How Backbase built AI into banking infrastructure: lessons from 100+ deployments
Banks have spent years running AI pilots. Backbase took a different approach: building AI into the foundation of its Banking OS. Not as a feature layer but as core infrastructure. The platform now powers over 100 banks globally with production capabilities that deliver measurable impact. Here's how Backbase is unlocking what banks can do for their clients with AI.
Personalization that works at scale
Banks know their customers want personalized experiences. Marketing teams create campaigns anyway, launch them broadly, and watch activation rates stay low.
The Customer Lifetime Orchestrator (CLO) changes that. It's a machine learning-powered engine that generates individualized engagement plans across the entire customer lifecycle. The system analyzes behavior patterns, identifies the next best action for each customer, and uses generative AI to create campaign content that matches the bank's voice.
Banks can now deliver segment-of-one experiences at scale, predicting churn before it happens and moving from reactive outreach to proactive engagement that drives measurable activation.
Banking agents that execute, not just respond
Many banks have deployed chatbots that answer customer questions. But the real advantage comes from AI agents that can actually execute tasks and complete operations on behalf of customers.
Backbase has deployed over 25 AI agents that handle banking operations through conversation. A Financial Coach analyzes spending patterns and provides guidance. A Dispute Management agent handles intake and routing. Lending Policy agents review applications against product criteria automatically.
What used to require customers to navigate multi-step workflows now happens through simple conversation. Call centers see reduced volume. Loan processing moves faster. The result is banking where the interface becomes the interaction itself.
Integration in weeks, not months
Banks typically spend 40 to 60 percent of digital transformation budgets on integration. Mapping data between systems requires specialized expertise and eats months of project time.
Connector Studio uses AI agents trained on knowledge from over 100 bank integrations to automatically map schemas between third-party APIs and Backbase's data model. What took months now takes weeks.
The impact goes beyond speed. Integration time drops by 80 percent, transformation costs fall, and banks reduce their dependency on specialized resources while accelerating their path to value.
From pilots to production
These aren't roadmap features or proof-of-concept experiments. They're production-grade capabilities with early customer traction, delivering practical outcomes across personalization, customer service, and integration infrastructure.
AI in banking is moving past the experimentation phase. The question isn't whether to deploy AI. It's whether your infrastructure can deliver it at scale.
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