Making a credit decision involves a risk assessment process whereby
numerous pieces of information are being sourced from customer applications,
credit bureaus, open banking data, and other third parties. Banks use this data
as input for scoring models and credit policies to ensure that the requested
financing options are within risk appetite. This is very much an automated
process, however very often there is a need for skilled credit analysts to have
a final say in the approve-or-decline decisions and agree on the limits, rates,
and terms. Across the credit lifecycle (after account origination),
transactional data, behavioral scores, and other customer performance data are
used for making account management decisions, such as limit modifications and further
product offerings.
Credit Risk Decisioning systems are crucial for evaluating the creditworthiness of borrowers. By leveraging data like credit history and repayment capacity, these systems ensure responsible lending and are integral to the credit lifecycle. Modernising these systems can significantly enhance their accuracy and efficiency, improving the bank’s competitive edge.
Many credit providers still use outdated legacy systems, which pose operational challenges and struggle to keep up with growing consumer demands. Modernising these systems is vital to maintaining responsible Lending practices, ensuring customer satisfaction, and meeting regulatory requirements. Failing to do so can result in financial losses, reputational damage, and regulatory penalties.
Our solution and consulting services are focused on making the credit decision processes smoother, smarter, and more scalable. We have merged our credit and lending experience with Pega’s advanced decisioning technology. We help to modernize and streamline the way in which credit decisioning teams operate and function. Your teams will be enabled to make accurate risk decisions, optimize credit policies and provide guidance to the credit business processes.
"How to Navigate through the Noise of Decisioning data"
