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FAKO vs. FICO: will a free credit report provide an accurate credit score?


By law, U.S. consumers are allowed to obtain a free copy of their credit report once a year. The report does not include a credit score, and that forces consumers who are interested in knowing their score to purchase it, usually from one of the three major credit reporting agencies: Experian, Equifax, or TransUnion. Alternatively, several online credit score calculators offer free credit score range estimates utilizing user-provided data. These informative scores, commonly known as FAKO scores, are not to be confused with the official FICO scores that can only be obtained from a fee-based provider (McFadden, 2007).

Once a year, U.S. consumers have the right to receive a free annual credit report from each of the three major credit reporting agencies: Experian, Equifax, TransUnion. A recent study (Federal Trade Commission, 2012) by the Federal Trade Commission, the federal agency in charge of regulating trade credit in the U.S., has shown that approximately five percent of consumers had errors in their credit reports. The credit report is the result of the FACT Act of 2003; however, the provisions in the law do not allow consumers to obtain a free credit score. If consumers want to know their scores, usually known as a FICO score, they have to purchase it from one of the credit agencies, since this is considered proprietary information. Many lenders use Fair Isaac Corporation's FICO score as their credit granting criteria, as well, as to determine the interest rates they will charge borrowers. In addition, employers and insurance companies use credit scores to screen potential new hires and customers.

In an extensive study of 1,000 U.S. consumers, Smith et al. (2013) found that FICO scores, which credit bureaus use to determine credit worthiness, have an accuracy sufficient for lending and the management of creditor accounts. However, their findings suggest that the accuracy at the individual consumer level might be faulty and may contain costly errors for consumers. They recommended that individual consumers remain vigilant (by checking credit reports and scores) to ensure their credit bureau files do not contain errors that might incorrectly cause them to pay higher interest rates, or be denied credit.

There are some websites that provide a free credit score range estimate after one has answered eight to ten credit-related questions similar to those that credit agencies use to determine a credit score. These free score estimates are known as FAKO scores. Littwin (2013) attributes the coinage of the FAKO term to security expert, Evan Hendricks (For more information, refer to Leslie McFadden's (2007) The Dark Side of Credit Reports and Scores). FAKO score provider websites contain a disclaimer stating that the score range obtained should only be used for educational purposes. There are many companies that provide FAKO or estimated credit scores such as,,,, Equifax Credit Complete,, MSN Money Credit Calculator, to name a few.

In this study, researchers attempt to determine whether the credit score range provided by a free credit score calculator (MSN Money) is comparable to the actual credit score obtained from a fee-based service provided by Experian. If FAKO scores are within a reasonable range from FICO scores this would imply that consumers can estimate their own score without having to pay a fee or having to subscribe to relatively expensive credit monitoring services. This is the first paper that uses actual consumer credit scores to compare the performance of online credit score calculators, thus eliminating potential problems associated with self-reported credit information. The study continues as follows: section two reviews the literature; section three contains the methodology; section four includes the analysis of our results while section five concludes.


Many studies show the importance of having informed consumers, especially when they are shopping for financial services. Levinger, Benton, and Meier (2011) document that those who do not know their credit score are more likely to underestimate their credit worthiness and may be willing to pay higher interest rates for consumer loans. It is good practice for borrowers to review their credit reports at least once a year (Lusardi & Mitchell, 2007). Studies have shown that educated consumers are more likely to make better financial choices and eliminate behaviors that may lead to lower credit scores.

The main goal of credit scores is to assess the likelihood of default by the borrower, with lower scores associated with higher credit risk. The Fair Isaac Corporation (FICO) has developed a proprietary algorithm which is protected by trade secrets (Howat, 2009), to produce a credit score that is used by the three major credit reporting agencies: Experian, Equifax and TransUnion. Arya, Eckel, and Wichman (2011) speculate that the most important determinant of credit scores is the ratio of debt to available credit, with available credit oftentimes being linked to a person's income. This ratio is later adjusted by other factors such as payment history, previous bankruptcy or foreclosure, types of credit, and recent credit applications. FICO scores ranges from 300-850 points.

