Abstract: Systems and techniques for developing and implementing a credit risk model using various sources of data, including price data, financial accounting ratios, ESG (Environmental, Social and Governance) data, and textual data are disclosed. Each source of data provides unique and distinct information about the health of an entity, such as a firm or company. The systems and techniques combine information from disparate sources to create a uniquely powerful signal. The systems and techniques can be used to predict a number of events including, but not limited to, probability of default or bankruptcy, loss given default, probability of rating agency rating change, and probability of equity price moves.

Abstract: Systems and techniques for developing and implementing a credit risk model using various sources of data, including price data, financial accounting ratios, ESG (Environmental, Social and Governance) data, and textual data are disclosed. Each source of data provides unique and distinct information about the health of an entity, such as a firm or company. The systems and techniques combine information from disparate sources to create a uniquely powerful signal. The systems and techniques can be used to predict a number of events including, but not limited to, probability of default or bankruptcy, loss given default, probability of rating agency rating change, and probability of equity price moves.