Understanding Credit Scores Customer Tutorial This tutorial is intended to provide a high level understanding of how credit scores are developed, common terminology used, what credit file factors go into credit scores and the impact these factors could have on a score value. There is also discussion around how to read gains charts to help with setting score strategies.

Important Legal Note The information in this presentation is not to be relied upon, is not intended to be, nor should it be used or construed as, legal advice. Equifax assumes no liability for any errors or omissions in the information in this presentation. Compliance with the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA) or their respective regulations is the responsibility of each entity to which such laws apply. All specific consumer, customer and other third-party information in this presentation is fictitious. This presentation contains Equifax proprietary and confidential information. Do not distribute or copy. The information in this presentation is not to be relied upon, is not intended to be, nor should it be used or construed as, legal advice. Equifax assumes no liability for any errors or omissions in the information in this presentation. Compliance with the Fair Credit Reporting Act (FCRA), the Equal Credit Opportunity Act (ECOA) or their respective regulations is the responsibility of each entity to which such laws apply. All specific consumer, customer and other third party information in this presentation is fictitious. This presentation contains Equifax proprietary and confidential information. Do not distribute or copy.

What is Credit Scoring? The application of statistical methods to credit data with the intent of predicting the likelihood of some credit-related event taking place. Makes use of credit history information Developed using “analytically derived, demonstrably and statistically sound” statistical techniques A credit score does not tell how an individual will act. Rather, it tells the probability or likelihood that the individual will act a certain way. What is credit scoring? Credit scores utilize credit files and therefore credit history to generate a score. The score is based on empirically derived evidence using statistically sound techniques such as logistic regression. It is important to note that a credit score does not tell how an individual will act or behave, but tells the probability or likelihood that the individual will act or behave in a certain way.

Developing a Scoring Model Commonly used terms An Attribute (aka “Characteristic” or “Variable”) is an aspect of an individual’s credit history. Some examples might be “Age of Oldest Trade” or “Utilization Rate on Open Bankcard Trades”. A Bad Definition is what the model is developed to predict. A common bad definition for the Financial industry is “90+ Days Past Due”; common bad definitions for the Telecommunication industry are “No Pay”, “Involuntary Disconnect” or “60+ Days Past Due”. Before we discuss model development, let us cover some commonly used terms. An Attributes, often referred to as a characteristic or variable, is a particular aspect or piece of information related to a credit file. For example, Age of Oldest Trade or Utilization Rate on Open Bankcard Trades which is the percentage calculation of total balances on all open bankcards to total high credit on those open bankcard trades. The Bad Definition is what the credit model or score is developed to predict. The typical bad definition for the financial services industry is 90 days past due or worse, but can vary depending on the financial instrument or market such as prime verses sub-prime. For the telecommunication and utility industries commonly used bad definitions include No Pay, Involuntary Disconnect or 60 days past due or worse.

Developing a Scoring Model (continued) Commonly used terms A Performance Period is the time period for which the bad definition applies. Financial industry scorecards are designed to predict the likelihood of some event occurring over the next 12-24 months. Telecommunication industry scorecards have a performance window of 6-12 months. The Observation Point is the point from which the model development data was taken. It is the starting point of the Performance Period. A Bad Rate is the percentage of accounts that meet the “bad definition” within a certain score range. Typically, bad rates are quoted as “interval” or “cumulative” bad rates. Continuing with commonly used terms, the Performance Period or Window is the timeframe within which the model predicts the likelihood of the outcome of interest as defined by the bad definition. For the financial services industry the typical performance window is 24 months, but can be as short as 12 months depending on the market or product. Most standard generic risk scores for the financial services industry predict the likelihood of an account going 90 days past due or worse over the subsequent 24 months. The telecommunication and utility industries are interested in shorter performance periods typically 6 to 12 months. The performance period is based upon how often the behavior of interest or defined bad occurs within a given timeframe – if the greatest concentration of bads occur within 12 months then that will be the performance period used for model development. The Observation Point is the point in time that decisions are made; for example when applications for a new product are approved or declined. The observation point is also the start of the performance period and is the point in time from which we generated the credit file attributes to be used in the model development to create the score.

Utilizes historical information to predict future events and outcomes Model Development Utilizes historical information to predict future events and outcomes Historical Information Observation Point Prediction Time Frame Performance Window Independent Variables Dependent Variable Credit Attributes 90+ Days Past Due No Pay We will now move on to the model development. This slide depicts the data framework required to develop a credit model. Two additional terms are Independent Variables which in the case of credit models are the credit file attributes that will be used to predict an outcome and Dependent Variable which is the outcome or behavior the model is trying to predict. Credit files from the observation point are utilized and based on these credit files attributes are created. These attributes will look at a wide range of information from the past and present on the credit file. Using sound statistical techniques these attributes will then be analyzed to determine which ones help separate out those consumers who were good or made payments on their accounts from those that were bad by defaulting on payments. Predictive Model

