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Regression Analysis for credit scoring and bad debt prediction
Integrated solution with data modeling, visualization, reporting
Manage big data with clustering, in-memory cache and NoSQL based OLAP
This combines multiple explanatory variables into a representative few for better understanding underlying phenomena, i.e. Exploratory analysis of data sets. The principal use cases include:
Here, we identify linear inter-relationships among variables during exploratory analysis. This is useful for producing pricing analytics
This finds close relationships between two sets of occurrences/events (identify market-basket), and is used for cross-sell/up-sell of products
Here we segment based on transaction behaviour or similarity of customer attributes, and works for Product Pricing Analytics and portfolio insights
This is a generalized linear model for classification of events used in Product pricing analytics, Credit scoring, Bad Debt prediction, churn analysis
A classifier which builds a decision tree based on historic examples basis which new cases can be predicted. This can be used for customer response to dunning actions and Churn Prediction
Crisil Ratings webinar: Ratings Roundup FY23, H1: Steady sailing in choppy waters
Steady ascent
For any risk solution related queries, please contact: +91 22 33428266 Sales.Birs@crisil.com