3rd International Conference on Business Intelligence and Data Analytics (BIDA 2026)

Organized by RV Institute of Management (RVIM), Bangalore, India

 Technically Sponsored by Soft Computing Research Society

April 11-12, 2026The after-conference proceeding of the BIDA 2026 will be published in Scopus Indexed Springer Book Series "Smart Innovation, Systems and Technologies"

The after-conference proceeding of the BIDA 2026 will be published in Scopus Indexed Springer Book Series "Smart Innovation, Systems and Technologies"

Narendra Boggarapu

Real Time Cloud Based Credit Scoring Using AI and Big Data Analytics

Abstract:

The rapid evolution of financial technology has significantly transformed credit risk assessment, shifting from traditional rule-based models to intelligent, cloud based, real time credit scoring systems. Conventional credit evaluation approaches rely on limited historical data and often require several days for processing, leading to inefficiencies, delayed decision making, and exclusion of individuals with insufficient credit history.

This study presents a cloud based architecture that leverages artificial intelligence and big data analytics to enable real time, scalable, and highly accurate credit scoring. By integrating diverse data sources including transactional, behavioral, and alternative financial data, modern systems can evaluate thousands of data points per applicant within seconds. The use of machine learning models significantly enhances prediction accuracy while reducing false positives and enabling proactive fraud detection.

The proposed framework adopts a layered architecture comprising data collection, processing, and analytical components supported by microservices and distributed systems. Real time data streaming and cloud infrastructure allow high throughput processing, enabling financial institutions to handle thousands of concurrent credit evaluations while maintaining high availability and performance. Advanced techniques such as ensemble learning, deep neural networks, and anomaly detection further strengthen risk assessment capabilities.

Empirical evidence demonstrates substantial improvements in operational efficiency, including reduced processing time, enhanced decision accuracy, and expanded credit access for underserved populations. Additionally, the integration of security and compliance frameworks ensures data protection, regulatory adherence, and transparency in automated decision making.

This research highlights how AI driven cloud based credit scoring systems are redefining financial services by enabling faster, more inclusive, and reliable credit decisions, ultimately improving both institutional performance and customer accessibility.
 

Profile:

Narendra Bhargav Boggarapu is a Lead Software Engineer at Wells Fargo in Charlotte, NC, with over 11 years of experience in enterprise IT and Salesforce platform development. As a Salesforce Technical Architect and Subject Matter Expert, he specializes in delivering large scale Salesforce implementations across all phases of the software development lifecycle.

He holds a Master’s degree in Computer Science from Texas A&M University and a Bachelor’s degree in Information Technology from JNTUH. His expertise spans Lightning Web Components, Apex, Visualforce, and enterprise cloud platforms including Sales, Service, Financial, and Marketing Clouds.

Narendra has led major initiatives such as loan origination systems, tax filing gateways, and supplier onboarding platforms across organizations including Wells Fargo, American Express, and the State of Ohio. He brings deep expertise in system integration using REST and SOAP APIs, MuleSoft, Informatica, and modern DevOps tools such as Git, Jenkins, and Copado.

A certified Salesforce professional, he is recognized for designing scalable, secure, and high performance enterprise solutions. He plays a key role in guiding architectural decisions, ensuring compliance, and driving business value through robust and innovative CRM implementations.