Debt collection is a difficult business to be in. The competition is fierce and consumer behaviors and methods of communication are changing faster than ever before. Because of this, Collection Agencies are always looking for ways to get an edge over their competition and find ways to optimize their consumer engagement and recover more in a shorter period. One of the newest improvement tools the industry has started to embrace is Machine Learning (ML). With the use of consumer data, ML helps identify trends in consumer behavior and predict outcomes for a decision-making process.
What is Machine Learning?
In simple terms, Machine learning is a type of artificial intelligence that enables self-learning by analyzing patterns in existing data and then applying that learning without the need for human intervention. By harnessing the power of sophisticated technologies like ML, the overall performance of the debt collection operations can be improved.
How does Machine Learning work in debt collections?
In today’s fast-paced, digital world, people demand flexibility, personalization, and choice in everything, especially in debt collection. Settling a debt needs to feel easy, comfortable, and painless, not a process consisting of annoying phone calls at appropriate times and spammed mailboxes. Not providing an experience that meets consumer expectations can lead to lower recovery rates and a negative brand experience. This is where Machine learning in debt collection can help.
Here are just a few ways ML is helping the debt collection processes.
1. Contact optimization
Machine learning in debt collection will identify patterns in the data from the communication channels, such as phone calls, SMS, email, and suggest the best mode of communication for each consumer and the optimal time to reach out to them. Knowing and utilizing a consumer’s preferred method of communication immediately makes calls more comfortable. It removes some of the initial friction and helps increase the likelihood of response and discussion.
2. Improves efficiency
ML improves collector efficiency by assigning the best suitable agent to the case. It does this by analyzing historical data on each agent to find their strengths as it relates to industry, mode of communication, debt amount, demographics of the debtor and many other factors. It takes chance out of the equation and makes sure each engagement has the best probability of a successful outcome.
3. Recovery prioritization
Most companies cannot afford to have receivables age and need their Collection Agency to recover as quick as possible. With ML algorithms, agencies can identify consumers with the highest propensity to pay and prioritize those cases first. Helping them recover more debt in a shorter amount of time for their clients.
4. Speech Analytics
ML can evaluate consumers’ speech in real time to help agents make informed decisions. It does this by analyzing consumers tone, context, and sentiment during conversations and provides agents with suggestions on the best ways to handle the engagement. It can also review recorded calls and analyze consumer and agent conversations to suggest improvements for future interaction.
5. Strategy simulations
ML enables businesses to simulate and test various campaign strategies and ideas, giving the best strategy for success in advance. It uses advanced algorithms that fuses data and strategy to make and execute campaign decisions, so the data is not just actionable, but also is proactive. It allows Agencies to schedule campaigns and phone strategies days in advance and modify them in real-time.
6. Regular monitoring
ML assists agencies monitor their debtors accounts easily through reporting dashboards that automatically update information using data of consumers. This data points collectors and agents to view all the information in one place and provides updates on consumer’s actions and automatic analysis on the consumer’s data. This eliminates the tiring process of going back and forth into various files and systems to view updated information.
Importance of Human Element
While Machine Learning can bring many advantages in debt collections, the human element plays a large role in supplementing it. While ML provides insight on when to call a consumer, propensity to pay, and strategy planning, it is the collector’s ability to use this data in real-time that makes it actionable. For Example, ML technologies such as speech analytics support the human element by suggesting the tone and intention of a consumer. A collector then has to use that information to better communicate with the consumer. The correct balance and blend of Machine Learning alongside the human element is what companies should strive for.
Machine Learning in debt collection is no longer a futuristic process as this article has shown, the applications are vast and varied.
At First Credit Services, we have built a proprietary system that combines the best of both worlds by combining state-of-the-art machine learning technologies like NLP (Natural Language Processing) with the best-trained collection agent staff in the industry. This powerful combination of technology and staff helps your company not only keep your customer at the center of the collections process but also helps you increase your collections rate by taking advantage of cutting-edge data science innovations, without having to reinvent the wheel and spend millions. With an industry experience of over 25 years catering to numerous industries, FCS specializes in credit collection services, particularly First Party and Third Party collections, and also offers Extended Business Office (EBO) services and Customer Engagement Outsourcing services under the same hood.