How VFPL Leveraged Account Aggregator Framework for Faster Credit Underwriting
VFPL, established in July 2022, is a financial institution offering small-ticket, secured loans to underserved rural and semi-urban populations within 100 km of Ahmedabad. To address credit challenges faced by low-income groups, VFPL prioritized operational efficiency and compliance while focusing on financial inclusion and literacy. Partnering with us as a Technology Service Provider (TSP), VFPL aimed to integrate India's innovative Account Aggregator (AA) framework for better credit underwriting processes.

Tech used

Zero Trust

Consent Management

Microservices

Open Banking
India
Challenge
Developing APIs and integrating them with multiple Financial Information Providers (FIPs) posed significant technical hurdles. Each FIP used different data formats and protocols, creating compatibility issues. VFPL, with its limited in-house technical expertise, found it challenging to manage these complexities effectively.
Additionally, VFPL relied heavily on manual processes for customer data collection. Customers were required to provide physical documentation, which was not only error-prone but also caused significant delays in the credit underwriting process. These inefficiencies created bottlenecks in customer onboarding and led to slow loan disbursements.
The manual system also raised concerns about data accuracy and compliance. Errors or tampered information in documentation often resulted in unreliable credit assessments. This lack of reliability increased operational risks and made compliance with regulatory requirements more difficult, further underscoring the need for a more efficient and secure solution.
Solution
To address these challenges, Cateina Technologies proposed transitioning VFPL from manual data collection methods to a digital-first approach. At the heart of this solution was the adoption of the Account Aggregator (AA) framework, which enables secure, consent-based sharing of financial data. This standardised framework eliminates the need for custom integrations, ensuring compatibility with all participating FIPs and significantly reducing technical complexity.
The AA framework automated data collection by directly fetching encrypted customer financial data from primary sources. This approach reduced VFPL's reliance on customer-provided documents, resulting in faster and error-free data acquisition. A digitized workflow was implemented to manage consent and automate data retrieval, streamlining the entire process from customer onboarding to credit approval.
With guided consent journeys, the system was designed to be user-friendly. Customers experienced a simplified process, while VFPL benefitted from reduced manual effort and operational costs. These innovations not only accelerated loan underwriting but also improved data security, accuracy, and compliance.
Result
The implementation of the AA framework brought transformative results for VFPL. By automating data collection and consent management, VFPL reduced data processing and loan underwriting times by threefold. This improvement enabled faster approvals and allowed the organisation to process a higher volume of applications with its existing resources.
The streamlined processes also enhanced customer experience. Customers no longer needed to gather extensive documentation, and the simplified consent flow made the process more accessible. These improvements helped VFPL onboard 928 satisfied customers, many from underserved rural and semi-urban areas.
Data integrity and compliance were significantly improved. Accessing encrypted and validated data directly from primary sources eliminated the risks of tampered or inaccurate information. Adherence to regulatory standards further strengthened VFPL’s reputation as a reliable financial institution.
The solution also supported VFPL's growth ambitions. By reducing operational costs and improving efficiency. The foundation laid by the AA framework ensures scalability and sustainability, enabling VFPL to serve more customers effectively.
.jpg)