Essential Decision-Making Technology Critical for Success in Debt Collection

Press Release
GDS Modellica
Incorporating new procedures and technology tools enables management and decision-making automation and streamlines the debt collection process.
The deployment of decisional platforms is necessary to automate, segment, and optimize debt collection outcomes and manage risks in an advanced way, in order to meet changing customer behaviors and industry needs where immediacy, agility, and security are crucial.
Digitalisation and the implementation of new technologies in the financial sphere have entirely transformed the processes and components that interact; financial institutions had to respond effectively to users’ demands and new needs. The number of transactions and operations has multiplied exponentially. They are increasingly faster and take just a few seconds, enough time to conduct the appropriate analysis and validate the offer.

The Bank of Spain’s supervisory statistics on credit institutions reveal that, in the third quarter of 2022 in Spain, the percentage of non-performing loans (NPLs) of all financial institutions fell by four-hundredths compared to the previous quarter, standing at 2.63%. These data contrast with the warning message from the European Banking Authority (EBA) to banks, which have lowered their solvency levels when facing an uncertain future due to the price crisis and interest rate rises. In its annual risk assessment of the European banking system, it has detected that the level of banks’ capital has fallen in recent months and, in addition, the volume of loans classified as under special surveillance or stage 2, a step prior to being considered doubtful, has increased.
In this fitful scenario, technology and the development of solutions through applied software have their reason for being to improve the global process, thus reducing the default rate and increasing the debt collected. This better-integrated management of human resources and the costs associated with debt collection procedures was possible thanks to new data processing and automation tools that facilitate, among other things, the analysis and segmentation of customer data, as well as the identification and classification of debtors according to their probability of paying. Good segmentation dramatically facilitates management and intelligent decision-making.

In the case of the debt collection process, it helps to:

  • Perform better analysis of both debtors and risks
  • Make better decisions
  • Mitigate risks
  • Ensure a better return on investment in debt collection regarding time and cost.
To boost their profitability and growth, entities and firms must automate and optimise their operational decisions in order to leverage customer business value by responding effectively to markets and increasing business risks. The Decision Management Architecture platform developed by GDS Modellica provides a comprehensive rapid decision development environment for customers to customise, evaluate, implement and scale decision-making technology in a truly effective manner. Its extensive evaluation and segmentation capabilities enable companies to manage and automate operational decisions with high performance across processes, systems and channels, whether on-premise or in the cloud.

Antonio García Rouco, CEO of GDS Modellica, describes our decision-making process as follows: It performs calculations and executes formulas and models according to the variables determined to make safe decisions. Moreover, it applies to multiple products. The bank itself draws its own risk strategy and implements the decision-making process through tools and internal architecture.” In this sense, says García Rouco: “Organisations are adopting decision management technology because they need a higher return on investment in infrastructure, due to the increased complexity of business decisions, competitive pressure for more sophisticated decisions and ever-shrinking gaps in competitive advantage. Companies must improve the value created through each decision by deploying analytics solutions that better manage the trade-offs between accuracy, consistency, agility, speed and cost of decision making“.
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