According to Courchane, Gailey, and Zorn (2008), credit scores can be categorized as follows:

300-580--Very Bad




721-850--Very Good

Anecdotal evidence from some financial bloggers (Berger, 2012) suggest that FAKO scores, those provided for educational companies not affiliated with Fair Isaac Corporation, are adequate estimates and can help consumers understand credit, their credit scores, and improve their credit record, thereby qualifying for lower interest rates on their loans. However, Smith and Gilbert (2011) note that during discussions to update the Federal Credit Reporting Act in 2010, some members of Congress manifested doubts about the benefits to consumers of educational or FAKO scores (For more information on comments made by Representative Jackie Speier (D-California) regarding FAKO scores, refer to Smith and Gilbert (2011) What Borrowers Need to Know About Credit Scoring Models and Credit Scores: Hearing Before the Subcommittee on Oversight & Investigations of the H. Commission on Financial Services). Some personal finance self-help book authors (Singh, 2012; Weston, 2013) also recommend obtaining credit scores directly from FICO using the site since, according to them, consumer educational scores can be off by 30 points of more. However, they do not provide any reference to studies substantiating these claims.

No previous studies were found that documented the accuracy of FAKO scores in comparison to actual FICO scores (not self-reported consumer data), so this may well be the first such study.


MSN Money provides a credit score range based on Experian's scoring methodology that study participants used to compare against the actual, fee-based credit score obtained from Experian. Participants recorded the summary of their findings on a survey. The purpose of this study was to determine whether use of a free credit report and credit score calculator would provide an accurate, acceptable proxy for the FICO credit score.

For this study, the authors surveyed 146 business college students enrolled at a mid-size university in the Southwest during the Spring semester of 2014. Participation was voluntary and survey results were anonymous. The authors instructed survey participants to obtain a copy of their free annual credit report from Experian at, to save a copy of the credit report, and to purchase a one-time credit score from Experian. This purchase was subsidized by a participation fee they received after completing the task. Subsequently, participants were asked to enter the information contained in the credit report into a free web-based credit score calculator ( and estimate their FAKO score. MSN Money provides a credit score range based on Experian's scoring methodology, which participants used to compare with their actual annual credit score they had obtained from Experian. Participants then recorded the summary of their findings in a survey which also included socio-demographic questions.

From the original sample of 146 respondents, five observations were removed because they did not have complete data, and another four were removed because they either did not provide a FAKO score range or the credit score range entered was outside the 300-850 possible range.


Table 1 describes the socio-demographic characteristics of the sample. The average actual credit score obtained after paying for this service is 707 points, which is a good credit score applying Courchane, Gailey and Zorn's (2008) classification. The average FAKO score from MSN Money credit score calculator was slightly over 728 points, and that is in line with some observers who indicate that FAKO scores tend to slightly overestimate actual credit score (Berger, 2012; Marquit, 2013). Females, which comprise 60.6% of the sample, had statistically significant lower average FICO scores than males (p-value = 0.010). This pattern, shown in Figure 1, is consistent across age groups for females whose credit score remains constant at around the 700 level through every age group, while males tend to have higher average scores as they age.

Younger cohorts and African Americans had lower average credit scores than older study participants and those from other races respectively, though the number of respondents who classified themselves as black in the sample is relatively low (n=9). Those with a large number of children under 10 years of age reported lower actual and lower FAKO scores than the other groups. Consistent with previous literature (Lusardi, 2008; Hung et al., 2009), those who have less formal education (some college) have lower credit scores than those with higher educational attainment, such as a graduate degree. This effect may be related to the age of the respondent as well since younger people in the sample are less likely to have completed college. Middle income respondents (those making between $50,000 and $100,000 a year) showed the highest average credit scores. It is important to note that, in this sample, lower scores for those with some college education and low income may be highly related since many young respondents are students attending college.

