A mid-size US digital bank processes 5,000–8,000 loan applications monthly across retail and SME lending portfolios, alongside high volumes of payment transactions and daily reconciliation cycles. Manual underwriting, reactive fraud response, and multi-day reconciliation were slowing operations and increasing risk exposure. Within the first year, BNXT deployed six autonomous AI agents across loan processing, fraud response, payment recovery, credit risk, reconciliation, and collections - cutting manual underwriting effort by 65%, fraud response time to under 2 minutes, and reconciliation cycles from days to hours.
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Reduction in manual effort
Payment failure recovery
Reconciliation cycles
Every critical lending function ran on manual handoffs and reactive workflows:
Industry: Financial Services / Digital Banking
Services: AI Product & Agentic AI Development
Region: United States
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BNXT's AI services team built six coordinated autonomous agents, each owning a distinct function within the lending operation - connected to existing core systems rather than replacing them.
All six agents run on a shared cloud services infrastructure connected to the bank's core systems via Azure API Management. Operational performance across every workflow surfaces through a business intelligence layer in Power BI - resolution rates, escalation volumes, recovery rates, and decision audit trails in a single view.
The 65% underwriting reduction changed where skilled people spent their time. Underwriters who previously cleared a queue of straightforward validations now handle complex income structures, borderline eligibility, and high-value SME applications. The volume held steady; the composition shifted entirely. Judgment-required cases up, routine processing out.
Fraud response saw the sharpest operational shift. The previous workflow had a structural gap between detection, alert, and action - in a live transaction environment, that gap is where damage compounds. Closing it to under 2 minutes wasn't an incremental improvement; it changed the risk profile of the fraud function.
For finance, reconciliation removing a 3–5 day bottleneck meant downstream work - reporting, period close, regulatory submissions - stopped waiting on it. Escalations that do reach the team arrive with reasoning already assembled; the work is a decision, not a reconstruction.
Collections moved from volume to precision. Uniform batch outreach treats every delinquent account identically; the agent segments by profile, debt age, and response history. Doubling contact effectiveness from ~28% to 55–60% reflects fewer, better-targeted touchpoints - not more outreach.
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