N2B Risk Management

  


N2B Credit Risk Management

 

N2B Credit Risk Management provides the operator with a holistic view that helps in understanding subscriber/partner risk profi­le and thereby aids its management. Further, N2B Credit Risk Management can quickly and seamlessly, accommodate new service information to provide an accurate picture of the exposure at any point in time and help its Customers to:

 

  • Reduce net bad debt expenses
  • Lower costs from credit and collections operations
  • Improve cash flow
  • Retain subscribers/partners
  • Increase top-line revenues

N2B Credit Risk Management is part of N2B Framework of tightly integrated, business focused applications that quickly and accurately transform under-exploited data from disparate sources into competitive opportunities for revenue protection, optimization and generation.



Benefits
  • Enable Operator to understand subscriber/partner behaviour across the entire credit lifecycle
  • Use quantitative analytics to understand and manage trends within your portfolio
  • Create environment of continuous learning through “test-and-learn” discipline
  • Provide 360° view of any Customer and Partner Operator and complete  understanding of customer/partner risk profile
  • Align business processes, technology, and organization structure
  • Customer specific approach and design risk exposure detection and monitoring
  • Proven increase of profitability due to minimized customer risk exposure
  • Prevent bill shock to customer, reduce churn and manage retention through increased customer loyalty and satisfaction
  • New service offering – Credit Limit applicable on any Level in Customer Hierarchy
Key Features
  • Continuous detection and assessment of subscribers’/partners’ usage (e.g., events from switches) and non-usage (e.g., payments, deposits), triggering alarms
  • Credit management capabilities extended to include dynamic credit limit management, credit classification, “balance due” management including late payment notification
  • Credit risk scoring is based on demographic information, usage behaviour and, if desired, other sources such as internal systems or external credit score rating companies.

N2B Credit Risk and N2B FM Advanced Scoring are additionally enriched in N2B Risk Management 6.0 with following algorithms: 



Random Forest

The aspect of variable importance ranking, which estimates predictive value of variables by scrambling the variable, in practice is used to test (one way) interaction effects and added to  model, resulting in performance which matches (or outperforms) random forests.



Ranked Multi-Label Rule (RMR) algorithm

In N2B’s approach to RMR algorithm a new associative classification technique are introduced, which are generating rules with multiple labels. RMR algorithm  has been developed specifically to address the problem of finding best scores within very large and sometimes noisy datasets.

N2B FM use RMR algorithm to generate a relatively large number of rules, which are then pruned. Additionally developed fuzzy set theory in N2B dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. The fuzzy sets are logically true formulas recursively enumerable (in spite of the fact that the crisp set of valid formulas is not recursively enumerable).


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