There was no noticeable difference between the credit scores of U.S. armed forces veterans and the scores of those with no military service. There also was no noticeable difference in credit scores between those who speak English at home versus those who speak a foreign language (primarily Spanish) at home. In the same note, marital status of the respondent (single -never married, married living with partner, divorced or widower) seems to have no effect in credit score. One of the survey questions asked who made most financial decisions in the household. Those who indicated that they consulted with a family member had lower average credit scores; however, this result seems more likely due to respondents' age rather than having received negative advice from a family member. Younger students tend to earn less since they may be working part time while attending college and may not be financially independent. These analyses, not presented here, are available upon request.


The study sought to explore whether credit scores varied depending on earnings or the number of dependents. It is important to note that credit scores are based on the borrower's ability and willingness to pay, and that income may be an indirect factor used in credit score estimation (Arya, Eckel, and Wichman, 2011). Figure 2 shows that those who earn less than $25,000 and have dependents in their care show lower average credit scores than those with higher earnings or with no dependents in their care. The lower credit score for high income participants with 3 or more dependents may reflect a relatively small sample size effect for this subgroup (n=6). These findings imply that low income participants may have to use more debt (perhaps from non-traditional lenders); moreover, especially considering that they are currently enrolled at a university, which may hurt their score.


Although the findings are presented using actual credit score figures, the results do not materially change when using FAKO scores in the analysis (results not shown here for brevity).

First the authors estimated the Pearson pairwise correlation to explore the association between the variables in the study. According to Table 2, Actual Credit Score and FAKO score are highly related as corroborated by the statistically significant, at the 1% level, correlation coefficient ([rho] = .76). As expected, Education, Income, and Age are significantly positively correlated.

In order to determine the accuracy of FAKO scores, the authors did a t-test of equality of means between actual credit scores and FAKO scores presented in Table 1. The two scores are statistically similar (p-value = 0.41). These results can be validated graphically by looking at Figure 3 which shows the scatterplot between actual credit scores and FAKO scores.


In 69% of the observations, FAKO scores overestimate the actual credit score. On average, FAKO scores are upwardly biased by about 21 points. This overestimation is consistent across the socio economic variables in this study but does not significantly change the credit score outcome.

As a robustness test, we calculated the logistic regression with overestimated FAKO scores (indicator variable = 1 if FAKO scores are larger than actual scores) as a dependent variable and a series of socio-demographic as explanatory variables. None of the independent variables had any effect in the overestimation (Results not shown here for brevity).

This study used a survey methodology to directly assess consumers' actual credit scores in a straightforward manner. The major implication of our findings is that consumers can create a good proxy of their actual credit score by first obtaining a copy of their free annual credit report and input that information in one of the free online FAKO score calculator websites. Midpoint FAKO scores will be, on average, within 21 points range from the actual credit score. This free 'homemade' credit score estimator can help consumers become better informed and serve as a benchmark to monitor their credit worthiness. Moreover, FAKO scores could be incorporated in financial literacy programs as a simple way of estimating one's credit score at no cost. Researchers found that the actual average FICO fee-based credit score obtained from Experian is not statistically different from the free FAKO score obtained from MSN Money website, which also uses a similar scoring methodology as Experian.


Our study focused only on one of the for-fee credit score providers, Experian, and on one free online FAKO score estimator that also uses Experian's credit scoring algorithm. Further research might explore the accuracy of other credit score calculators. The convenience sample used in this study consisted of college undergraduate and graduate students at a university. It would be interesting to see if these results also apply to more diverse populations.


Based on this study, after consumers have obtained their free annual credit report, free online credit score calculators (a.k.a. FAKO score calculators) provide a good approximation of the actual credit score. The average actual credit score obtained from Experian is not statistically different from the free FAKO score. The findings suggest that there seems to be little value added in the credit score service fees charged by major credit reporting agencies. Midpoint FAKO scores, which are provided for educational purposes, can serve as a proxy of the actual credit score. Using this simple method, consumers can increase their financial knowledge, eventually leading to better management of their personal finances.

The results suggest that FAKO scores provided by MSN Money are relatively close, on average, to participants' actual FICO scores. The mean FICO score is not significantly different from the mean FAKO score. This implies that there is limited value to consumers purchasing credit scores from one of the three credit reporting agencies. Instead, consumers can create a good estimate of their actual credit score by first obtaining their free annual credit report and entering that information in one of the websites providing a free credit score calculator.