Developing a Scoring Model General scoring model factors Payment History Has there been delinquency in the recent or historical past? Amount Owed What are the aggregate balances? How high is the credit utilization (balances as a percent of available credit)? Length of Credit History This is a proxy for stability – longer history equates to stability and often more credit information. Generally there are a range of typical factors or broad categories of types of attributes that come into credit based models. Payment History looks at how a consumer has been paying on accounts since the account opened. Has the consumer stayed current on payments or has there been delinquency in the recent or historical past? Amount Owed looks at how much debt does a consumer has and how much more debt could the consumer take on based on the products he or she currently has? How much of their available credit have they used or utilized? The Length of Credit History can be used as a proxy for stability. A longer credit history will often provide more information on the credit file which is good for better prediction of behavior.

Developing a Scoring Model (continued) General scoring model factors New Credit Has the consumer escalated their use of credit? Types of Credit in Use Does the consumer have a healthy mix of credit devices? Public Records Publicly available information related to bankruptcies, judgments and liens. New Credit or recently opened accounts tells us that the consumer has recently increased their debt or has the potential to do so. Types of Credit in Use tells us how well the consumer has maintained payments of different types of products. If the consumer only has installment loans, such as auto loans, it may not be the best predictor of how the consumer will behave with regards to revolving debt. However, if there is a good mix of financial products then there is more information on which to predict future behavior. Public Records are publicly available and items added to the credit file include bankruptcy filings, judgments and liens.

Developing a Scoring Model (continued) An example scorecard (for illustrative purposes only) Once the statistical analysis is complete and the analysis has determined which attributes help predict a behavior a scorecard is generated. This is an example of a simple scorecard. It is possible that a scoring model can contain multiple scorecards based on the segmentation of the development population. Population segments, and therefore scorecards, could be based on think and thin credit files or current on payments i.e. clean verses late payments i.e. dirty credit files. The scorecard provides details of how each attributes affects a consumers credit score. In this example, each consumer with sufficient information in their credit file starts with a score value of 500 and then points are added or subtracted depending on the value of the attributes in the model. If the consumer is less than or equal to 30% utilized on their revolving accounts then 41 points are added to the 500; however, if the revolving utilization is greater than or equal to 61% then 21 points are deducted. And so on through each one of the attributes in the model until a final score is generated. The final score value will predict the likelihood of a particular behavior the score was developed to predict. In general the higher the score the less risky the consumers and the lower the score the greater likelihood of making late payments or defaulting.

What Factors Affect a Score? Payment History A record of late payments on current and past credit accounts may lower the score. Public Records Matters of public record such as bankruptcies, judgments, and lien items may lower the score. Amount Owed Owing too much may lower the score, especially if the accounts are approaching the total credit limit. It is important to remember that there are multiple attributes in a scoring model which impact score values and so it is difficult to pin point any single change on a credit file that causes a change in the score value. This section explores factors in the credit file that generally impact risk scores. Payment History, that is how a consumer has and is paying on his or her financial products is a good predictor of how they may pay in the future. Late payments and defaults, especially in the recent past, may lower the credit score. As time passes, and as the consumer stays current on all payments, past late payments may have less negative impact on the credit score. Public Records such as bankruptcies, judgments and liens generally may lower your score as they are indicative of failure to make payments in the past. Amount Owed reflects how much debt a consumer has; this combined with credit limit or original balance is indicative of how much debt they are able to pay-off. The greater debt to credit limit or original balance, referred to as utilization rate for revolving debt, the lower the score is likely to be.

What Factors Affect a Score? (continued) Length of Credit History In general, a credit history that dates back for a longer period of time is better. New Accounts Opening multiple new accounts in a short period of time may lower the score. Generally, the longer a credit history on a consumer file the better the score. This relates to the fact that there is generally more information on the credit file to generate a more predictive score; a relatively young credit file in comparison may not contain much performance information and therefore is more difficult to predictive behavior and so could lead to a lower score. New accounts often reflect an increase in the amount of payments that a consumer needs to make and as these are recently opened accounts there is relatively little information to know how the consumer will pay these new obligations; also with the increase in payment amounts how will the consumer be able to take on more debt and make the payments? Recently opened accounts generally may lower the score.

What Factors Affect a Score? (continued) Inquiries Whenever someone else, i.e. a lender, gets a credit report an inquiry is recorded on that credit report. A large number of recent inquiries may lower the score. Open Accounts The presence of too many open accounts can lower the score, regardless of whether the accounts are being used or not. However, closing accounts will likely cause the utilization rate to go up which may lower the score. Inquiries indicate that a consumer is shopping for more credit and therefore could lead to more debt and the need to make payments on that debt. The greater number of recent inquiries may lead to a lower score. The final example, Open Accounts, really helps in explaining how generally no single factor on the credit file impacts a score by itself. The presence of too many open accounts is reflective of a consumer having to make payments on a large number of accounts and/or the potential to increase their debt in the case of revolving products, the combination of which may lead to a lower score. However, in the case of revolving debt, if the consumer decides to close a number of unused revolving accounts, that consumers debt to credit limit or utilization rate will go up which may lead to an even lower score.

