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AI adoption: Fintech & Financial Services

AI adoption in Fintech & Financial Services

Financial services gain the most from AI in document-heavy, rule-heavy work, and carry the most risk when a model is wrong. The path starts where a person can check every output.

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Use cases

Where AI pays off first

  • 01

    Onboarding and KYC

    Reading identity and income documents, flagging gaps and preparing cases for review.

  • 02

    Fraud and risk signals

    Models that surface unusual behaviour for analysts instead of deciding alone.

  • 03

    Customer service

    Assistants that answer account and product questions from approved content.

The path

The path for Fintech & Financial Services

Four stages, each ending with a decision. The first line of every stage is specific to your industry.

Step 1: Assess

Find where AI is worth it

  • Map each use case to the regulation and audit requirements it touches before estimating value
  • List the work that is repetitive, slow or stuck in documents
  • Score each use case by value, feasibility and risk

Use case scoring

12 assessed
Use caseValueFeasibleRiskOnboarding and KYCFraud and risk signalsCustomer service

Owner: Head of onboarding

Measure: minutes per application

Step 2: Pilot

Prove one use case

  • Start with document reading for onboarding, where staff verify every extracted field
  • Build one narrow use case with real users and real data
  • Write evaluation tests before the first prompt

Pilot: onboarding and kyc

Week 4

99% fields correct

Time to verify an application

Baseline24 min
Pilot7 min

Decision: continue

Reviewed with the owner

Step 3: Integrate

Put it where work happens

  • Keep customer data in approved environments and log every model input, output and human decision
  • Connect to the systems people already use, not a separate tool
  • Add access control, logging and data protection

Connected systems

  • Core bankingCustomer records
  • KYC providerIdentity checks
  • Case managerStaff review queue
Access controlLoggingData protectionUsage policy
Step 4: Scale

Make it routine

  • Extend to underwriting support and collections once explainability and audit trails are proven
  • Reuse the same platform, patterns and guardrails for the next use case
  • Move from assistants to agents where the evidence supports it

AI use cases

This quarter
  • Onboarding and KYCLive
  • Fraud and risk signalsLive
  • Customer servicePilot

3x applications

Handled per analyst, each day

Fintech & Financial Services

Secure financial workflows, designed for trust.

We build payments, onboarding, lending operations and financial data products. AI-assisted document processing and support workflows include review paths, access controls and traceability from the first release.

What we buildWhat we build

Find your first AI use case

Tell us about your business and where the time goes. We will come back with the use cases we would assess first and how we would test them.

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Your project plan

Ready five days after the first call

Share plan

Approach

Fixed scope
  • Start with sign-up and onboarding, the riskiest flow
  • Ship to a pilot group every week from week three
  • Add the invoice reading agent once data is flowing
First release
Week 4
Demos
Weekly
Code
Yours

Team

5 people
  • PLProduct lead
  • SESenior engineer
  • SESenior engineer
  • PDProduct designer
  • AIAI engineer

Timeline

10 weeks
Discovery
Design
Build
Launch