Abstract: A credit risk decision management system and method using voice analytics are disclosed. The voice analysis may be applied to speaker authentication and emotion detection. The system introduces use of voice analysis as a tool for credit assessment, fraud detection and a measure of customer satisfaction and return rate probability when lending to an individual or a group. Emotions in voice interactions during a credit granting process are shown to have high correlation with specific loan outcomes. This system may predicts lending outcomes that determine if a customer might face financial difficulty in near future and ascertains affordable credit limit for such a customer. Information carrying features are extracted from the customer's voice files, and mathematical and logical transformations are performed on these features to get derived features. The data is then fed to a predictive model which captures the probability of default, intent to pay and fraudulent activity involved in a credit transaction.