More than a third (34%) of decision-makers admit they’re now struggling to get a complete picture of indebtedness or identify financially at-risk customers. Analysts warn many lenders are face financial exposure to spikes in non-performing loans, over-commitment, debt and default, and insolvencies are forecast to increase by up to 26% by the end of this year, as the ongoing pandemic continues to hamper recovery and growth.
The transition from a reactive environment to pro-active, pre- and early collections activity to help cut the flow of cases into the collections system.
From reactive to pro-active
An effective Early Warning System (EWS) covers the preventive and very early collections phase within the customer life cycle. It allows businesses to pro-actively identify and contact customers who are up-to-date or have just became past due, but that have a high risk of becoming delinquent or seriously delinquent in the very near future.
Ahead of a payment deadline, warning indicators are created and distributed across the risk management systems to help prompt pro-active contact with the customer.
Typically, this takes the form of a courtesy call to the customer to ensure they are okay and to gently remind them payment is due. Any additional options available can also be outlined, if they’re unable to pay.
A successful sensitive treatment of customers at an earlier stage of the process. is more likely to encourage payment and prevent the account being passed to collections managing.
See how it works in practice:
3 key considerations for optimum Early Warning strategy
1. Don’t rely exclusively on internal data
Many organisations rely solely on internal information as the only perspective they have on a customer. Some of the information is provided at the point of application and may be out of date when needed at later stage.
Including relevant external data sources will enable a robust and comprehensive risk assessment. By using a mix of insight from credit bureau information, to geo-marketing data and macroeconomic factors, will provide even more data variables for effective modelling.
2. Use advanced analytics to enhance strategies
Development of a robust model, which combines internal and external data, can enable a rapid response to changing behavior and offer enough time to take pro-active preventative action for high potential risk customers.
Predictive models used in EWS are set against a relatively short outcome period, but for an effective early warning solution, is important to use advanced analytics to identify key data attributes that show correlation with default or stress scenarios.
3. Improve your customer experience combining Analytics with automated decisioning
This will enable your organization to treat each customer as unique driving segmented treatments such as self-cure, pay, move to recovery agent or debt sale.