ZIRA Credit Risk Management
Reduce Net Bad Debt Expenses & Increase Top-line Revenues
ZIRA Credit Risk Management provides the operator with a holistic view that helps in understanding subscriber/partner risk profile and thereby aids its management. Further, ZIRA 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
Increase top-line revenues
ZIRA Credit Risk Management is part of ZIRA 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.
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.
Partner / Subscriber Behavior Lifecycle
End-to-end 360° View
Streamline your Processes
Bill Shock Prevention
AI Continuous Learning
Risk Exposure Detection
Customer Hierarchy Credit Limit
ZIRA Credit Risk and ZIRA FM Advanced Scoring are additionally enriched in ZIRA Risk Management with following algorithms
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 ZIRA’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. ZIRA FM use RMR algorithm to generate a relatively large number of rules, which are then pruned. Additionally developed fuzzy set theory in ZIRA 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).
Fra Anđela Zvizdovića 1
UNITIC Business Tower A/5
Sarajevo, Bosnia & Herzegovina
For over 25 years, ZIRA has been a leading vendor of innovative BSS solutions for customer, revenue and risk management covering the full order to cash process for retail and wholesale billing.
Working with 50+ telecommunication operators across 30 countries, ZIRA implements integrated and flexible modular solutions to protect legacy investments, reduce the risk and cost of implementation and meet customers’ unique needs.