Regulators use statistical analysis to find Fair Lending violations. Use the same tools they do—before they arrive. Comply Fair Lending runs regression analysis, BISG proxy testing, and risk scoring using FFIEC and CFPB examination procedures, helping you identify and remediate pricing disparities, underwriting patterns, and redlining risk proactively while demonstrating your commitment to fair lending compliance.
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Comply Fair Lending helps you identify disparities before they become enforcement actions. Run the same statistical tests examiners use—regression analysis for pricing, BISG proxy methods for demographics, comparative file review for underwriting—and address issues proactively. Built on FFIEC and CFPB examination procedures, Comply provides exam-ready documentation that demonstrates your institution's commitment to fair lending.
Last updated: January 14, 2026
Regulators use statistical analysis to find fair lending violations. Use the same tools they do—before they arrive. Comply Fair Lending runs regression analysis, BISG proxy testing, and risk scoring using FFIEC examination procedures, helping you identify and remediate issues proactively while demonstrating your commitment to fair lending compliance.
Spot potential discrimination patterns instantly with comprehensive risk scorecards covering all 12 FFIEC fair lending risk factors. Click any cell to drill directly into application-level data—no closing reports or rebuilding filters. Create custom scorecards for your institution's specific risk areas or use our examiner-tested templates. Interactive scorecards cut fair lending analysis time from weeks to hours, letting you investigate anomalies immediately while examiners wait days for manual reports.
Users can add new fields, parametric or nonparametric comparisons, and report groups; apply conditional formatting; and specify display options.
Perform logistic regression for underwriting and linear regression for pricing—the exact methodologies CFPB and federal examiners apply during fair lending examinations. Test for unexplained disparities in approval rates, interest rates, and fees while controlling for legitimate underwriting factors like credit score, LTV, and DTI. Identify statistically significant disparities before regulators do, then target your remediation efforts on applications with the highest risk residuals.
Field values can be automatically transformed, error-checked, and dummy-coded on the fly. This is something that is particularly cumbersome with other commercial Fair Lending packages, since it is typically a manual process (i.e. before processing the model, a user has to manually update field values). Optionally, the post-processed variable values can be exported for analysis in an external package.
In addition, dummy-coded variables can be created automatically by the system, based on breakdowns of alphabetic, numeric, or date values.
The regression module has a robust set of consistency verification tools to help identify relationships between variables.
The loan-application-level output statistics can be analyzed like any other field value in Comply. This includes filtering and sorting, cells for scorecards, and selection criteria for file reviews. Automation can be used to group applications by these output statistics (e.g. set up a rule to automatically group applications where the standardized residual value is +/- 3). No other Fair Lending package allows this level of analysis.
BISG Proxy Analysis: Analyze All Borrowers, Not Just Self-Reported Demographics: Over 40% of applicants don't provide race or ethnicity data. Comply uses CFPB-approved BISG (Bayesian Improved Surname Geocoding) methodology to estimate demographics for non-reporting borrowers, giving you complete fair lending coverage. Run comprehensive fair lending analysis on your entire portfolio—including applications where borrowers didn't self-report—using the same proxy methods regulators apply during examinations.
Reports and scorecards show the potential for risk at an aggregate level, but at the lowest level an analyst should be able to quickly and easily compare similar loan applications across groups. The criteria by which applications in a comparative file review are partitioned into groups, matched for similarity, and reduced to a pool of eligible application pairs must allow for ultimate flexibility.
Once an application pair pool is created, Comply allows the user to document each pair at every level of analysis by providing notes at the file review level, the application pair level, the application level, and even down to the individual field comparison level.
Along with Comply's native statistical analysis tools, users have access to total integration with any external analysis package. Create user fields to represent additional values, automatically export data, run external commands, and reimport the results with a single click.
These fields, generated from an external package, can then be used just like any other field value in Comply. This includes creating scorecards and file reviews.
Synchronization: Users can check out subsets of data from the primary server in order to work offline. The offline data can then be checked back into the server after the offline work has been completed.
