Credit scoring and automated rules based decisioning are the most important tools used by the financial services organizations for credit lifecycle management in their consumer lending businesses for such products as credit cards, individual unsecured loans and lines of credit, and residential mortgages. Traditional “bureau” or generic credit scores, such as the FICO® score, and generic scorecards, built using traditional statistical (primarily regression based) techniques, have been used to improve the management of consumer lending portfolios for some time.

However, leading organizations in finance are pursuing new best practices that extend these generic scores, which are available to all lenders and do not yield competitive advantage, with more strategic use of their own customer and transactional data. Through application of data mining to their own data, and creation of proprietary scores and scorecards to target new opportunities for growth, these organizations are able to improve their ability to proactively manage and optimize "risk | reward" trade-offs for individual lending decisions, and across their portfolios.

Learn how Angoss predictive analytics systems can help in reducing risks of loss at every stage of the customer credit lifecycle, from acquisition and targeting, through origination and pricing, to account management and collections.