According to Bloomberg, even small but incremental debt collection improvements present an opportunity worth billions of dollars. At times of recession, understanding this is particularly important as fine-tuned debt collection may precipitate recovery, optimize expenses, and improve consumer relations.
The first thing you should note is cultural differences. As the statistics show, people from different countries interact with businesses differently, which influences how lenders must adjust their processes to consumer behavior.
Of course, the issue is more complicated, and goes beyond cultural differences. However, in terms of major consumer lending markets, the U.S. and the U.K. demonstrate the highest collection success rates.
Currently, the U.S. notably lags behind the U.K. The effort must be made to bridge the gap, considering that the U.S.’s delinquent household debt gradually approaches $16 billion.
Next, regulations. Until May 2019, strict legislation has been affecting debt recovery, making it difficult in some cases. Yet, with CFPB’s proposed amends to FDCPA, including call volume and frequency limitations and clarified usage rules for voicemail, text messages, and email, the opportunity for improvement came along. Similarly, TCPA also needs significant enhancements due to its current legal uncertainties revolving around consumer consent.
Machine Learning to Transform Debt Collection
Consumers always demand more, especially in such urgent areas as lending. Minding this, you should continuously provide a satisfying service to consumers every time they apply for a loan. The collection process is generally considered irritating for consumers. Four in ten US consumers think collectors’ calls are pushy, so it’s your primary job to make their experience pleasant.
One of the solutions machine learning introduces to make debt collection less irritating for consumers is delinquency predictions. By analyzing the FICO score’s parameters and consumers’ credit history, these tools predict whether an individual will default in the future.
In this novel fashion, the FinXEdge Collection solution, for example, parses loan details, income, credit scores, and other data. Also, it covers external (like weather, macro-, and microeconomy) and behavioral factors (like voice, call notes, demographics, and more) to compose a comprehensive image of the consumer’s creditworthiness.
Tailored Debt Recovery Offers
Defaulters are people with different credit profiles and, thus, motivations and behavior. Despite this, lenders tend to offer “standard” repayment options to everyone without considering individual credit. The strategy is losing right from the beginning since consumers don’t like when their demands are neglected.
Machine learning-based collection solutions, such as Paydit, bring back this “personal touch” to debt negotiation, defining the exact profile of each lender. Paydit gains analytical insights from consumers’ credit history and allows collectors to customize the consumer journey with personal payment schedules and options, billing frequency, reporting, and more. At this point, debt collection becomes less stressful for both parties.
Borrowers’ Risk Segmentation
Most debt collections still have to randomly contact defaulters through phone calls, follow-up emails, and other mediums that can help reach them out. They may grow too persistent in their efforts as some consumers complain about frequent calls that repeat several times a week. Lenders may continue doing so even if they are asked not to. Probably some use loopholes in debt collection regulations where the time and frequency of calls aren’t specified.
On the other hand, dedicated tools use machine learning’s capabilities to segment consumer accounts by order of their priority. Also, they provide recommendations on when and how it’s better to reach out to the borrower to get positive feedback from them, which saves you the trouble of making seemingly infinite “trial” calls.
Despite all these efficiency improvements, the process remains compliant with TCPA, FDCPA, and other related laws and local regulations.
Discovering the Potential of Machine Learning for ROI
Perhaps, one of the main benefits of machine learning for lending is behavioral segmentation, that has high potential to increase recovery rates and cut operational costs per collected loan. To bring the sizable profit, a machine learning-based lending solution must cater to the European market’s needs, adjusting the lending process to each country’s legal framework.
Repurposing collectors’ efforts to high-risk issues may also bring better outcomes. A personalized approach to debt recovery issues may increase sales’ performance in the long run. Applying machine learning should also help rationalize efforts through task automation, which will free staff on more complex issues.
While the digitalization breakthrough in debt collections is hard to miss, the technology is yet weakly adopted. Cost and compliance issues are seemingly unbearable to many lenders. Others target their money and efforts on underwriting and customer acquisition.
What Do you Need to Know to Jumpstart your Digital Transformation?
Are you all set to transform your lending with machine learning? There’s only one way to find out by making sure all of these points are about your business:
- Well-defined use cases
- Open-minded leadership
- Strong compliance practices
Having these, you will be more likely to beat the fierce competition with other collectors. Over time, you may discover new potential applications to improve payments, compliance, enhance performance, and better combat fraud.
Don’t think that machine learning means randomly automating collections to improve outdated methods. It’s a whole new way to enhance the consumer experience and improve collectors’ performance. This is a fresh look at consumer lending, and it’s up to you to try it out (if the arguments we brought in the article are convincing enough to you).