Filtering and sorting: Access to Comply's application data is facilitated by a robust set of filtering, sorting, and sampling functionality. These filters can be designed on the fly, saved, and loaded with any set of application data. Filtered or individually selected applications can be saved as flexible application groups, which can then be used as data sources for any analysis � automated or interactive.
In addition to the filter designer, the Field Explorer allows users quick and easy access to filtered sets of application data by breaking the parent filter down by any field values in Comply.
The field explorer can also be used for other types of exploratory statistical analysis.
Intuitive interface: Comply's user interface was designed to keep the look and feel of other commonly used applications (e.g. the Microsoft Office suite). The context-based ribbon design makes tasks available to the user as needed to reduce clutter and confusion.
Applications can be viewed and edited in a data-entry form view or in a list-style spreadsheet view. The view is customizable by field list templates.
Design once: All of the views and processes in Comply can be designed using templates (called "Definitions" in the software), which can then be used throughout the system. In addition to the standard processes (e.g. importing and exporting data), this includes Fair Lending analysis automation, user-defined scorecards, regression models, and file reviews.
Auto-Pilot: Comply's Auto-Pilot can be used for offline and online automation. Create jobs to execute repetitive tasks, schedule the jobs to run offline (as an optional, separately installed service on the server or client), or run the jobs manually from within Comply.
Analysis automation: When creating a new analysis workspace, or at any point during analysis, the Analysis Automation Wizard can be used to filter and group application data, identify outliers, set field values, and create new elements (e.g. scorecards, regression models, file reviews).
The steps used in analysis automation can be defined in templates or created on the fly. Users specify conditions and actions for each step, and when the process is executed, Comply evaluates the step properties and applies any relevant action.
Processing: The modular design of Comply allows users to run processes individually or in batch mode. This includes edit checks, geocoding, setting field values, etc.
Other commercial Fair Lending software packages have a single "update" process with no user control. This "update" process also copies all of the non-relational second-tier information (e.g. demographics) to the individual loan application.
Geocoding: Comply's integrated batch and single-application geocoding is the most accurate compliance-grade geocoder available. In addition, users have access to the ZOOM interactive geocoder, which allows you to view a map of results for several geocoding data sources.
Comply's server is built on a single Microsoft SQL Server database. The workstation software (and optional add-on services and software, e.g. Auto-Pilot) are built using the latest Microsoft .NET technology. Enterprise functionality forms the backbone of the Comply framework, including user- and group-level security, offline database synchronization, extensive offline and online automation, and integration with Windows and/or SQL authentication.
Comply's database is fully relational. Integrity between tables is enforced by local and foreign key constraints. Other commercial Fair Lending software adds geographic and demographic information to each individual application; this is necessary in their case because each dataset is its own database.
Organization: Within the hierarchy of datasets in Comply, users can create folders to better organize and secure their data. Workspaces are created to contain and organize all of the elements involved in any statistical analysis (regression models, file reviews, scorecards, application groups, etc.).
Flexibility: Comply allows an unlimited number of user-defined fields of any data type. These fields can be used anywhere in the system: as filtering and sorting fields, variables in a regression model, cells and statistics tests on a scorecard, etc. Individual user fields can be shown or hidden for specific datasets.
Multitasking: Comply uses a tab-based interface, which allows users to keep multiple sets of data, reports, or other tasks open at the same time. Each tab maintains a history of its filtered set of data, so navigating between tasks and within the history of a given tab is as easy as clicking.
There are far too many additional features to mention here, all of which stand out from our competition in flexibility and ease of use. For example:
All of the tools that you need to analyze your application data, identify lending patterns and assess risk using the simple and familiar Comply interface.
Applications groups, Data Mines, File Reviews, and Regression Models are all conveniently packaged together and accessed through a common dashboard. Organize applications and analysis elements into logical groups. Define focal points at an arbitrary level of granularity and then create reports or risk scorecards, access interactive charts, generate regression prediction sets and much more.
Browse loan information interactively at a summary level. Drill down to focal points. Create Data Mines, File Reviews, Reports, Application Groups, and Regression Models from selected collections of applications.
