Credit Risk Scorecard
Fraud and Risk Management
Credit risk modelling is an approach to identify high risk customers. Credit scoring is based on analysing customer’s personal information, transactional information, last premiums and many more variables providing a unique score reflecting the creditworthiness of the customer.
With credit scoring, TransOrg helps in building customer application evaluation, collection strategies, cross/up-sell models, customer acquisition and retention strategies.
We help our clients make strategic decisions resulting in sizable impact
Data Sourcing
Data stitching from various sources
Understand the data schema and data dictionary.
Data Pre-processing
Access data quality gaps and data sufficiency.
Perform vintage and roll-rate analysis, if needed.
Data Transformation
Perform variable transformation and correlation tests for multicollinearity.
Feature selection based on variable significance and intelligence value.
Model Fitting & Validation
Model methodology specification and implementation
Build machine learning risk scoring classifier model
Risk Scorecard Validation
Carry out scorecard validation and out of time validation
Perform reject inferencing, if needed
Monitor Model Performance
Review Model approach
Access model implementation
Want to learn more about TransOrg’s value proposition solution methodology and implementation approach?
Unique Range of Benefits
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1
Superior Customer Experience
Improve customer experience and build strong relationships through customer analytics and personalization leading to high customer acquisition and low churn
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2
Fraud and Risk Management
Ward off potential fraud by analyzing customers’ behavioral patterns and flagging anomalous behavior to prevent losses and possible reputational damage
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3
Growth Opportunities
Identify high-value customers to explore up-sell and cross-sell opportunities. Get market insights for profitable launch of a new product or expansion of existing product line
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4
Loss Mitigation
Mitigate loss via identifying high-risk customers to reduce delinquency, estimating credit loss, reducing churn and focusing on collection and recovery
Success Stories
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