Cumulative Population Performance Charts Accounts Eliminated Cumulative Population Cut off score A gains chart is a tool used to obtain an overall view of how a particular score performs on a specific population. Generally speaking, the population is rank ordered from the lowest risk (top) to the highest risk accounts (bottom), and is then separated into equal-sized groups. Gains charts can be used in a variety of ways and here is an example of information that a risk model gains chart can provide: Isolating high-risk accounts in the low level score ranges. When using risk models, this information is useful to clients who are looking to reduce their overall portfolio delinquency rate. By referencing the “Decum % of bads” column, the client is able to determine which score ranges capture the most amount of bad accounts in their portfolio. (The higher the number, the higher percentage of bads that are isolated at or below a particular score range.) This information can then be used to drive their acquisition strategy by allowing the client to determine which customers they wish to add to their existing customer base. Equifax returns the results of a credit score in the form of a gains chart, sometimes referred to as a performance or risk table. Using gains charts, a credit grantor can establish a “cut-off,” which determines the scores that fall within a range that provides acceptable behavior. The approval cut-off score is based on the level of behavior that is considered acceptable to the creditor. This performance chart is derived from the results found in a gains chart and helps to visualize the impact of a particular score cut. Above the score-cut, in this case 650, the creditor would accept all applicants. In this example, this would correspond to an approximate 50% approval rate. The next slide provides more details on the gains charts.

Gains Chart Explanation Min / Max Score: The minimum and maximum score ranges within that specific percentile. Total Accounts: The number of accounts present in the portfolio. Goods: The population of accounts the customer is targeting to keep (e.g., paid accounts). Bads: The population of accounts the customer is targeting to eliminate (e.g., non-payers or 90+DPD). This gains chart is color coordinated to reflect differing risk segments from low risk green to high risk red. The risk score is used to rank order the population and then place the population in score ranges. The score ranges can be based on fixed score ranges of interest, population size or manageable number of ranges. In this example, we have opted for the latter and created 10 scored population segments based on even population segments of just below 10% with the remaining population representing the unscorable segment. The chart shows that the lowest score consumers received in this population was 420, with the highest score being 830. There is a minimum and maximum score for each of the 10 ranges which is required for determining the appropriate score cut. In each score range, there are corresponding numbers that reflect the total population and the split between good accounts and bad accounts. Then to further help with setting score-cuts we provided interval bad rates and cumulative bad rates, but more importantly de-cumulative bad rates, number of bads and percent of bads. In this chart the area colored green reflects the low risk population. In theory based on this example, if the creditor set their score cut at 678 and accepted only applicants with a score equal to or above 678 they should expect a bad rate of only 4.34%; however, their approval rate would be less than 30%. An alternative way of looking at this is that if the creditor declined all applicants with a score of 686 or less then they would be eliminating 93.57% of the potential bad accounts as reflected in the Decum % of Bads column. The yellow colored area reflect medium risk applicants and the pink and red colored areas reflect more risky segments. By understanding the risk related to applicants appropriate risk based offers can be developed. Percent of Goods: A calculation that divides the summation of good accounts by the total good population. For example, 11.86% = 23,775 / 200,533. Decum % of Bads: A calculation that divides the summation of bad accounts by the total bad population. For example, 43.58% = 21,239 / 48,741. Note: Calculations for this number starts from the bottom score range and filters to the top. Interval Bad Rate: A calculation that divides the number of bads by the population within that interval. For example, in the upper most decile the interval bad rate of 1.16 = 280 / 24,055. Cumulative Bad Rate: A calculation that divides the summation of bad accounts by the summation of total of accounts. For example, 2.36 = (280+847) / (24,055 + 23,746).

Dual Score Matrix- Risk Strategy High Risk Medium Risk Low Risk To take the gains chart and risk strategy one step further, here we have added an additional score, in this case the Bankruptcy Navigator Index 3.0. We have maintained the same color scheme with green being the low risk segment and still corresponding to an approximate 4.34% bad rate overall, but now includes more accounts. By creating a dual matrix using 2 scores the creditor can further stratify the population and better understand risk associated with a given population. Compared to a single score strategy, a dual score strategy will allow the creditor to identify high risk segments within the single score low risk segment and vice versa thereby swapping in lower risk applicants and swapping out higher risk applicants. This will lead to increased approval rates while maintaining delinquencies levels or maintaining approval rates while reducing delinquency levels. A dual score matrix offers further risk segmentation. Risk based product offers can be set within each one of the risk segments based on combinations of the General Risk Score and Bankruptcy Navigator Index 3.0 score’s value.