Solution Accelerator

The Crego AI Lending Platform Transformation A Fintech Success Story Powered by AWS AI

The Moment of Truth

In early 2024, Crego — India’s fastest-growing AI-native lending platform — faced a critical challenge weeks before its launch. 

“We expect thousands of real-time AI credit decisions per minute and hundreds of concurrent loan applications,” said the CTO.

“Our legacy on-premise MySQL and rule-based workflow will collapse under this scale. Any outage risks losing customers, trust, and regulatory compliance.”

The Harsh Reality We Discovered

  • Legacy monolithic lending systems with manual credit evaluation. •  
  • Slow rule-based processes: ~2–3 days per loan application. 
  • No AI-driven decisioning or workflow automation. •  
  • Security gaps: no encryption at rest for sensitive customer data. •  
  • Zero ability to scale to multi-tenant, high-throughput workloads. 
  • Limited observability, monitoring, and auditability. 

Immediate and long-term risks threatened both market launch and regulatory complianc

The New Foundation: AWS AI-Powered Lending Platform

Crego partnered with AWS to build a cloud-native, scalable, secure AI platform, capable of delivering real-time lending decisions across multiple financial institutions. 

Core AI & Compute Layer 

  • Generative AI: Amazon Bedrock (Claude-2) for semantic analysis of loan applications and historical customer data. 
  • Containerized Orchestration: Amazon ECS + Fargate for AI workflow automation and real-time decisioning. 
  • Persistent Storage: Amazon RDS MySQL for structured transactional data; Amazon S3 for unstructured loan documents and historical datasets. 
  • Real-Time Scaling: Auto-scaling ECS clusters handled thousands of concurrent AI scoring requests with sub-second latency. 

Performance Achieved: 

  • Credit evaluation time: days → minutes (~85% faster) •  
  • Operational cost reduction: ~40% savings via AI automation 
  • Real-time loan approvals: thousands of decisions per minute 1 Agentic AI Practice: Smarter Decisioning 

Customer Requirements Analysis: 

  • Automate credit scoring and risk evaluation across multiple financial institutions. •  
  • Integrate historical and real-time transactional data with low latency. 
  • Ensure auditability, security, and regulatory compliance. •  

Agentic AI Workflow: 

  • Data ingestion triggers ECS workflows from S3. 
  • Pre-processing validates and normalizes loan data. 
  • Bedrock models perform semantic analysis, scoring, and risk evaluation. •  
  • ECS orchestrates rule-based overrides, stores outputs in RDS, and logs all activity. •  
  • Continuous feedback loop improves AI model accuracy over time. 

Security & Compliance (RBI-Style Governance) 

  • Authentication & Authorization: AWS IAM with fine-grained RBAC. 
  • Data Encryption: KMS-encrypted RDS/S3 storage, TLS 1.2+ in transit. 
  • Monitoring & Audit: CloudWatch + CloudTrail track all agent activity, AI inference, and workflow execution. 
  • Operational Security: Zero-trust controls with automated policy enforcement and compliance logging. 

Responsible AI Implementation 

  • Bias and fairness checks against historical loan datasets. •  
  • Explainable AI outputs with rationale for regulatory auditability. •  
  • Safety thresholds and rule-based overrides prevent unsafe lending decisions. 
  • Continuous production monitoring and iterative improvements based on feedback. 

Compute Infrastructure Highlights 

  • Amazon ECS + Fargate: Containerized AI workflows with serverless scaling. 
  • Streaming AI Inference: Bedrock models accessed via ECS tasks, ensuring low latency and high throughput. 
  • Cost Optimization: Right-sized containers with auto-scaling reduce idle compute costs. 
  • Data Integration: ECS workflows interact with RDS and S3 for a secure, fully managed compute 
  • pipeline. 

Launch Day: AI Stack at Scale

  • Concurrent users: 4,300+ active loan applicants. •  
  • AI decisions processed: 180,000+ per hour. 
  • Latency: p95 query latency < 20 ms; end-to-end offer generation < 90 ms. 2
  • Uptime: 99.999%, including Multi-AZ failover. 
  • Customer impact: Zero disruption, real-time approvals, instant satisfaction. 

Beyond Survival: Tangible Wins

  • 50% reduction in total cost of operations vs legacy systems.
  • Continuous compliance reporting reduced from days → minutes using automated AI workflows. 
  • Multi-tenant AI workloads scaled seamlessly across multiple financial institutions. •  
  • Faster feature rollouts enabled Crego to outpace competitors and deliver new lending products in weeks. 

AWS Services Leveraged

Service Purpose

Amazon Bedrock 

Generative AI for credit analysis, semantic evaluation, and decision 

(Claude-2) 

making

Amazon ECS + Fargate Containerized AI workflows and task orchestration

Amazon RDS (MySQL) Persistent storage for transactional and AI output data

Amazon S3 Repository for unstructured loan documents and historical datasets

CloudWatch Real-time monitoring of AI inference and workflow execution

CloudTrail Audit and compliance tracking

Customer Testimonial

“AWS allowed us to move from slow, manual processes to a fully AI-driven, cloud-native lending platform. With Bedrock and ECS, we can scale, stay compliant, and deliver intelligent credit decisions in real time.” 

Conclusion

Crego’s adoption of AWS AI Competency services demonstrates how Generative AI and Agentic AI workflows can transform financial services. By combining cloud-native architecture, scalable AI inference, and responsible AI practices, Crego achieved: 

  • Real-time credit decisioning at scale. •  
  • Operational efficiency and cost reduction. 
  • Regulatory compliance and auditability. •  
  • A platform that enables multi-tenant, scalable growth. 

This project highlights the potential of AWS AI services in modernizing and scaling production-grade digital lending solutions.