GUATEMALA CITY (CentralAmericaData) – The use of predictive models based on artificial intelligence processes and automated collections are some of the changes that companies are already applying in this new reality to reduce operating, management and risk costs.
The spread of COVID-19 changed the rules in almost all markets and business models, a situation that has affected the collections departments of companies, whose work teams are currently facing complex challenges.
Making decisions based on predictive artificial intelligence models and, in parallel, developing automated and self-service collections capabilities, allow reducing operating costs in this scenario in which customers have changed their behaviors.
Martesfinanciero.com reviews that “… by moving away from the traditional way in which collections agents call customers to collect and the use of analytics technologies, we can obtain better collections management: more and better results, improve the performance of collections teams and increase customer satisfaction even in a post-COVID-19 world.”
The discussion of improvements in collections operations arises in a context in which several companies in Central America have faced situations of late payments from customers.
According to the article, companies that are ready to expand the use of predictive and prescriptive analytics for their collections functions should consider the following four reasons:
“… 1. Improved customer service. Predictive models can also help improve customer service in several ways. Allowing banks to give lower risk customers more time to pay, phone calls and letters to customers can also be less frequent, which will result in fewer complaints and less pressure.
2. Optimization of collections processes. Analytics can help to improve processes, collections strategies and the different treatments that are decided to be applied, allowing the measurement of the effect of individual changes in collections strategies and check the effectiveness of collections and can also be applied to compare different times to contacts, channels, payment arrangements, etc.
3. Optimizing the performance of collections teams. Optimizing prescriptive analytics takes predictive analytics one step further by examining the entire business process to find the strategy that will result in the highest level of overall performance.
4. Reduce the cost of collections processes. Predictive models can literally predict which cases are most likely to pay, making cases assigned to collections staff more productive.”
The new trends do not replace the expertise of collections staff, but rather seek to improve overall collections performance by leveraging new digital capabilities, the publication notes.
