AI-Driven Modernization of SMB Lending Platform with RPA & Cloud-Native Architecture

Overview

A fast-growing SMB lending platform has carved out a niche in business financing by combining relationship-driven sales with a responsive underwriting team. With increasing deal flow and a growing partner ecosystem, the company needed to scale operations without a proportional increase in headcount.

However, the early stages of the loan process—such as responding to inquiries, conducting background checks, and processing documentation—were highly manual, consuming significant time and resources. As demand grew, these inefficiencies began to stall the platform’s growth momentum and partner experience.

To overcome these bottlenecks, the platform partnered with TVS Next to modernize their operations through Robotic Process Automation (RPA) and cloud-native architecture—accelerating deal turnaround, improving compliance, and unlocking new scalability.

Challenges

Manual Pre-processing

Inquiry handling, background checks, and email replies were time intensive.

Slow Partner Onboarding

Integrating a new partner could take weeks, delaying revenue opportunities.

Operational Silos

Sales, underwriting, and accounts worked in disconnected systems.

Testing and QA Delays

Manual, repetitive QA cycles slowed down new feature rollouts and bug resolution.

Rigid Legacy Stack

Launching new loan products or funding sources required lengthy development cycles.

Our Approach

TVS Next facilitated Accelerated Discovery Workshops with stakeholders from sales, underwriting, QA, and compliance to understand operational pain points and design a modernization roadmap. The transformation was rolled out in four focused phases.

Application and Infrastructure Modernization

  • Refactored monolith into containerized .NET microservices and migrated to Azure for scalability.
  • Built CI/CD pipelines to enable seamless DevOps automation.

RPA and Workflow Automation

  • Automated pre-processing tasks: Bots handled email triage, document extraction, and background checks.
  • API-driven partner onboarding: Enabled near-instant partner setup through a centralized integration gateway.

UX and Approval Journey Optimization

  • Redesigned workflows for role-based dashboards and approval routing.
  • Reduced turnaround times by automating inter-department handoffs and status tracking.

Testing and Compliance Automation

  • Automated QA framework: Introduced early, parallelized testing with auto-regression checks.
  • Implemented real-time compliance monitoring with automated alerting and tracking.

Impact and Results

Inquiry Pre-processing

Before: Manual, 2–3 hours daily.

After TVS Next Intervention: Automated, under 15 minutes.

Partner Onboarding

Before: 4+ weeks.

After TVS Next Intervention: Real-time via APIs.

Testing & QA Cycle

Before: Manual, repetitive.

After TVS Next Intervention: Fully automated and parallelized.

Feature Rollouts

Before: Delayed by QA bottlenecks.

After TVS Next Intervention: 35% faster time-to-market.

Deal Approvals

Before: Slower.

After TVS Next Intervention: 35% increase.

Employee Productivity

Before: Manual task overload.

After TVS Next Intervention: 25% higher efficiency.

Customer Acquisition

Before: Steady growth.

After TVS Next Intervention: 3X increase.

Key Technologies Used

  • Automation Anywhere (RPA)
  • Azure Kubernetes Service (AKS)
  • Containerized .NET Microservices
  • Azure API Management
  • Azure DevOps Pipelines
  • Parallel Automated Testing Framework

Conclusion

By modernizing the platform’s foundational processes and automating key operational tasks, the SMB lending provider gained the agility and scalability it needed to thrive in a competitive market.

Testing bottlenecks were eliminated, partner onboarding became instant, and customer acquisition tripled—all without a proportional increase in headcount.

With RPA and automation at its core, the platform is now equipped for sustainable, tech-driven growth.