Toronto, Canada, April 12, 2017 --(PR.com
)-- If you are a supplier that offers credit terms to new customers, CogniFrame has a solution for you.
CogniFrame users benefit from the power of adaptive learning and dynamic ratings Traditional credit models are algorithm based and largely static. They do not benefit from adaptive learning; the ability to constantly learn from every decision made and every repayment received. Credit rating solutions provide standard personal or business credit scores based on aggregate data. CogniFrame uses an alternative method that combines adaptive learning with personal and business application data into one simple easy to use model that helps lenders make instant decisions on credit requests from new clients who request credit terms. CogniFrame rating models are dynamic and client ratings are constantly updated as repayment information is received.
The solution is available free for 30 days. Go to http://cogniframe.com/lenders/ to sign up and try the machine learning based alternative to traditional credit decisioning solutions.
CogniFrame Inc. is a machine learning, intelligent data analytics and predictive framework. It is a Cognitive as a Service (CaaS) based platform that uses machine learning algorithms to offer Autonomous real-time approval or rejection of loan applications leading to consistently optimal decisions besides driving profitable growth and operational cost savings for lenders on an ongoing basis. Its algorithms help predict delinquency probability of an applicant using historical patterns coupled with new data and outcomes that helps train the machine learning algorithms and improves its predictive ability over time. CogniFrame’s cognitive predictive engine is based on millions of recent historical credit applications and repayment data. CogniFrame detects patterns and anomalies based on certain predictor variables found in a borrower’s credit profile. CogniFrame can produce results with high accuracy with or without credit checks. Additionally, it is able to tailor decisions within the framework of corporate objectives such as reduction in delinquency, increasing loan volumes, etc.