Debt collection and the banking sector: how Artificial Intelligence supports precise and advantageous collection
The COVID-19 pandemic has brought about such economic instability that most governments have proposed moratoriums on insolvencies. At the same time, many institutions have had to quickly adopt new digital services to continue serving customers, regardless of how prepared they were for this change.
Since then, most of the programs that assisted customers during the pandemic have expired, causing a sharp increase in delinquency rates.
To be successful, financial institutions must develop a debt collection strategy that reduces risk and increases collections without exceeding the operating budget. The strategy must take into account factors such as compliance laws, brand reputation, exposure, level of risk, effectiveness of collections, quality of contact information and internal resource limitations.
To obtain the right result it is necessary to know the customer better and fully understand each situation to develop an optimal strategy. This goes hand in hand with a flexible foundation that takes full advantage of the promising opportunities arising from digital banking and cutting-edge technologies such as artificial intelligence.
How to implement artificial intelligence to improve your company’s debt collection?
Using BigProfiles, an Artificial Intelligence platform designed for debt collection activities, it is possible to carry out an in-depth study of your debtor lists and thus set data-driven strategies that are able to reduce risks and costs, increasing the number of recoveries and the value of the total debts recovered.
Intuitive and easy to use, the BigProfiles AI Platform allows even those without coding knowledge to create predictive models capable of:
- Identify the percentage of spontaneous debtors within the list. In this way these profiles can be eliminated from processing, saving time and resources.
- Predict the propensity to settle NPL cases out of court.
- Predict portfolio value. By predicting the expected value of each NPL it is possible to predict the future value of the loan portfolio and divide it into segments based on the future recovery value.
- Assign the most complex practices to the most experienced operators.
By doing so, whether by a data scientist or a Collection Manager, it will be possible to analyse one’s debt portfolio and set the best data-driven strategies based on these analyses to achieve company objectives.
Do you want to know more about how to implement BigProfiles within your company and start harnessing the power of Artificial Intelligence?
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