The environment of Telcos has changed with growing functionalities to do banking and making payment transactions from mobile phones. This is forcing the Telco companies to think differently to plan strategic initiatives to overcome the major challenges:
- Organization structure lacks a dedicated department accountable for credit and risk related decisions.
- Absence of Integrated Data warehouse/ Data mart to have easy access of the data across the customer lifecycle.
- Standard KPIs not in place for performance measurement and benchmarking
- Absence of structured Reporting Tool and Management Information System
- Statistical scoring Models and Analytical Framework not in place
- Risk segmentation strategies not defined appropriately
- Legacy systems for Vetting, Customer Management, Collections and Fraud
- Integration with Fraud Control.
Organisation structure lacks a dedicated department accountable for credit and risk related decisions.
The organisational structure of most telecom companies does not have an independent dedicated Credit and Risk management unit to design policies and processes on Vetting, ongoing Customer Management, Collections and monitoring them on an ongoing basis. The best practice approach is to have distinctive advantages of end to end lifecycle picture, balanced view on accounts and customers, profitability based model for acquisitions and customer management, understanding of legal and compliance issues, long term strategies based on economical, market and customer trends.
Overall risk ownership is missing throughout the customer life cycle of Vetting, Customer Management, Collections and Fraud. It is observed that the mentioned functions and processes are independently managed by the respective departments and not accountable for any gaps i.e. in case there is a gap in the vetting process, a wrong account has been activated, a deactivated account reactivated etc. the respective departments will be held responsible, but there is no dedicated Credit and Risk department to set risk triggers on critical processes and work as third eye to monitor the entire customer life cycle.
Absence of Integrated Data warehouse/ Data mart to have easy access of the data across the customer lifecycle.
A dedicated credit Data Warehouse (DWH) / Data mart) that should include all the customer information which is relevant for risk and exposure measurement, monitoring and management at subscription/ customer and portfolio level. This DWH should act as the primary source to feed all Vetting, Credit management and Collections processes
Standard KPIs not in place for performance measurement and benchmarking
- The organization should have standard KPIs defined separately for every phase across the customer life cycle E.g. for each portfolio and segment vetting decisions, Offers and Terms of Business, Credit Control (re)-organization, detailed operational and strategic KPIs optimizing Customer Life Time Values, revenues adjusted on Risk measures etc
- This organization should be fed with enhanced analytics and simulation capabilities simulating expected outcomes of changes in credit strategies at all levels: portfolio level but also every customer sub-segment and offer level
- The set of reporting KPIs should be able to cover both the business risk control at portfolio level and the operational performance e.g. performance of risk factors driving the decision, effectiveness of policy rules, effectiveness of scorecards and score ranges ageing reports, number of never paid customer by registration month etc.
Absence of structured Reporting Tool and Management Information System
The absence of a structured Reporting Tool and MIS results in the manual generation of reports. This is highly time consuming blocking valuable resources thereby delaying the decision making. Implementation of a comprehensive reporting tool to support both pre-defined and bespoke reports in an automated environment will minimize the report generation time and invest the same for analysing and taking strategic decisions.
Statistical scoring Models and Analytical Framework not in place
The current risk assessment criteria is based on expert rating models and a number of policy rules determining the decision on Accept, Decline, Referral and Deposit strategies. However, there is no statistical model in place for scorecards and decisioning. The most effective best practice is that the overall system (models, rules and strategies) should be assessed, optimized and monitored regularly following a clear analytical and statistical framework for each phase of customer life cycle.
The strategy design should also measure and balance on agreed strategic KPIs: Risk, balance usage, churn in order to ensure and help the organization achieve its objectives. Greater confidence in the system will lead to greater automatic decisioning (and then less manual intervention) and more effective strategies.
