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, 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 without compromising regulatory compliance or fraud detection accuracy.
We developed a suite of AI agents orchestrated via an AI 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 AI 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 | 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 to flag negative mentions or regulatory issues. | AI reasoning with sentiment & 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. 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 agent precision.
Decision explanations are logged and auditable to meet regulatory transparency requirements.
Every automated action remains traceable and compliant under a controlled AI governance framework.
The deployment fundamentally transformed the client's onboarding process.
| Impact area | Before | After |
|---|---|---|
| Onboarding time | 72 hrs | 1 hr |
| Automation rate | 35% auto-decisioned | 90% auto-decisioned |
| Manual review scope | Full application verification | Only flagged edge cases |
| Operational efficiency | Linear team scaling 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 |
By deploying an AI agent ecosystem, the 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 AI-driven automation can create operational leverage achieving both speed and trust at scale in financial onboarding.