Abstract: The shared lending risk system and method provide a lead scoring engine that generates a score representing an assessment of the credit risk of the transaction that is based on an assessment of the merchant, purchaser and product. The score is provided to a pool of lenders and a credit product is selected from the pool based on the score.

Abstract: An apparatus, system and method for automatically evaluating a transaction request are provided. An adaptive modeling platform builds models and deploys them in a very systematic manner, without requiring much or any human intervention. This system ensures an end to end data management; encompassing variable generation, model building and evaluation of the built models, decision logic and strategy design. It guarantees deployment of these predictive models in real time, monitors performance of the portfolio and generates reports & alerts for the same. The system periodically examines the models in production and rebuilds them when their performance falls below a predefined threshold. When the human discretion so permits, the system can be interrupted at any point in time and changes can be made wherever desired in the process.

Abstract: The system enables a merchant to choose the degree in which to participate in financing of a customer. This enables a new revenue stream for merchants beyond retail margin. The system also enables merchants to reach new under-banked consumers who were previously unable to buy from the merchant because they lacked the cash and/or credit to do so. The system also allows a consumer to participate in modification of credit terms by providing a customizable credit product.

Abstract: A method for calculating probability distributions and probability scores associated with a consumer credit score is disclosed. The probability scores may comprise a bureau volatility score, and bureau error score, and/or a multi-bureau variability score.

Abstract: An automated real estate appraisal system (100) and method generates estimates of real estate value using a predictive model such as a neural network (908). The predictive model (908) generates these estimates based on learned relationships among variables describing individual property characteristics (905) as well as general neighborhood characteristics at various levels of geographic specificity (906). The system (100) may also output reason codes indicating relative contributions (1009) of various variables to a particular result, and may generate reports (701) describing property valuations, market trend analyses, property conformity information, and recommendations regarding loans based on risk related to a property.