For risk managers, using big data and risk analytics provides an unprecedented ability to identify, measure, and mitigate risk.
Use machine learning algorithms to identify high risk customers and reduce charge-off losses by screening for risky deals.
Adopting a clear, consistent scoring policy enables credit managers to compare customer performance and historical trend analysis without pattern exceptions. Most importantly, individual credit scores can be used to segment customers.
Place each segment in the right workflow and create the best actions for each customer.
Using features such as the latest announcements about an organization, their quarterly revenue results, etc., machine learning techniques have the potential to uncover patterns and insights that we haven’t see before, and these can be used to make unerringly accurate predictions.
The solution augments the capabilities of human investment managers by predicting future performance under various economic test conditions.
Subrogation is a term describing a legal right held by most insurance carriers to legally pursue a third party that caused an insurance loss to the insured. This is done in order to recover the amount of the claim paid by the insurance carrier to the insured for the loss.
The solution automates the determination of the subrogation potentiality by inferring the adjuster notes of a case.
The Credit Risk Modelling solution helps banks and financial institutions to access and administer present and future credit risk exposure across various assets classes.
The solution helps to considerably reduce the rate of defaulters.
The document retrieval system creates articles and blogs from millions of internal as well as external documents (PDFs, business and financial news, reports, etc.) by querying the corpus and retrieving relevant paragraphs and sections from reference materials.
An effective document retrieval system results in a huge boost in productivity
