GenDa Architecture: A Conceptual Data Processing Framework for Accelerating Residential Mortgage Origination

Madhan Gopal Perumal

Abstract: In the realm of residential mortgage origination, enterprises face complexities in automating their loan approval process. Enterprises encounter challenges when designing an effective data architecture to accelerate the loan approval process. Manual processing of loan application has long served as the keystone for mortgage approval; therefore, the impetus for a transformative mortgage origination process is more profound than ever before. This paper introduces the GenDa architecture, a methodology aimed at enhancing residential mortgage origination data processing. In this paper we outline at a high level the integration of Generative AI with Lambda architecture to create the Genda architecture, a paradigm designed to reshape and augment the design of data platforms for mortgage loan applications. This paper delves into practical implementation of GenDa architecture, the implementation concepts detailed herein may serve as a foundational guide for loan data processing, to automate the residential mortgage origination process. Additionally, this study aims to provide a roadmap for enterprises aiming to navigate the transition towards a more robust, innovative, and efficient residential home loan origination process.

Keywords: Lambda Architecture, Modular Data Architecture, Automating loan origination, Generative AI. Cloud Data Management. Residential Mortgage