# Which large banks use AI for fair-lending testing and explainability?

Source: https://www.bankingnewsai.com/banks/leaders/ai-fair-lending
Last updated: Oct 30, 2026


Checked 2026-10-28 · bank profiles. Nothing on this page is written by a model: every row is computed from that dataset.

On the public record as of Oct 28, 2026, 8 of the 141 large banks profiled describe at least one sourced AI use case for fair-lending testing and explainability on their profile. Ranked by use cases (in production), the top 5 are Renasant (1 use case), Ameris (1 use case), Société Générale (1 use case), City National (1 use case) and Banco Santander (1 use case). Renasant's strongest evidence: Standing fair lending compliance committee: The proxy describes a standing committee that includes the chief risk officer and chief compliance officer and reports….

## Which of the large banks use AI for fair-lending testing and explainability?

| # | Bank | Use cases (in production) | Strongest use case | Status | Date | On this site | Sources |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | [Renasant](https://www.bankingnewsai.com/banks/renasant) | 1 | Standing fair lending compliance committee: The proxy describes a standing committee that includes the chief risk officer and chief compliance officer and reports… | In production | Mar 18, 2026 | [Renasant profile](https://www.bankingnewsai.com/banks/renasant#where) | [Renasant Corporation (SEC filings)](https://www.sec.gov/Archives/edgar/data/715072/000071507226000030/rnst-20260316.htm); [Renasant Corporation (SEC filings)](https://www.sec.gov/Archives/edgar/data/715072/000071507226000017/rnst-20251231.htm) |
| 2 | [Ameris](https://www.bankingnewsai.com/banks/ameris) | 1 | AI exposure under fair-lending and equal-opportunity law: The 10-K lists equal opportunity and fair lending laws among those applying to AI and warns of unwanted bias | In production | Feb 26, 2026 | [Ameris profile](https://www.bankingnewsai.com/banks/ameris#where) | [Ameris Bancorp (SEC filings)](https://www.sec.gov/Archives/edgar/data/351569/000035156926000050/abcb-20251231.htm) |
| 3 | [Société Générale](https://www.bankingnewsai.com/banks/societe-generale) | 1 | Active Shell financial-fragility early warning: An XGBoost scoring model using over 250 variables and 70 data pipelines assesses about 10 million retail clients each… | In production | Feb 1, 2025 | [Société Générale profile](https://www.bankingnewsai.com/banks/societe-generale#where) | [Societe Generale](https://societegenerale.com/sites/default/files/documents/2024-09/applying-data-ia-flagship-use-cases.pdf) |
| 4 | [City National](https://www.bankingnewsai.com/banks/city-national) | 1 | Fair-lending programme and ML fairness testing: The consent order requires a fair-lending risk-management programme | Announced | Oct 13, 2026 | [City National profile](https://www.bankingnewsai.com/banks/city-national#where) | [OCC](https://occ.gov/static/enforcement-actions/eaAA-ENF-2024-08.pdf); [City National Bank](https://www.cnb.com/about-us/leadership/marc-fisher.html) |
| 5 | [Banco Santander](https://www.bankingnewsai.com/banks/santander) | 1 | Discrimination testing with counterfactual comparators (mutatis-mutandis): Open-source research code for situation testing of algorithmic discrimination, published with a paper on revisiting… | Announced | Jun 25, 2026 | [Banco Santander profile](https://www.bankingnewsai.com/banks/santander#where) | [Banco Santander](https://www.santander.com/en/stories/santander-publishes-ai-projects-under-an-open-source-licence-to-ramp-up-shared-innovation) |
| 6 | [Dime](https://www.bankingnewsai.com/banks/dime) | 1 | Algorithmic-bias and 'digital redlining' risk: The 10-K flags fair-lending exposure from AI use as an evolving regulatory risk | Announced | Feb 20, 2026 | [Dime profile](https://www.bankingnewsai.com/banks/dime#where) | [Dime Community Bancshares (SEC filing)](https://www.sec.gov/Archives/edgar/data/846617/000084661726000013/dcom-20251231x10k.htm) |
| 7 | [SoFi](https://www.bankingnewsai.com/banks/sofi) | 1 | Fairness-constrained ML training (patent): US 11,928,730 B1 describes training machine-learning models with a fairness improvement for financial decisions such… | Announced | Dec 5, 2024 | [SoFi profile](https://www.bankingnewsai.com/banks/sofi#where) | [Google Patents (USPTO)](https://patents.google.com/patent/US11928730B1/en); [Google Patents (USPTO)](https://patents.google.com/patent/US20240403952A1/en) |
| 8 | [United Bank](https://www.bankingnewsai.com/banks/united-bankshares) | 1 | Mortgage-lending record on file with the Federal Reserve: In approving the Piedmont merger the Fed recorded an adverse comment on United Bank's home loans to Black borrowers in… | Announced | Dec 2, 2024 | [United Bank profile](https://www.bankingnewsai.com/banks/united-bankshares#where) | [Banking Dive](https://bankingdive.com/news/fed-approves-united-piedmont-merger/734324); [Federal Reserve Board](https://www.federalreserve.gov/newsevents/pressreleases/orders20241129a.htm) |

## How is this ranked?

Data: the 141 bank profiles on this site (lib/banks), every entry dated and sourced: the "Where AI runs" use cases, each with a status (in production, rolling out, pilot, announced).

Rule: Use cases tagged fair-lending. Score = matching use cases. Order: matching use cases, then those in production, then the strongest stage, then the newest source.

Counts: 8 of 141 profiled banks qualify; 12 distinct sources cited in the table. Data checked 2026-10-28; ranking template updated 2026-10-30.

## What are the caveats?

Banks absent from the table have nothing matching on the record this site holds, which is not evidence that they do nothing. Each row links the bank's own page, where every entry carries its sources.

## FAQ

### Which bank ranks first?

Renasant Corporation leads with 1 use case: Standing fair lending compliance committee: The proxy describes a standing committee that includes the chief risk officer and chief compliance officer and reports…. Ameris is second with 1.

### How many large banks describe AI use cases for fair lending?

8 of the 141 profiled banks (120 US, 21 non-US groups). Among those listed, 6 are US banks and 2 non-US groups.

### What counts, and what does not?

Use cases tagged fair-lending. Score = matching use cases. Order: matching use cases, then those in production, then the strongest stage, then the newest source. Source: the 141 bank profiles on this site (lib/banks), every entry dated and sourced: the "Where AI runs" use cases, each with a status (in production, rolling out, pilot, announced).

### Is this a measure of capability?

No. Every row is a sourced fact on the public record: what the bank, a regulator, a filing or an independent outlet has put on the record. The page does not rate how well anything works.

### When was this checked, and how often does it update?

The data behind the table was last checked on 2026-10-28 (bank profiles). The page recomputes whenever that dataset changes and is republished with the site; it is never edited by hand.

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