NeoGrowth Credit Pvt. Ltd. is one of India's leading fintech NBFCs, providing collateral-free business loans and merchant financing solutions to small and medium enterprises through a fully digital lending platform.

The organization processes millions of financial transactions across loan management systems, customer onboarding, repayment platforms, merchant settlements, CRM systems, payment gateways, and financial applications. As business volumes increased, NeoGrowth required a modern cloud-native analytics platform capable of supporting enterprise-scale reporting, governance, compliance, and real-time business intelligence.

Business Challenge

  • NeoGrowth's existing analytics environment struggled to keep pace with rapidly growing data volumes and evolving reporting requirements.
  • Business data was distributed across multiple SQL Server databases supporting lending, repayments, collections, merchant onboarding, and customer servicing, making centralized analytics difficult.
  • Manual ETL scripts required continuous maintenance, while frequent schema changes across lending products resulted in recurring downstream failures and inconsistent reporting.
  • The organization also lacked centralized metadata management, automated monitoring, proactive alerting, and robust disaster recovery capabilities. As an RBI-regulated financial institution, NeoGrowth required stronger governance, encryption, audit logging, and enterprise-grade security.

Goals & Objectives

  • The objective was to establish a secure, scalable, and fully automated AWS-native analytics platform capable of centralizing operational data, automating ETL processes, improving reporting speed, strengthening governance, and enabling enterprise-grade business intelligence.
  • The platform also needed to provide automated metadata management, disaster recovery, monitoring, and compliance-ready security controls.

Solution Approach

  • Pentagon System & Services designed and implemented a modern cloud-native analytics platform leveraging Amazon Redshift, AWS Database Migration Service (DMS), AWS Glue, Amazon S3, Amazon QuickSight, AWS Lambda, and AWS CloudFormation.
  • Operational data from multiple on-premises Microsoft SQL Server databases was securely migrated into Amazon Redshift using AWS DMS over an AWS Site-to-Site VPN connection.
  • AWS Glue replaced manually developed ETL scripts with fully automated serverless data processing pipelines, while AWS Glue Crawlers continuously scanned datasets stored in Amazon S3 and automatically maintained the AWS Glue Data Catalog.
  • Amazon Redshift became the centralized enterprise data warehouse supporting lending analytics, repayment reporting, merchant performance analysis, executive dashboards, portfolio analytics, and regulatory reporting.
  • Amazon QuickSight enabled secure, role-based dashboards for executives, finance teams, risk analysts, and operations users, providing near real-time business insights.

Implementation Approach

  • AWS Lambda automated workflow orchestration while Amazon SNS generated real-time notifications for ETL failures, replication issues, and operational events.
  • Amazon CloudWatch and AWS CloudTrail provided centralized monitoring, logging, operational visibility, and audit capabilities across the platform.
  • Enterprise security was implemented using AWS IAM, AWS KMS encryption, AWS Secrets Manager, Microsoft Entra ID integration, Amazon VPC, and Site-to-Site VPN connectivity.
  • To strengthen business continuity, Amazon Redshift automated snapshots with cross-region replication ensured disaster recovery readiness, while the complete infrastructure was provisioned using AWS CloudFormation for consistent Infrastructure-as-Code deployments.

AWS Services Utilized
  • Amazon S3
  • AWS Database Migration Service (AWS DMS)
  • Amazon Redshift
  • AWS Glue
  • AWS Glue Data Catalog
  • AWS Lambda
  • Amazon QuickSight
  • Amazon SNS
  • Amazon CloudWatch
  • AWS CloudTrail
  • AWS IAM
  • AWS KMS
  • AWS Secrets Manager
  • Amazon VPC
  • AWS Site-to-Site VPN
  • Amazon Redshift Snapshots
  • AWS CloudFormation
  • Microsoft Entra ID

Results

  • The implementation transformed NeoGrowth's analytics ecosystem into a centralized, scalable, and highly automated cloud platform.
  • Manual ETL processes were eliminated through AWS Glue automation, significantly improving operational efficiency while increasing data consistency across reporting systems.
  • Amazon Redshift established a unified enterprise data warehouse, enabling faster analytics and near real-time visibility into loan portfolios, merchant performance, repayment trends, customer behavior, and executive KPIs.
  • Automated metadata discovery through AWS Glue Crawlers simplified schema management and reduced the operational impact of evolving lending products.
  • Comprehensive monitoring, automated alerting, enterprise-grade encryption, centralized governance, and cross-region disaster recovery significantly strengthened platform reliability, compliance readiness, and operational resilience.
  • The result was a future-ready analytics platform that empowers NeoGrowth with faster decision-making, improved governance, enhanced security, and a scalable foundation for continued digital lending growth.

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