Identify missing or invalid data. Focus on applications with incomplete information or data entry errors. Update or remove outlying data from your analysis workspace. Use automated tools to help you find and correct anomalies.
Compare fields and identify trends or data integrity issues. Use interactive charts to filter or manipulate data graphically.
Create regression models using any set of applications in Comply. Quickly assess model and parameter quality, analyze variance and variable significance. Perform cross-validation to identify weaknesses in training or testing sets.
Create model variables using any field information in Comply. Transform numeric values to correct distribution issues, dummy-code character data on the fly, or create contrasts to test for variable significance.
Create risk scorecards to find areas that need attention. Use regression models to generate reports that will help you identify disparities in predicted pricing. Print summary information or save and post it on an internal website for executives or employees. Compare analysis reports between periods of time, assessment areas, underwriters, brokers, or any other groups of applications.
Create sets of files for matched-pair analysis. Create file reviews from any set of applications or analysis elements. Create executive summary or detailed field analysis reports. Print matched file pairs that need further attention. Group and annotate file pairs for which no risk was identified.
FFIEC Interagency Fair Lending Examination Procedures
This overview provides a basic and abbreviated discussion of federal fair lending laws and regulations. It is adapted from the Interagency Policy Statement on Fair Lending issued in March 1994.
http://www.ffiec.gov/pdf/fairlend.pdf
OCC Comptroller's Handbook (Fair Lending)
Examiners use these procedures to evaluate a national bank's compliance with the Fair Housing Act (FH Act), Equal Credit Opportunity Act (ECOA), and the Federal Reserve Board's Regulation B. This booklet contains the Federal Financial Institutions Examination Council's (FFIEC) "Interagency Fair Lending Examination Procedures," and appropriate OCC supplemental material.
http://www.occ.treas.gov/publications/publications-by-type/comptrollers-handbook/fairlend.pdf
http://www.chicagofed.org/digital_assets/others/region/community_reinvestment/bankers_guide_to_risk_based_fair_lending.pdf
GAO Data Limitations and the Fragmented U.S. Financial Regulatory Structure Challenge Federal Oversight and Enforcement Efforts
GAO analyzed fair lending laws, relevant research, and interviewed agency officials, lenders, and consumer groups. GAO also reviewed 152 depository institution fair lending examination files. Depending upon file availability by regulator, GAO reviewed all relevant files or a random sample as appropriate.
http://www.gao.gov/new.items/d09704.pdf
Anatomy Of A Fair-Lending Exam: The Uses And Limitations Of Statistics
In this paper, we consider the role of statistical analysis in fair-lending compliance examinations. We present a case study of an actual fair-lending examination of a large mortgage lender, demonstrating how statistical techniques can be a valuable tool in focusing examiner efforts to either uncover illegal discrimination or exonerate an institution so accused.
http://www.federalreserve.gov/pubs/feds/2000/200015/200015pap.pdf
How Low Can You Go? An Optimal Sampling Strategy for Fair Lending Exams
Empirical researchers face a tradeoff between the lower resource costs associated with smaller samples and the increased confidence in the results gained from larger samples. Choice of sampling strategy is one tool researchers can use to reduce costs yet still attain desired confidence levels.
http://www.occ.treas.gov/publications/publications-by-type/economics-working-papers/2008-2000/wp2001-3.pdf
CRA And Fair Lending Regulations: Resulting Trends In Mortgage Lending
In this article, we examine the evolution of the fair lending regulations and the CRA. We then summarize the economic literature that pertains to these regulations.
http://www.chicagofed.org/digital_assets/publications/economic_perspectives/1996/epnd96b.pdf
FDIC Side by Side: A Guide to Fair Lending
In this guide, which we first distributed in August of 1994, we provide alternative means that an institution may use to discover uneven customer service or inconsistent lending practices that may be discriminatory. This guide is not about finding discrimination, that is, violations of the fair lending laws. It's about tools that a lender can use to compare the treatment of loan applicants, identify differences and correct potential problems.
http://www.fdic.gov/regulations/resources/side/side.pdf
BISG (Bayesian Improved Surname Geocoding) is a proxy method to estimate borrower race and ethnicity when not self-reported. Comply Fair Lending includes BISG analysis using CFPB-approved surname and geocoding data to support comprehensive fair lending reviews and ECOA compliance testing.