This easily available credit score estimator can help consumers become better informed, and it might be convenient to incorporate this procedure in financial literacy programs as a simple way of estimating one's credit score at no cost.


Arya, S., Eckel, C., & Wichman, C. (2011). Anatomy of the credit score. Journal of Economic Behavior and Organization, 95(5), 175-185.

Berger, R. (2012). Are FAKO credit scores worthless? d9db-4f2c-b4d5-f3451b06e358 retrieved on September 8, 2014.

Courchane, M., Gailey, A., & Zorn, P. (2008). Consumer credit literacy: What price perception? Journal of Economics and Business, 60(1-2), 125-138.

Federal Trade Commission. Report to Congress Under Section 319 of the Fair and Accurate Credit Transactions Act of 2003. (December 2012). ( fifth-interim-federal-trade-commission/130211factareport.pdf) retrieved July 14, 2014.

Howat, J. (2009). Full utility credit reporting: Risks to low income consumers. National Consumer Law Center. Available at retrieved July 14, 2014.

Hung, A.A., Meijer, E., Mihaly, K., & Yoong, J. (2009). Building up, spending down: financial literacy, retirement savings management, and decumulation. Santa Monica, CA. RAND Corpopration, WR-712. Available at retrieved September 8, 2014.

Levinger, B., Benton, M., & Meier, S. (2011). The cost of not knowing the score: Self-estimated credit scores and financial outcomes. Journal of Family and Economic Issues. 32(4), 566-585.

Littwin, A. (2013). Escaping battered credit: A proposal for repairing credit reports damaged by domestic violence. University of Pennsylvania Law Review, 161(2), 363-429.

Lusardi, A. (2008). Household saving behavior: the role of financial literacy, information, and financial education programs. NBER Working Paper 1382.

Lusardi, A., & Mitchell, O. S. (2007). Financial literacy and retirement preparedness: Evidence and implications for financial education programs. Business Economics, 42(1), 34-44.

Marquit, M. (2013). What are FAKO credit scores and how do they stack up to FICO scores? September 8, 2014

McFadden, L. (2007) The dark side of credit reports and scores. available at retrieved July 14, 2014.

Singh, D. (2012). Finance 101: The whiz kid's perfect credit guide. Bloomington, IN: AuthorHouse.

Smith, A., & Gilbert, P. (2011). Fair Credit Reporting Act update--2010. The Business Lawyer, 66, 473-482.

Smith, D., Staten, M., Eyssell, T., Karig, M., Freeborn, B. A., & Golden, A. (2013). Accuracy of information maintained by US Credit Bureaus: Frequency of errors and effects on consumers' credit scores. The Journal of Consumer Affairs, Fall 2013, 588-601.

Weston, L. (2013). Liz Weston on personal finance, 2nd ed. Cambridge, MA: FT Press.

Pablo Calafiore

Josephine Sosa-Fey

Syed Harun

Texas A&M University-San Antonio

Pablo Calafiore is an Assistant Professor of Finance in the College of Business at Texas A&M University-San Antonio. He earned a PhD in Business Administration (Finance) and an MBA from the University of Texas-Pan American. His research interests are personal finance, corporate finance, international finance, and financial education. He has published in the Journal of Economic Education, Journal of Current Research in Global Business, Journal of Information Systems Technology and Planning, and the Handbook of Behavioral Finance. He has presented his research at various national and international conferences.

Syed M. Harun is an Associate Professor of Finance and Department Chair in the College of Business at Texas A&M University-San Antonio. Previously he held faculty and chair positions at the Department of Economics and Finance at Texas A&M University-Kingsville. His research interests are in financial literacy, monetary policy and financial markets and Institutions. He has published in Multinational Finance Journal, International Journal of Finance, Global Journal of Finance and Economics, International Journal of Business, Accounting, and Finance, and South-West Teaching and Learning Journal.

