By: Brice Smith, Founder and CEO of InvestiNet
Machine learning – sometimes referred to as AI – has transformed many industries with enhanced data insights and a clearer view of operational inefficiencies requiring remedy. And while the debt resolution industry is slower on the uptake, we are on the precipice of realizing machine learning’s potential in accounts receivables.
Traditional debt resolution methods, such as postal mail and phone calls, often miss the mark with borrowers. These tactics often don’t align with modern life where mail and landlines have lost their influence. Innovation allows us to customize our approach to contacting consumers about their past due accounts. It’s smart business that drives profits for clients while positioning our services as a helping hand for borrowers.
What is machine learning and why should our industry further invest in it? Machine learning is an application that learns and adapts without following specific human commands. In other words, our decisions get smarter over time by continuously analyzing data and extrapolating insights before taking or recommending action. Insights are implemented into company operations and execution, such as borrower outreach strategies. What does that mean for accounts receivables? Through machine learning, the industry can emphasize a human-approach to debt resolution that meets people where they are on the devices they use to communicate. A customized approach that views each borrower as an individual produces more success in collecting delinquent debt, and it creates an environment where people feel supported in their effort to muscle out of that debt.
Machine learning removes the guesswork. This enables collectors to focus their energy on using the skills they’ve honed over a career to help resolve debt without wasting time placing as many ignored calls or mailing notices that will hit the recycle bin the same day they hit the mailbox.
Machine learning doubles down on communication methods that have been battletested to show high efficacy. Put simply, receivables management firms that invest in machine learning realize more time-savings for their teams and produce higher yield for lenders. A customized, helping-hand approach also demonstrates empathy for borrowers.
The promise of machine learning extends to all phases of the debt resolution life cycle, from the initial outreach to final settlement or post-judgment. Implementing machine learning results in a streamlined, cost-effective and customer-friendly approach. In addition, this new technology offers many other benefits:
Enhanced Data Analytics:
Technology plays a pivotal role in evaluating the borrower’s risk and financial situation. By employing advanced data analytics and machine learning, a tech-forward agency creates sophisticated models to predict the likelihood of successful debt resolution. These models analyze borrower behavior, financial history and other relevant data points, allowing for more informed decision-making.
Omnichannel Communication:
Communication with borrowers is an important part of the debt resolution process, and technology enables an omnichannel approach. Instead of relying on phone calls tech-forward agencies use SMS and personal web portals for communication. This approach increases the likelihood of reaching borrowers.
Automation and Efficiency:
Too often bogged down by manual, time-consuming tasks, agencies that automate some of their processes reduce human error, accelerate the debt resolution process and lower operational costs.
Reduced Cost and Increased Yield:
Perhaps the most significant benefit of a tech-forward agency is the ability to reduce costs while also increasing yield from those delinquent accounts. By automating tasks, optimizing outreach strategies and enhancing communication, these agencies can operate more efficiently.
A quality debt resolution partner can yield higher returns without having to invest in robust R&D themselves. For creditors, it’s crucial to find a partner that’s able to not only up your debt resolution game, but that also helps you collect throughout the account life cycle, from early delinquency to post-judgment.
About The Author:
Brice Smith is founder and CEO of InvestiNet, a tech-forward accounts receivable management firm that developed machine-learning applications over 10 years ago. He formed the company in 2011 after noticing that there was a need for a new type of resolution provider. Firmly rooted in a commitment to innovation, compliance, and empathy, InvestiNet helps creditors improve yield on delinquent accounts throughout the collection account life cycle, from early delinquency to post-judgment.
