AI Agent - Customer Onboarding Automation
A U.S.-based fintech offering a full suite of business banking, payments, merchant services, and lending solutions for small and medium-sized enterprises (SMEs) partnered with Marslytics to develop an Agentic AI-enabled customer onboarding workflow. As the platform expanded, new business account applications began scaling rapidly reaching over 20,000 onboardings per month.
The existing manual onboarding workflow, involving Know Your Business (KYB), Know Your Customer (KYC), adverse media, fraud checks, and business verification etc created a serious operational bottleneck.
Before automation, the onboarding process required extensive manual document verification and compliance review, often taking up to a week per applicant.
The client's operational and compliance teams faced several critical issues:
Extended processing times leading to drop-offs during onboarding
Heavy dependency on manual review, limiting scalability
Different agents interpreted checks differently, leading to inconsistent outcomes
Operational costs increasing with growing onboarding volumes
The goal was to build a fully automated, AI-driven onboarding system that could handle 90% of applications end-to-end without compromising regulatory compliance or fraud detection accuracy.
We developed a suite of AI agents orchestrated via an LLM-driven decision framework hosted on AWS, enabling the automation of multiple checks from identity verification to fraud risk assessment.
The solution was built using modular LLM agents, each responsible for a distinct onboarding task. These agents collaborated within a controlled orchestration environment, executing checks either in parallel or sequentially, depending on risk thresholds.
| Agent Type | Functionality | Technology |
|---|---|---|
| Document Verification Agent | Extracts and validates KYC/KYB documents, registration certificates, tax IDs, and licenses. | AI based entity extraction & document parsing |
| Business Context Agent | Reviews the applicant's business description and pitch to verify legitimacy and category alignment. | AI reasoning & context validation |
| Adverse Media Agent | Performs media and sanctions screening, searching the entire web using natural language understanding to flag negative mentions or regulatory issues. | AI reasoning with sentiment and entity context |
| Fraud Detection Model | Evaluates application patterns, device fingerprints, and behavioral signals to detect anomalies. | ML based risk scoring |
| Orchestration Layer | Coordinates parallel or sequential checks depending on intermediate results. For instance, high fraud scores halt further downstream checks. | AI workflow control & rules layer |
Each AI agent interacts through a decisioning API, ensuring explainability and consistent decision flow.
Feedback from manual reviews is fed back into model retraining loops, continuously improving the precision of the agents.
Decision explanations are logged and auditable to meet regulatory transparency requirements.
The system operates under a controlled AI governance framework, ensuring every automated action remains traceable and compliant.
The deployment fundamentally transformed the client's onboarding process.
| Impact Area | Before | After |
|---|---|---|
| Onboarding Time | ~1 week | Few minutes |
| Automation Rate | 35% applications auto-decisioned | 90% auto-decisioned |
| Manual Review Scope | Full-application verification | Only flagged edge cases |
| Operational Efficiency | Linear scaling of team with growth | Flat ops team even as volume scales |
| Decision Consistency | Varies by human reviewer | Standardized AI-driven evaluation |
| Drop-offs | High, due to long turnaround | Significantly reduced |
Improved accuracy and trustworthiness consistent decision logic removed human interpretation bias
Reduced compliance backlog instant checks free up compliance officers for complex cases
Enabled the client to handle exponential growth without proportional hiring
"What used to take a week of back-and-forth and manual verification is now handled in minutes. The AI agents don't just automate they reason, ensuring every decision is consistent and auditable."
By deploying an AI agent ecosystem, fintech transformed a resource-intensive, manual onboarding process into a real-time, automated, and intelligent verification engine.
With 90% of cases now fully automated, onboarding is faster, more reliable, and seamlessly scalable empowering compliance, operations, and customer experience teams alike.
This initiative illustrates how LLM-driven automation can create operational leverage achieving both speed and trust at scale in financial onboarding.