Empowering Student Futures With Underwriting Engines To Transform Education Loans
Introducing The Client
Our esteemed client, a leading financial institution, specializes in underwriting educational loans for postgraduate students pursuing studies abroad. However, they faced a significant challenge due to the limited financial and credit information of their student borrowers, hampering their underwriting processes. This challenge led to a collaborative effort to innovate and address these hurdles effectively.
Our client, faced challenges due to students having limited financial and credit history, making traditional underwriting ineffective.
To address sparse credit history, our focus shifted towards assessing candidates’ employability and employment potential, enabling more accurate underwriting decisions.
Client hesitations included concerns about the adaptability and complexity of the new approach, data accuracy, and the need for new data sources and tools.
Solutions That Redefine Lending Excellence
To overcome the challenge of sparse historical credit information for student borrowers, we shifted our focus to assess their employability and future potential. We expanded our data sources to include educational details, GMAT/GRE scores, work experience, and course details of their future educational programs. Despite the initial limitations in applicant differentiation, we gathered and curated publicly available data, such as school rankings, acceptance rates, course completion rates, employment rates post-graduation, average salaries, and proximity to major employment centres.
Using this comprehensive dataset, we created risk underwriting models that considered applicant credit bureau information, educational and employment history, and co-applicant credit ratings. Finally, we developed a decision engine that accurately rates applicants based on limited information, enabling more precise lending decisions and a reduction in credit risk for our client.
Impact That Transforms Lending Solution
Our scorecard distinguishes between low-risk and high-risk candidates, enabling precise loan-to-value and collateral assessments. Additionally, our decision engine achieved a remarkable 300% reduction in Turnaround Time (TAT) for loan approvals.
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