AI against non-payments and defaulters

Gamco

Gamco Team

An article published in April 2021 by Óscar Jiménez El Confidencial, was titled "34 billion prize for banks for applying artificial intelligence well to credit". The text included the conclusions of a study by the Bank of Spain, which quantified the benefits of predicting defaults in the healthy credit portfolios of the banks through machine learning and AI.

34 billion euros may seem an exorbitant figure, typical of large Spanish banks, but, regardless of the size of a company, the objective is to predict in advance which customers may have payment problems, and act accordingly with this "privileged information".

Therefore, an SME, applying the possibilities of AI, could also control the risks of non-payment by its customers.

AI applied to your business to avoid customer defaults

Artificial intelligence and machine learning can help improve the proactivity of your business in terms of debt management, before the problem materializes.

In this way, mechanisms can be put in place to avoid debt avoidance or non-payment, such as visiting the customer to facilitate payments or adjusting the delivery of products or services to the customer's ability to pay. 

The basis of AI operation is learning from historical analysis. In this sense, AI is not limited to "memorizing" the history, since, on rare occasions, the same situation will be repeated (two customers with the same purchase history, payment history, etc.).

Artificial intelligence learning makes it possible to distinguish which customers start to behave like those who eventually end up having payment problems. In this way, alerts can be created that warn of the deterioration of this customer.

software Artificial intelligence against non-payments and defaulters

So if a bank can benefit from the advantages of AI, so can its business. For any company that sells on credit, i.e. does not get paid at the same time it issues the invoice, it would be useful to be able to predict payment problems for its customers before they materialize. 

Solutions based on software in Artificial Intelligence to automate the prediction of non-payments in medium-sized companies make the people who manage them much more efficient and productive, as they do not focus exclusively on "chasing" non-paying customers. 

The implementation of these solutions in SMEs does not necessarily have to be complex and costly. To do so, only two issues need to be resolved:

  • Solve the business problem. Based on the data provided by the SMEs, the aim is to develop predictive models that warn of future customer defaults and with an acceptable range of error. 
  • Solving the problem of access to AI techniques. The best way to do this today is through software services (SaaS), which provide a reliable and secure service, with no impact on the current systems of the companies and with a pay-per-use or fixed monthly fee.

The two previous points are solved with software solution providers that have solutions in this area. It is true that these providers are scarce for the SME universe, but the prize will be very large: avoiding most of the non-payments without worsening the service provided to customers. And this can only be done thanks to the capabilities of artificial intelligence.

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