NBFC Cut Reporting Time by 70% with Data Scientists

Our client is a licensed NBFC (Non-Banking Financial Company) regulated by the Reserve Bank of India. Their mission is to support financially excluded MSMEs, especially in unorganized, underserved segments across urban and rural regions.

The platform enables:

• Micro-entrepreneurs to secure loans to start or grow small businesses

• Transparent and affordable financing options tailored to underserved markets

• Operational expansion without compromising compliance or credit risk evaluation

The NBFC has grown steadily in India, especially among informal sector businesses, but needed better visibility into operational performance.

The company had a clear mission:

Provide transparent, cost-effective financing for MSMEs with speed and accountability.

But legacy processes were holding them back:

Manual Excel-based modeling made it difficult to accurately track sales team productivity, operational bottlenecks, and loan turnaround time.

Despite a solid borrower base, the company faced key data challenges:

Sales and operations teams tracked performance in Excel sheets. This led to errors, inefficiencies, and inconsistent reporting.

There was no unified analytics platform to measure loan processing time, sales performance, or demographic insights.

Manual modeling lacked validation, and trends couldn’t be trusted for decision-making.

Without automation and a robust data pipeline, the company couldn’t scale operations or forecast growth with confidence.

Muoro deployed a team of expert Data Scientists and Architects to build a modern analytics foundation.

We audited their reporting workflows and raw data structures to:

• Understand their key operational and sales KPIs

• Identify gaps in data collection and aggregation

• Define the models and metrics that mattered most

We built a scalable data ecosystem in stages:

• Database Design: Structured their MS SQL environment for clean data input/output

• Cloud Migration: Moved infrastructure to AWS (EC2, S3) with Kubernetes orchestration

• Data Warehouse: Centralized all historical and live data

• Model Development: Applied ML models to analyze sales patterns and demographic vectors

• Cross-validation: Verified sales performance across sample data for accuracy

• Visualization Dashboards: Enabled real-time Power BI views of team productivity and operational metrics

We used a Python-based stack to drive both backend data processing and advanced analytics:

1. Python – Core scripting language for data manipulation, model training, and automation pipelines

2. NumPy – Powered numerical computations across large data sets with optimized array processing

3. TensorFlow – Enabled the development of predictive models to assess team performance, loan efficiency, and customer segmentation

We migrated the entire solution to AWS, ensuring flexibility, scalability, and security:

1. AWS EC2 – Hosted application and model services on secure virtual servers

2. AWS S3 – Provided cost-effective, durable object storage for structured and unstructured data

3. Kubernetes – Managed microservices, streamlined CI/CD, and ensured reliable deployment of model updates and APIs

A robust backend was essential to support analytics at scale:

1. MS SQL Server – Structured data warehouse setup to centralize historical, demographic, and transactional data

2. Data Pipelines – Automated ingestion and transformation of raw data into analysis-ready formats

3. Metric Layer – Built reusable KPIs and reporting logic to support executive and operational dashboards

We brought clarity and actionability to complex data sets with rich, interactive interfaces:

1. Power BI – Delivered role-based dashboards for sales, operations, and leadership teams

2. Custom Visuals – Created slicers, drill-downs, and heatmaps to highlight productivity gaps and turnaround trends

3. Scheduled Reports – Enabled automated daily, weekly, and monthly reports for tracking performance over time

Here are major deliversables we provided the client.

Managers gained real-time visibility into field team performance, reducing manual tracking time.

Loan turnaround times were reduced as bottlenecks were flagged through analytics.

Validated models and removed inconsistencies caused by manual Excel reporting.

The company is now positioned to grow operations with real-time insights and predictive analytics.

Visual dashboards enabled better credit assessment by tracking borrower profiles and repayment patterns.

The client gained:

• A scalable, cloud-based analytics system

• Automated reporting and insights

• Accurate models to guide field teams and loan officers

• A dedicated data science partner to evolve their solution

If your financial institution still relies on manual reporting and fragmented data, Muoro can help.

We provide fully managed teams in Data Science, ML, DevOps, and more, built around your needs and delivered fast.

Let’s talk.