Paving the Way to Success: Exploring the Advantages of Digitised Debt Collection in Financial Institutions

Press Release
GDS Modellica
Technological Advancements in debt collection bring challenges and opportunities in an uncertain financial environment, offering specific interventions and improved outcomes.
GDS Modellica and its solutions result in a 25% increase in credits granted, a 78% increase in credits approved, and a 35% reduction in losses.
Digitisation is vital for effectively detecting the likelihood of customers defaulting on payments with increased accuracy. It involves realisation and understanding through advanced analytics, as well as the ability to incorporate information obtained from existing transactions. In this complex, much more demanding, competitive and uncertain environment, technology is a cornerstone of the financial sector that opens up numerous possibilities. By developing and implementing digitisation and embracing Artificial Intelligence and Machine Learning technologies, the financial industry has gained the ability to utilise algorithms and techniques that significantly enhance the success potential of credit risk management models.

According to the results of the 2022 Supervisory Review and Evaluation Process (SREP) published by the ECB, credit risk and internal governance remain key areas for supervisory action. ECB identifies many banks as under-resourced in all their control functions, including risk management, compliance, and internal audit. Additionally, some banks have not improved their risk data aggregation and reporting capabilities sufficiently, damaging data quality and their ability to generate non-standardised reports.

Incorporating technological tools and analytical elements guarantees the Debt Collection Process with more significant knowledge and optimal results. Debt Collection should be an integral management circuit that begins with forecasting non-payment and integrates anticipatory actions for specific customers with a high probability of default. Once the risk management cycle begins, technology facilitates the segmentation of customers according to their behaviour and efficient actions to obtain the debt collection or regularisation of outstanding balances: the greater the micro-segmentation, the more targeted and effective the interventions. These tools generate higher value, improve effectiveness and profitability. GDS Modellica is a prime example of this, as its expert solutions have achieved a remarkable 25% increase in credit approval and a substantial 35% reduction in losses.
According to GDS Modellica, proactive measures combined with early warnings facilitate a better understanding of customers through behaviour analysis. We emphasise the importance of implementing innovative tools that enable the adoption of tailored strategies for each portfolio segment with fundamental multi-channel communications. Messages must be carefully crafted and agile, offering proposals customised to customers’ individual circumstances, including their ability to make payments. In this regard, automation plays a crucial role in implementing corrective actions to address any irregularities and ensure that customers fulfil their payment obligations, such as sending reminders.

Customer contact is pivotal and prioritised in enhancing the debt collection process. As highlighted by Antonio Garcia Rouco, Managing Director of GDS Modellica, the ability to influence customer behaviour and foster a strong relationship is indispensable. This approach is essential for successful collections, as it transforms the situation into an opportunity that strengthens loyalty and further nurtures the customer relationship. Establishing a solid relationship fosters effective communication and yields multiple benefits, including improved debt collection rates and enhanced understanding. Such a relationship facilitates early detection of fraudulent activities or instances of non-payment. Implementing tailored strategies for each case and customer enables the identification of specific situations and determines the appropriate level of support or decisive action. This approach also allows for evaluating the effectiveness of implemented default indicators to gauge their success.

“The Value of Credit Risk Transformations and the Role of AI,” a study conducted by GARP and SAS, emphasises the significance of Advanced Analytics, Machine Learning, and Automation in shaping the future of banking. These technologies will provide crucial information and actionable data that will drive operations in this new era. The study highlights the importance of risk professionals possessing a deep comprehension of advanced analytics to integrate digitisation into their existing operational frameworks effectively. Banks that prioritise rapid returns on investments are already actively working towards achieving credit risk transformation at an accelerated pace. They recognise the critical importance of credit risk measurement and active credit risk management for their survival, driven by a sense of urgency to implement these changes.

Financial institutions must implement technologies and professional solutions that improve debt collections without neglecting their core business: customer service and profitability. Technologies and digitalisation, says García Rouco, “are a clear competitive advantage that simplifies and improves financial processes and management, including collections, which contribute to being more efficient, profitable and secure”.
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