The Automation Miracle in Debt Collection Processes

Press Release
GDS Modellica
Data analytics and automation are vital to obtaining payments, influencing customer behaviour and maintaining customer relationships in the debt collection management process.
Based on its in-depth knowledge of the sector, GDS Modellica generally differentiates between three phases in the collection process: early-stage collections (delinquency period) (30-60-90), mid-stage collections (120-150-180) and the final stage collections (charging-off period), from six months onwards.
In Spain, the financial situation is marked by uncertainty and the increase in non-performing loans with late payments, more particularly when the volume of doubtful loans in October 2022 was 46,048 million euros, with a year-on-year rate of 13.06% (October 2021) according to the Bank of Spain’s records. The NPL ratio for consumer credit at deposit institutions stood at 4.9% in the second quarter, a far cry from the 5.6% reached before the health crisis. The consumer NPL ratio represents 0.3% of the total credit figure. Many entities keep these loans off their balance sheets as they present a higher risk of default. According to financial credit institutions (EFC in Spain) that provide consumer credit, the NPL ratio stood at 6.35% in October 2022. The delinquency rate in consumer credit is always higher than in mortgages, as it is a category that represents a higher financing risk.   In debt collection management, data analytics is critical to obtaining collection, influencing customer behaviour and maintaining customer relationships. Until recently, institutions were using outdated and inflexible tools and manual techniques; today, the process has been completely transformed. Even so, developing the infrastructure to automate the process takes a lot of work, so many institutions outsource this activity to improve their operations. Quantitative data management and the rise of automation have greatly facilitated the collection process. Quantitative information is essential; the underlying data enables the segmentation of customers to know the reasons or causes of non-payment and the possibility of retrieval. GDS Modellica, based on its in-depth knowledge of the sector, generally differentiates three stages:

  • Early-stage collections, known as the delinquency period (30-60-90 days)
  • Mid-stage collections, for assessing the level of risk (120-150-180 days) 
  • Final stage Collections, known as the charging-off period, from six months onwards
  Each of them has specific objectives and solutions that vary in decision-making strategy depending on the particular stage. In the early stages, it is necessary to improve communication channels with the customer, to reduce friction, and offer alternative methods, which will favour the evolution of the customer’s intention to pay.
Efficient integrated management of collections is necessary to establish the strategies to follow to be successful. GDS Modellica has the “Modellica Collection Suite” solution for collections, which facilitates the task by providing accurate customer segmentation, identifying collection actions, automating decisions and increasing customer loyalty through higher-quality interactions. The firm highlights the value of the data, precisely the information obtained after analysis. This knowledge allows identifying patterns to predict behaviour, which is a solid basis for applying the appropriate measures that lead to greater efficiency, improved service quality and security.

Advanced analytics and machine learning make it easier for banks to learn more about their customers and identify their level of risk. Automation is essential to manage and monitor collections’ status in real time. Efficient management for collecting debts relies on data analytics and automation: they are the cornerstone of debt collection. Data analysis provides essential information for planning and establishing strategies to ensure success and, thereby, customer loyalty. Having the right strategies and analytics in place is fundamental to move in a favourable direction. Financial institutions must prevent and anticipate, implementing mandatory predictive tools and policies to provide an adequate response before defaults occur. Prevention is the best cure in collections management systems. The goal is to collect debts in less time and at a lower economic cost.
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