Problem
Creditt receives a massive flow of 20,000 loan applications per month. Each application takes 20 minutes to process manually, leading to a monthly backlog of 18,000 loans. The inability to efficiently process applications resulted in delays, increased workload for loan officers, and customer dissatisfaction due to prolonged approval times. Creditt recognized that leveraging AI to predict loan defaults could significantly reduce their backlog.
Solution
Creditt partnered with Zams to develop their AI models. They were assigned a Dedicated Data Scientist with expertise in banking and fintech, ensuring a deep understanding of their industry-specific challenges. The Dedicated Data Scientist provided strategic consulting on industry best practices and outlined the optimal approach for building a loan default prediction model. Once approved by the Creditt team, he prepped the data and built the AI model in just 2 days. After development, Creditt’s team integrated the model into their app, enabling real-time loan default predictions for every submitted application.
Outcome
Creditt automated the loan application evaluation process using Zams, reducing the time required to process each application from 20 minutes to just 5 minutes. By streamlining the approval workflow, Creditt eliminated over 208 calendar days of backlog per month, significantly improving operational efficiency. Loan officers can now focus on higher-value tasks, reducing manual workload and improving overall decision-making speed. By leveraging Zams' expertise, Creditt has transformed its loan processing operations, setting a new standard for efficiency in the fintech industry.
