Customer Service Automation · Enterprise Knowledge Search · Risk Management
Home Credit Bank needed a safer enterprise AI environment that non-technical teams could use to build banking agents quickly without creating data-leak risk or relying on a long custom-development cycle for each new use case.
Reported impact
Home Credit Bank reduced AI deployment time, improved conversion, and gave business teams a secure path to create their own banking agents without pushing work into uncontrolled tools.
Home Credit Bank consolidated its AI work onto Gemini Enterprise so both business and technical teams could build banking agents inside a controlled environment. After migrating fully into the secure Gemini Enterprise setup, the bank began using no-code Agent Designer features to create a digital subject-matter expert that helps field officers determine customer financing eligibility from historical information. The same environment also enabled the CRM team to design and launch a churn-prediction agent in just two weeks, much faster than the prior approach, and that faster rollout improved customer conversion. By grounding workflows and internal knowledge inside Gemini Enterprise, the bank reduced the risk of employees pushing sensitive work into unmanaged tools while also making it much easier for business teams to build new agentic services themselves. The result is a secure, extensible foundation for compliant retail-banking AI.
Internal knowledge and historical banking data are grounded inside Gemini Enterprise, where teams use Agent Designer to build agents such as financing-eligibility and churn-prediction assistants. Those agents then return guidance to employees and CRM workflows within the secure environment.
Achieved 100% migration to a secure AI environment with Gemini Enterprise
Automating workflows and grounding internal knowledge with Gemini Enterprise
Expanding our use of Gemini Enterprise gives us the long-term technical infrastructure to safely scale our retail banking model
Using the no-code Agent Designer within Gemini Enterprise, non-technical business teams built a digital subject matter expert
our customer relationship management team was able to design and deploy a churn prediction agent themselves in just two weeks—80% faster than before—increasing customer conversions by 40%
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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