determined by Momentum or by any other means as Momentum deems fit from time to time at its sole discretion.

CREDIT SCORING. 9.1 The Customer’s Ezua Chapaa facility Limit and Momentum’s continued approval of the Customer’s Requests for access to the Ezua Chapaa facility funds will be determined by the Customer’s credit score or by any other means as Momentum deems fit from time to time at its sole discretion. 9.2 The Customer’s credit score will be assessed based on various metrics including the Customer’s Mpesa Statement, the Customer’s repayment history on the Customer’s existing Ezua Chapaa facility Limit and other facilities.

CREDIT SCORING. 5.2.1. Credit Scoring and provision of M-Pesa Merchant Loan Limit will be based on a score provided by the Bank Credit Scoring Engine embedded in the Loan Management Platform. 5.2.2. The Loan Limit is subject to review from time to time and we reserve the right to vary your Loan Limit without notice; we shall try to notify you of any variation to the Loan Limit when deemed necessary via SMS upon request.

CREDIT SCORING. Generally, the following provisions relating to credit scoring will be followed by BNB USA. These provisions, however, may be modified by BNB USA over the term of the Agreement. (A) BNB USA shall obtain credit reports in connection with all applications passing the Operational Guidelines. Three major credit bureaus will be used with each zip code being assigned a primary, secondary and emergency back-up credit bureau. (B) All applications are credit scored using BNB USA's in-house scoring model. BNB USA's scoring model, referred to as CAP, is comprised of 12 scoreable characteristics, eight of which are based on information from the application and four of which are based upon information from the credit report. CAP cutoffs will be customized for Merchant and will differ based upon the age of the applicant. (C) BNB USA shall utilize all three of the credit bureaus' bankruptcy score models or other risk scoring models. BNB USA's risk scoring model is referred to as FICO. FICO score cutoffs will be customized for Merchant and will vary based upon the applicant's age, depth of credit file, type of residence, and/or other determining criteria.