ClemRisk guides you through the entire risk management modelling process: from data quality assessment trough missing and outlier data management, data transformations, test environment design, selection of modelling technique and building models to comparing models, evaluation.
CLEMRISK is a solution that builds "best practice" credit risk scorecards for financial companies combining data mining knowledge and professional's experience. CLEMRISK includes automated processes for providing Basel II risk parameters for calculating the minimum capital requirements. CLEMRISK uses a wide range of modelling algorithms from the IBM SPSS Modeler/Watson Studio model suite, which includes a number of models ranging from regression-type models to decision trees and neural networks.
When a new loan application is made, we assess the risks involved in the client or transaction, rate the client and decide on the acceptance or conditions of the application.
We continuously monitor the behavioural patterns of customers (e.g. late payments) over the life of the loan and determine the risks of the transaction. The risks are used to calculate the expected loss, which is a determining factor in the provisioning process.
It's important to integrate and automate the solution into everyday business processes, from data transformation to automated reporting and customer classification.