Consumer complaint received 2021-09-03
Company: EQUIFAX, INC.
State: CT
Product: Credit reporting, credit repair services, or other personal consumer reports
Sub-product: Credit reporting
Issue: Problem with a credit reporting company's investigation into an existing problem
Sub-issue: Their investigation did not fix an error on your report

Complaint:
I am providing additional information asked for in complaint XXXXXXXX which was closed as I was writing this today. I had reported the company XXXX in error. The correct company is : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX CT XXXX Phone XXXX XXXX Both the Equifax and XXXX  responses to my complaint XXXX-XXXX  were non-responsive. Since I have been significantly damaged by their actions, to wit a score drop of XXXX XXXX in 1 month after successfully refinancing my home for {$570000.00} I am now unable to secure a HELOC loan to do improvements on my home at a reasonable interest rate. Even if I try now, my credit score will continue to be adversely affected due to the woefully questionable credit models used by the two of the three credit bureaus ~ XXXX  and Equifax. 
I have filed a small claims action in XXXX XXXX XXXX seeking damages and/or referral to NY State Attorney General against Equifax. The case Index XXXX XXXX XXXX The Court date is to be determined due to COVID-19. 

I have now decided to hire an attorney to represent me against XXXX, Equifax and XXXX XXXX XXXX XXXX and possibly principals in a single action. I will be filing a complaint with the Connecticut State Attorney General against all firms shortly as well. I am seeking damages and an order for the defendants to divulge the exact statistical method ( s ), factor ( s ), parameter estimate ( s ), confidence interval ( s ), false negative/positive rates, omitted variable analysis, point estimate error ( s ) and other such diagnostic data as to ascertain the efficacy of their credit scoring business and the harm it can inflict on individuals wrongfully denied credit. This can be done without the need for proprietary information. 

I have been engaged in and published related research for the XXXX XXXX  XXXX XXXX before these companies existed. For example, I published : XXXX XXXX XXXX XXXX XXXX XXXX XXXX. Interim Report for Period XX/XX/XXXX through XX/XX/XXXX. 
XXXX, XXXX XXXX ; And Others This report refines and improves upon a conceptual model and a mathematical procedure, based upon a blend of Likelihood Function Estimation ( LIFE ) and utility theory. Empirical studies conducted by the XXXX XXXX XXXX XXXX XXXX in XXXX and XXXX have shown that the LIFE procedure can be very useful in the prediction and study of dichotomous behavior, e.g., predicting attrition/success of a trainee in an XXXX XXXX Training program. The technique has been previously used to study attrition from the United States XXXX XXXX XXXX and Basic Military training. By generalizing the LIFE technique it can be applied to the situation of studying and predicting any dichotomous dependent variable. Currently, the XXXX XXXX XXXX XXXX XXXX is continuing studies of the procedure to compare its usefulness to other mathematical methods. ( Author ) Descriptors : Mathematical Models, Military Personnel, Military Training, Persistence, Prediction, Predictor Variables, Research Reports XXXX of Documents, U.S. Government XXXX XXXX, XXXX, D.C. XXXX ( Stock Number XXXX ) Publication Type : Reports - Research Education Level : N/A Audience : N/A Language : English Sponsor : N/A Authoring Institution : XXXX XXXX XXXX XXXX XXXX, XXXX XXXX , TX . XXXX and XXXX XXXX XXXX. 

The United States Chief of Staff Gen. XXXX, at the time, and the United States XXXX XXXX Judge Advocate General, in Washington did not allow us to use the computer model to take adverse action i.e. deny enlistment, against any candidate that passed the enlistment standards in effect in XXXX. The false positive rate was 4.7 %. Certain variables like zip codes were discriminatory and had to be removed. Only those who failed to pass were allowed to be evaluated by the computer model we developed. Those who did pass the computer screen did exceptionally well to the point the program was classified in XXXX. Recall there were no such things as widely used consumer credit scores in XXXX. 
There are millions of people being wrongfully denied credit and I INTEND TO CORRECT THAT. 

Thank you, XXXX XXXX XXXX

Company response to consumer: Closed with explanation