A new approach to collection management with an increase in late payments

With late payments predicted to reach 15% as a result of the pandemic, according to the Bank of Spain’s annual report, there is an urgent need for banking organisations to implement new collection management strategies.
The tools offered by GDS are a good investment for organisations seeking to manage credit risk and late payments in a comprehensive, customised and effective manner.
The term ‘collection management’ refers to the recovery of fees owed by customers due to their credit obligations. The Bank of Spain has warned that, in this current period of economic uncertainty, both in homes and businesses, the number of bad debts and non-payments could multiply. The outlook is not promising. The rate of late payments is currently around 7% and is predicted to rise to 15% by the end of the year (the worst rate since records began), much higher than the 4.8% seen in 2019. This is a devastating figure that could cause serious problems for the financial system, and banking organisations will have to do all they can to minimise the impact on their bottom line.
Combat late payments effectively with GDS Link's collection management tools. Optimize credit risk strategies and reduce costs with intelligent solutions.
Collection management strategies are often complicated and ineffective due to inadequate use of communication channels, poor process optimisation and the lack of a comprehensive view of interactions with the customer. In fact, due to the inability to reduce late payments, for many businesses, they simply become accepted as another operating cost. To address this, GDS Modellica offers customisable tools and intelligent strategies that provide complete solutions and improve existing applications to comprehensively manage and reduce risk through behaviour pattern analysis. Their services make it possible for any organisation to optimise and automate their credit risk management strategies and policies.
Financial organisations can simply not afford to accept the late recovery or non-recovery of non-payments, and in the current economic situation caused by the pandemic, businesses are driving structural changes to address this. These include the incorporation of automation, prediction, advanced analytics and artificial intelligence into their management tools with the aim of increasing customer loyalty but also optimising collection management to predict and reduce risky behaviours. This way, banks can more effectively and precisely prevent late payments.
When awarding credit to customers and businesses, it is key for banking organisations to use predictive analytical strategies with personalised, up-to-date information. This way, they will be able to provide loans without compromising their viability and keep risk within controlled parameters that make it possible to establish early warnings and detect possible non-payment situations before they arise. In the words of the managing director of GDS Modellica, “it is important to put in place robust liquidity and lending mechanisms, as well as enhance credit risk management processes, review business continuity plans and manage processes in a comprehensive, joined-up manner”.
The banking industry needs to make a significant investment in incorporating AI technology, Machine Learning and other software in different areas in order to acquire valuable information that can later be used to improve customer service, business relations, after-sales services, the user experience and credit risk management in general. For example, the GDS MODELLICA Behavior Engine (MBE) evaluates the borrower’s credit situation based on previous behaviour and predictions made using all available information about that person or SME. The system then periodically identifies the total line of credit that will be extended to an account or borrower and enables the management of limits and lines for each product. It is also capable of accurately identifying the most valuable customers or those with the potential to become important customers.
Automating credit processes and operations is key since it allows organisations to analyse potential scenarios using accurate, real-time data and evaluate credit risk before awarding a loan. Such new collection management techniques are optimised, agile, fast and customisable and allow the process to be monitored more comprehensively. Investing in the right management strategies is the most effective way to reduce late payments, improve the recovery of non-payments and ultimately, reduce costs.
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