Risk segmentation strategies not defined appropriately
The credit segmentation model of most Telcos seems too unsophisticated to deal with the current complexity of customer base and with the growing pressure of bad debt. A statistical predictive risk behavioural scoring would allow greater differentiation in most of the credit management processes (e.g. Limit Setting, High Spend) improving their effectiveness (i.e. reduced bad debt through reduced exposure to riskier customers and reduced operational cost through reduced manual activities) and improved customer experience (i.e. reduced voluntary churn). This segmentation shall drive CRM cross-/ up sell campaigns, product and tariff planning, retention strategies etc. Based on local legislation if possible, the credit bureau scores can be incorporated for a robust model based on internal & external info.
The collections scoring models are mostly expert rating systems and not statistical in nature. To effectively manage the collections portfolio and optimize the resources, it is important to have statistical scoring models beginning 1Dpd onwards predicting the risk on the account/ customer level. Those accounts with no prior history will be worked based on the risk scores at application level and behaviour level. The segmentation model by customer groups should include risk assessment of “High”, “Medium”, “Low” and “Premium”
Adopting a finer segmentation of collection paths and actions based on risk/exposure, an organization can achieve:
- larger collections/ recovery rates in less time (through tailoring actions to each customer’s specific profile)
- lower operational cost reducing manual and ineffective activities
- reduced churn rates from low and mid risk customers disgruntled by inappropriate collection treatments
- reduced disconnections and barring of outgoing calls adopting softer strategies for low risk customers and allowing them to self cure.
Account level Management to Customer Management
Transition from the existing account/ subscription management model to the best practice model of customer management that incorporates risk assessment at account and customer level thereby driving credit limits management to optimally manage the portfolio for maximizing revenue and minimizing losses. The customer management system will introduce adequate usage management (to prevent Bill Shock).
The best practice model is to calculate and assign initial customer level limits at the time of application (based on application scores on risk assessment) and manage the same as ongoing customer credit management process throughout the contract period. The risk behavior scores will drive the ongoing limit management process to increase/decrease the limits corresponding to the risk levels. Business objective will remain to limit losses without affecting real usage spends.
Legacy systems for Vetting, Customer Management, Collections and Fraud
The systems for Vetting, Customer Management and Collections are legacy systems and mostly disintegrated from one another thereby breaking the flow of the information during the customer life cycle. The legacy systems also lack flexibility and the scalability for changes in future to manage the increasing volumes.
The system should be modern enough to include Champion Challenger functionalities i.e. Implementation of proper tests, control and documentation process (i.e. random or specific sample selection – monitoring of outcome – implementation of successful strategies) within the current test environment. The system must have simulation capabilities to simulate expected outcomes of changes in Vetting, Customer management, Collection policies and strategies at customer and portfolio level for future existing, better or worse case scenarios.
The functional objective of the Customer Management Solution for Telco’s is to deliver account level solutions that can be used to effectively manage account relationships aggregated at customer level and automate many current decision making processes. The customer management solution should offer:
- Sophisticated customer and portfolio management technology.
- Powerful linking capabilities to provide holistic customer view – over time.
- Powerful analysis and optimization.
- Regulatory compliance management.
- Base reporting and insight.
- Up-selling the right service at the right time to the right customers.
A standard dedicated and automated collections system using end-to-end work flows and full customer view to manage all delinquent cases in-house and also for the DCAs using multiple account treatments, driving or supporting operator activity. This is to ensure standardization and proper controls in the hands of Business. E.g. Historical actions of DCAs will be visible to the Business; status of No contacts/ wrong phone after numerous attempts can be tried for different treatment; Return post info. will be visible to get the correct address.
Integration with Fraud control
The focus of the Fraud department in Telcos is mostly on Post-activation fraud. The best practices emphasize to tighten the processes to control any fraudulent activity (identity fraud etc.) at Pre-activation stage of application processing. Early Warning Indicators in collections to identify post activation potential fraudulent customers and escalating the same to the Fraud department. E.g. Accounts/ Customers with just less that 3 mob(months on books) and missing the 1st/ 2nd payments. Never payers or 1-2 payments and then never paid together with Return post etc.