Is your borrower going to take the easy way of default or take a long-term view to maintain his rating? How is he going to deal with difficulty or crisis? Can you tell in advance?
Beyond financial records, rich sources of behavioral data extracted from public, online activities are available for mining and are incredibly accurate due to their scope, breadth, and up-to-date nature. We collect and combine all of these data points which represent unique insights to create a “crystal ball” to predict the efficacy of a loan, deal, partnership, or other interaction.
We scrape dozens of digital sources together with Albe’s proprietary application process.
Game theory methodologies enable fast analysis of unstructured data that other tools cannot analyze.
We use several algorithmic engines each based on behavioral economics models, game theory and mathematics that identify subtle but critical behavioral patterns.
We use AI\machine learning for a constantly learning AI platform making automated real-time adjustments.
Through extensive testing, we have determined that this matrix of patterns can be woven together to indicate specific personality types, accurately identifying high-risk individuals or dangerous deals.
The algorithm not only enables better profiling of credit applicants. It also delivers more efficient monitoring with real time credit risk analysis.
Albe's algorithm is protected under US provisional patent application "System and methods for credit underwriting and ongoing monitoring using behavioral parameters."