The Future of Debt Collection Management with Artificial Intelligence

debt collections.jpeg
Compartir en Redes

Debt collections strategies in contact centers remain complicated and inefficient. Inappropriate management of channels of communications, low optimization of processes and not having a unified vision of interactions affect the process. Besides, companies perceive this as costs given the fact that they can´t lower their delinquency rates.

Companies need to rely on powerful and innovative technologies. Such as automation, prediction, advanced analytics and artificial intelligence. These will help to transform current business models with accurate and not intrusive intelligent strategies. Focused on customer loyalty to forecast and lower risk behaviors. All of the above preventing their debts to escalate to other phases.

Predictive models: reinventing customer contact rate

Thanks to Big Data and Machine Learning, companies are able to explode all the information that they have. Designing algorithms with predictive models based upon that data. These analyze and establish all of the debtor´s possible patterns of behavior. Based on demographic, economic and social data, different collection strategies can be prioritized and applied based on the time and amount associated with each debt.

For example, depending on the age, salary, type of work of a person and historical interactions. You could establish probabilities of debt payment, being able to design the strategies in the most efficient and convenient way for your company.

Another possible application is by personalizing the interaction. Through the channel that is most likely to execute debt collection, for that person and that specific debt.

New models of debt collection

These predictive models also work with other innovative technologies. Technologies such as chabots and virtual agents, to automate collection processes. With the irruption of artificial intelligence, humanization of interfaces and communications in these solutions, it has become one of the main energizing elements of the activity.

The recognition and synthesis of voice by these technologies also provide companies with the possibility of offering versatile experiences. These are increasingly natural and similar to those provided by agents. They also convey the professionalism, confidence and closeness to which customers are accustomed, without judging them.

On the other hand, the ability to integrate digital channels with the traditional ones and combine artificial intelligence with machine learning allows creating alternatives adapted to each situation. Thus increasing the probabilities that debtors pay their debt.

Updating obsolete channels

As we have seen previously, one of the most important advances of these analytical and predictive technologies is their capabilities of customization to contact debtors through the most indicated channel according to the machine. This customization, applied to traditional channels such as SMS or email, requires reinvention to adapt to the required collection strategies.

For example, smart SMS, shown to customers as chat. They can guide you throughout the process and include links in the conversation in case you have to access an external platform. Regarding email, QR codes or bar codes are included. These facilitate the payment process for clients, by means of a simple scan with the Smartphone.

In this way, we achieve the reuse of channels considered obsolete are able to reuse them. These processes increase the chances of collecting debts. Being more friendly and quick does not imply the introduction of a multitude of data, hence preventing the debtor from leaving the process.

The next generation of collections will be based on Artificial Intelligence, predictive models and data analysis and the opportunities are clear. The contact centers have to start experimenting with these technologies to develop new ways of approaching their clients. Always keeping in mind the final objective: find solutions to optimize collection processes.

0 I like it
0 I don't like it

Leave a Reply

Your email address will not be published. Required fields are marked *