Financial services · Databricks
Coastal Community Bank: Leveraging Databricks’ framework for AI security for banking as a service
Public case study · not a Metric Oak engagement
- Cost to run model giving highest accuracy (94%) on entire data corpus with 1 million tokens
- $1.05
- Decrease in time to complete vendor due diligence reviews by providing automated output to human reviewers
- 66%
- Accuracy of customer complaints classifier with coverage of 100% of records vs. prior process based on sampling
- 94%
What happened
The story in brief
A US community bank that also provides banking-as-a-service needed to review complaints and vendor due diligence at scale. It used large language models on Databricks to classify every customer complaint instead of a sample and to pre-draft due-diligence reviews for human checking.
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Related insights
Frequently asked questions
What results did Coastal Community Bank report?
Coastal Community Bank reported $1.05 cost to run model giving highest accuracy (94%) on entire data corpus with 1 million tokens; 66% decrease in time to complete vendor due diligence reviews by providing automated output to human reviewers; 94% accuracy of customer complaints classifier with coverage of 100% of records vs. prior process based on sampling, according to its customer story published by Databricks.
Which platform and technologies did Coastal Community Bank use?
Coastal Community Bank used Databricks, including Unity Catalog, Large language models.
How do these results compare with other financial services programs?
See our financial services benchmark for median cost reductions and speed-ups across published results in the industry.
Source
Customer story published by Databricks: Leveraging Databricks’ framework for AI security for banking as a service (opens databricks.com in a new tab) on databricks.com. Figures as published, retrieved 27 October 2026. The summary above is Metric Oak's own. This is not a Metric Oak engagement.
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