Underwriting Mission
In banks and financial institutions, Underwriting is the critical function responsible for ensuring portfolio quality—making informed approval decisions for customers applying for loans. The goal is to determine whether an applicant should be approved or rejected based on risk, creditworthiness, and profitability.
At AIGEN, our team spent five years in key leadership roles, including Director of Underwriting, and R&D, within international financial institutions. During this time, our mission was to develop and implement an AI-driven approval system that would revolutionize the decision-making process.
Our mission was a resounding success: by transitioning from a semi-automated underwriting process to a fully AI-driven system, we were able to reduce the company’s risk by 30%.
This achievement was made possible through advanced Big Data integration throughout the underwriting decision flow, enabling the creation of detailed customer profiles in real time.
Most importantly, we introduced a novel Machine Learning approach, specifically designed to meet the unique constraints and requirements of underwriting, delivering both precision and scalability.
AI Underwriting System
The AI Underwriting Methodology we developed is rooted in our Ph.D. research framework, with foundations in Bayesian Theory, Logit functions, Weight of Evidence (WOE), and Boosting Algorithms. This robust combination provides an intelligent and adaptive foundation for credit decisioning.
This approach allows for exceptional flexibility, even in challenging scenarios such as limited data availability or rapid market shifts—including emerging fraud patterns or aggressive competitor behavior.
Our AI-Driven Underwriting – UWAI – is a unique comprehensive tool that significantly enhances risk management by Reducing risk-related costs, Accelerating decision-making, Simplifying the process, Lowering operational costs, Increasing model interpretability.
We are prepared to deploy UWAI to any organization that requires highly accurate, fast, and scalable decision-making—especially where hundreds of variables must be evaluated in real time to make the best possible choices.