Generative AI development in fintech
Financial teams handle documents, rules, and customer questions at scale, and an auditor or regulator may review any output. Generative AI fits where it speeds up people who stay accountable for the decision.
Where generative AI fits
- Answering customer and agent questions from product terms, fee schedules, and policy documents, with sources.
- Summarizing and triaging support tickets, complaints, and dispute cases.
- Extracting data from statements, contracts, and onboarding documents.
- Drafting analyst notes and compliance reports from source material for a person to review.
What shapes the build
An audit trail
Each answer should trace back to its inputs: the documents retrieved, the model version, and the prompt version.
Consistency
Two similar cases should get similar treatment. That takes fixed test cases and version control on prompts and models, so a change does not alter behavior unnoticed.
Decisions and regulation
Credit, fraud, and eligibility decisions can fall under specific rules. Keep the model out of the final decision unless your compliance team has approved the design.
What we ask first
- Which decisions stay with a person, and which steps around them can be automated?
- What must be logged, and for how long?
- Which customer data will the system see, and where may it be processed?
- Who reviews vendors and models before they touch production data?
Services that apply
This page describes the field. We have not published a case study in fintech yet. The case studies page lists the work we have.