Comply Fair Lending provides logistic regression for underwriting analysis, linear regression for pricing analysis, comparative file review (matched-pair analysis), and risk scorecard generation. All methods follow FFIEC fair lending examination procedures and include built-in validation tools to verify model quality and statistical significance.
Risk scorecards aggregate statistical results to identify high-risk areas requiring file review. Comply calculates disparities between control and protected class groups across underwriting decisions, pricing, and terms. Scorecards prioritize areas by statistical significance and practical impact, helping focus limited review resources on applications most likely to reveal fair lending concerns.
Yes, Comply generates exam-ready reports following FFIEC procedures. Run fair lending analysis quarterly or annually to identify and remediate risks before examiners arrive. The software produces documentation examiners expect: regression model summaries, comparative file reviews, scorecard results, and corrective action tracking—demonstrating your proactive fair lending monitoring program.
See how software-driven analysis reduces risk and saves time
| Analysis Task | With Comply Fair Lending | Manual Analysis |
|---|---|---|
| BISG Proxy Analysis | Automated Bayesian Improved Surname Geocoding with Census Bureau surname list and tract demographics. Complete proxy analysis in minutes. | Manual surname lookup in Census tables, manual geocoding, complex spreadsheet calculations. Hours of work, high error risk. |
| Regression Analysis | Built-in logistic regression models for pricing and credit decisions. Automatic control variable inclusion, statistical significance testing, and results interpretation. | Requires statistical software expertise (SAS, STATA, R). Manual data preparation, model specification errors common, difficult to replicate. |
| Comparative File Review | Automated matched-pair selection based on your criteria. Side-by-side comparison reports with highlighted disparities and risk scoring. | Manual file selection prone to bias. Time-consuming individual file reviews. Difficult to document methodology and ensure consistency. |
| Pricing Analysis | Scatter plots, pricing disparity reports, and statistical tests for prohibited basis pricing differences. Visual identification of outliers and patterns. | Manual Excel charts, limited statistical testing capability. Difficult to control for legitimate pricing factors. Results may not withstand examiner scrutiny. |
| Risk Scoring | 12-factor risk scorecard with weighted assessment. Quantifies fair lending risk across multiple dimensions with actionable remediation priorities. | Subjective risk assessment. Inconsistent evaluation criteria. Difficult to track risk changes over time or justify to examiners. |
| Documentation | Exam-ready reports following FFIEC procedures. Complete audit trail, methodology documentation, and statistical validity evidence. | Manual report creation. Inconsistent documentation. Examiners question methodology. Difficult to demonstrate systematic monitoring. |
| Time to Complete Analysis | Hours for comprehensive fair lending review | Days or weeks for partial analysis |
| Regulatory Updates | Automatic updates to BISG surname list, FFIEC procedures, and regulatory guidance. No additional work required. | Manual research and implementation of methodology changes. Risk of using outdated procedures or data sources. |
Trusted by financial institutions nationwide for compliance excellence
Mark Mitchell
Panhandle State Bank
"The support has been exceptional. The program is very easy to use. RATA has been one of the best companies I have dealt with in my 25 years of banking. Their service has exceeded my expectations."
August 23, 2016
Paul D'Agostino
TruHome Solutions
"I wanted to let you know that RATA and Comply is the absolute best!!! My submission is complete and was accomplished in record time. I more than doubled the amount of activity I reported last year and it took me less than half the time. Thank you for being there for us and the industry! You are and always will be number one to me!!!"
May 20, 2015
Judy A. Edwards
Greer State Bank
"Would highly recommend both the product and the personnel!! Being able to accomplish what I did in such a short period of time is a testimonial to the product's user friendliness as well as the support I received from the great folks at RATA. Could not have done it without them!!!"
June 11, 2014
See how Comply Fair Lending uses FFIEC-based regression analysis and risk scoring to identify and address potential fair lending issues before they become problems.
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