SiteData.io provides a Locality Health Score (LHS) feature that utilizes machine learning and socio-economic data to enhance credit risk models for over 60,000 U.S. cities and neighborhoods. By predicting loan principal recovery rates, LHS enables credit professionals to optimize loan portfolios, potentially reducing principal loss by up to 30% on high-scoring loans.
Funding
Funding not disclosed
Founders
Product
Problem
Credit risk models often lack granular insights into local socio-economic factors, leading to inaccurate risk assessments and suboptimal loan portfolio management. Traditional models may fail to capture the nuances of specific geographic areas, resulting in increased principal loss and reduced returns.
Solution
SiteData.io offers a Locality Health Score (LHS) that leverages machine learning and socio-economic data to provide detailed credit risk insights for over 60,000 U.S. cities and neighborhoods. The LHS feature predicts loan principal recovery rates, enabling credit professionals to optimize loan portfolios and maximize returns. By incorporating LHS into credit risk models, lenders can better assess the creditworthiness of borrowers based on the economic health of their location. The historical and current LHS scores are generated by ML/AI models fine-tuned to mirror the expected principal recovery percentage of loans issued in localities with varying socio-economic conditions.
Target Audience
The primary customers are credit professionals, lenders, insurance companies, property investors, asset managers, and marketing firms seeking to enhance risk assessment and decision-making through location-based insights.
Features
- ML/AI models that generate LHS scores based on socio-economic data for over 60,000 U.S. cities and neighborhoods.
- Historical and current LHS datasets available by city, neighborhood, and 3-digit zip code.
- Ability to calculate LHS for specific US Census tracts and neighborhoods given a street address.
- Models do not include any featurization of geographic location, race, color, religion, national origin, sex, marital status, or age.