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."

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