Thanks to predictive modelling and data mining solutions becoming increasingly advanced, the loss of customers due to late payment or non-payment, whether on a one-time or recurring basis, as well as intentional, customer-originated account fraud, can now be accurately forecasted in a timely fashion, with a number of cases even allowing for full prevention.
Leveraging a company’s customer data and utilising analytical tools provides effective support to both risk and collections management on multiple levels, whether in the telecommunications sector, banking or public utilities:
Overdue account receivables and credit risk forecasting facilitate the proactive handling of receivables, and support both early warning and monitoring systems.
Service providers integrating predictive solutions for fraud investigation into their IT and CRM systems are better equipped and better prepared against misuse or abuse.
Debt management can be optimised by modelling customers’ willingness to pay through collections data, as well as by segmenting those customers who pay late and by analysing the collections process.
Account receivables and credit risk forecasting
By continuously analyzing customer data and their history, and using data mining models, customer segmentation and scoring-based evaluation methods, the early warning and monitoring systems can be perfected, even projecting changes in the payment behavior of certain customers. Through account receivables forecasting, service providers become able to take proactive measures, engage in meaningful dialogue with customers and successfully implement ”soft” solutions (restructuring, term extension, grace period, inclusion of additional security/collateral etc.) even before matters occur.
Fraud forecasting
Frauds, scams and other cases of misuse can result in enormous deficits and losses to service providers in the sectors of insurance and finance just as much as to administrative and healthcare providers, but malicious account fraud is of grave concern to a range of other industries as well. The application of data mining solutions can greatly improve the success rate of investigation. A fundamental task in fraud investigation is to establish a well-defined line between con artist and defaulted customer. Having identified past events of fraud, predictive models for fraud detection can be set up based on customer behaviour.
Optimisation of collections management
By segmenting and scoring the collections data, we can assess what would be the most successful type of collection process for a given customer (recognition of customers falling late with payments, lowering collection costs, selection of most affordable and most effective access channels, boosting customer satisfaction rates). Groups with a higher willingness to pay can be identified by means of various modelling processes. Collections analysis can also help to improve the process of receivables management.