Josephine Sosa-Fey is Director of Graduate Studies and Research and a tenured Associate Professor in the College of Business at Texas A&M University-San Antonio. Her scholarly work on leadership, personal finance, cultural values, organizational behavior, and human resource management has been published in refereed journals including the International Journal of Business and Public Administration, International Journal of Education Research, Journal of Business and Leadership: Research, Practice, and Teaching, Southwest Teaching and Learning Journal, International Journal of Business and Economics Perspectives, International Journal of Business, Accounting, and Finance, and Journal of Studies in Conflict & Terrorism.
Table 1

Average Credit Score and FAKO Score
by Selected Socio-Demographic Characteristics

                                          Actual Credit Score

Variable                             N      Mean    Std. Dev.

Actual Credit Score                  137    707     72.5
FAKO Score                           137
   T-Statistic and (p-value)                        0.412 (0.340)
Female                               83     692     75.4
Male                                 54     729     62.5
   T-Statistic and (p-value)                2.605 (0.010)
Age 18 to 24                         21     687     81.9
Age 25 to 34                         70     711     68.7
Age 35 to 44                         31     701     75.8
Age 45 to 64                         15     726     60.1
Race White                           99     707     73.4
Race Black/African American          9      682     78.6
Race Other (incl. Hispanic)          29     713     68.3
Dependents: 0                        31     711     77.1
Dependents: 1                        55     697     78.0
Dependents: 2                        31     714     68.3
Dependents: 3 or more                20     718     55.3
Number of Kids under 10: 0           96     709     69.7
Number of Kids under 10: 1           24     707     73.3
Number of Kids under 10: 2 or more   17     693     88.6
Education: Some college              20     658     73.9
Education: Associate degree          53     696     80.8
Education: Bachelor degree           56     730     52.1
Education: Graduate degree           8      741     66.4
Income: less than $24,999            18     651     80.4
Income: $25,000 to $49,999           39     689     77.4
Income: $50,000 to $74,999           38     732     58.2
Income: $75,000 to $99,999           22     742     61.2
Income: more than $99,999            20     706     53.0

                                     FAKO Score

Variable                             Mean   Std.
Actual Credit Score
FAKO Score                           728    52.9
   T-Statistic and (p-value)
Female                               720    57.9
Male                                 743    40.9
   T-Statistic and (p-value)         1.545 (0.125)
Age 18 to 24                         709    64.3
Age 25 to 34                         736    45.4
Age 35 to 44                         723    54.4
Age 45 to 64                         731    61.3
Race White                           729    53.4
Race Black/African American          730    52.6
Race Other (incl. Hispanic)          727    53.0
Dependents: 0                        740    52.1
Dependents: 1                        723    49.8
Dependents: 2                        728    63.2
Dependents: 3 or more                729    45.4
Number of Kids under 10: 0           732    52.4
Number of Kids under 10: 1           725    48.9
Number of Kids under 10: 2 or more   712    60.5
Education: Some college              701    57.5
Education: Associate degree          718    61.0
Education: Bachelor degree           746    37.3
Education: Graduate degree           746    29.1
Income: less than $24,999            694    65.8
Income: $25,000 to $49,999           711    60.6
Income: $50,000 to $74,999           747    33.6
Income: $75,000 to $99,999           744    48.1
Income: more than $99,999            742    33.3

Table 2

Selected Correlations

             Actual    FAKO      Gender   Age
             Credit    Score

FAKO Score   .764 **
Gender       .243 **   .212 *
Age          .094      .055      .074
Race         .035      -.013     .091      -.126
Dependents   .055      -.054     .065      -.041
Education    .352 **   .315 **   .102      .083
Income       .283 **   .312 **   .098     .314 **

             Race      Dependents   Education

FAKO Score
Dependents    -.004
Education     -.016     -.130
Income        .025      .071         .307 **

**. Pearson Correlation is significant at the 0.01 level (2-tailed).

*. Pearson Correlation is significant at the 0.05 level (2-tailed).
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Author:Calafiore, Pablo; Sosa-Fey, Josephine; Harun, Syed
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Article Type:Report
Geographic Code:1USA
Date:Sep 22, 2014
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