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Disparity is a measure that shows the odds a group of applicants face when compared to another group, based on a specific outcome. For it to be meaningful, care must be taken to measure disparity within applicable parameters. In the lender-oriented example below, two age groups are compared based on the decision status “Originated” as defined within the HMDA Schema.
| GRP | Applicant Age Group | Population | Originated | Origination Ratio | Disparity |
|---|---|---|---|---|---|
|
GRP 1 |
25-34 |
123,961 |
47,792 |
47792/123961 = 38.55% |
16.44% |
|
GRP 2 |
45-54 |
211,699 |
97,688 |
97688/211699 = 46.14% |
0% |
The presence of disparity does not imply a shortcoming or defect in the decision-making process or model. It is simply one of the measures for consistency of decisions. Understanding how much and where disparity exists in the data produces actionable intelligence for process enhancements.
The Fair Lending Rules forbid the use of prohibited factors in decision-making. FairBorrow™.Ai leverages only such factors that are permissible for credit decision-making in its analysis. Such factors include income, property value, type of loan, type of product, etc.
A “decision anomaly” is an instance where two similar loans receive different outcomes, such as one being originated, and the other being denied. The presence of a “decision anomaly” does not imply a shortcoming or defect in the decision-making process or model and is simply derived from the presence of a disparity in decisions. The proprietary Data Management Processes at FairBorrow™.Ai can identify decision anomalies from various angles to enable a risk manager to quickly identify potential actions for process enhancement.
Fair Lending-prohibited factors are used as Disparity Factors by FairBorrow™.Ai. These include Age, Gender, Race, Ethnicity, etc. Disparity of decisions is measured within each of these disparity factors.