It’s the information, disruptive, and innovative age. Everyday, new and developing technology is upending norms and standards, and our industry is the next.
I think the financial sector deserves access to these technologies, including blockchain, artificial intelligence, mobile technology, and a lot more. The advantages of having these technologies will improve the industry’s outlook going forward.
I support testing and putting these improvements into practice. I did, however, come to see why they haven’t gained broader acceptance.
Early Warning Mechanism
In the past, debt recovery has mainly been reactive, attempting to recover losses after a borrower has fallen behind on payments. This paradigm is altered by machine learning, which makes it possible to proactively detect at-risk accounts before they fall behind on payments.
The computational capacity of machine learning enables it to evaluate enormous amounts of diverse data types and uncover previously unknown characteristics that predict delinquency. Understanding these trends provides lenders with a more solid foundation for risk assessment that goes beyond basic indicators like credit bureau risk scores. Machine learning can quickly incorporate new data when conditions change, such as during a pandemic, updating analysis in real-time in ways that are not possible with conventional risk models.
Machine learning can also be used to estimate borrowers’ likelihood of defaulting on their debts. By concentrating their efforts on clients who are in danger, lenders can stop accounts from going into default in the first place.
Systems Modernization
By deploying cutting-edge case management systems that use the results of predictive modeling to choose the most suitable treatment techniques, debt collection can be significantly improved. Such systems must be flexible enough to be customized to the particular requirements of the business emerging.
A collection instrument that is tailored to manage the special collection abilities made possible for business debt collection differs significantly from one that is not built for government use (e.g. wage garnishments, bank levies, liens, etc.). Modern technologies should assist the expansion of operations to incorporate new best practices while preserving particular business laws, regulations, and current best practices.
The optimal use of new technology is made possible by utilizing an experienced team to find gaps between present operations and a model collections organization.
Business Challenge
The bank intended to modernize its strategy in order to enhance performance and take a more consumer-friendly stance after realizing the limitations of a conventional collecting method.
The client’s team and First Source worked together to:
- Transform traditional collecting operations into digital white-label models as soon as possible.
- Adopt a targeted, compassionate strategy to show them that we are “here to help”
- Boost sales collection efficiency and keep most accounts coming into collections under control. Create a scalable solution that may easily expand to handle additional market segments.
- Create and advertise self-serve options for customers.
- Enable voice, email, web chat, text messaging, social media, and whitemail as part of an omnichannel customer interaction strategy.
- Ensure complete compliance, improve service standards, and increase collection efficiency.
Recognizing And Classifying Borrowers
AI and machine learning have the potential to revolutionize how lenders perceive their borrowers. Data-driven machine learning can emphasize what makes a borrower unique within specific market segments, in contrast to the conventional strategy that lumps borrowers into a sector-based group.
In challenging economic times, a thorough grasp of borrowers becomes even more crucial. The disparate effects of COVID-19 highlight the differences between various economic sectors. Certain businesses are more suited to delivery or online purchasing in areas like restaurants, car dealerships, and retail stores.
Economic constraints and geographic variations in viral intensity have also had different effects on various economic sectors.
Effective debt collection depends on understanding a borrower’s condition by considering these and many other criteria. Lenders can create a complex client profile using AI and machine learning to identify which debtors are most likely to self-cure delinquencies and which require proactive intervention, such as loan restructuring or adjusted repayment terms.
Due to the size of both personal and corporate debt, even little gains in consumer segmentation can result in substantial returns. Lenders become more skilled at appraising clients based on their unique qualities rather than general market sectors as AI continues to learn and account profiles get progressively more detailed.
Enhancing Customer Engagement
Lenders have traditionally intervened with phone calls to fix payment issues. Phone calls are a harsh tool with diminishing results because fewer people rely on them for communication and financial transactions, whereas automated messaging and live agents can be helpful.
Today’s lenders have more options than ever before for communicating with borrowers. They have access to face-to-face meetings, phone calls, emails, text messages, social media, online chat, and mobile apps, but in my view, few lenders are making the most of these resources.
Having these outreach options available is not enough to optimize debt collection; you also need to know how to use them, when to use them, and how to develop a persuasive message.
These characteristics are dependent on context and influenced by a wide range of factors, making machine learning the perfect tool for their analysis to increase customer engagement.
The time is come to assess how you might enhance your debt-collecting efforts.
In order to give what we refer to as “empathy at scale,” Percipient offers a service that blends our strengths in customer engagement, data analytics, and artificial intelligence.
This benefits our clients:
After the business stimulus packages expire, deal with the predicted surge of debt-related issues.
Utilize innovative communication strategies to tailor the “debtor experience” and create a route to better recoveries and contented clients.
Conclusion
By preventing delinquency and dealing with past-due accounts more successfully, the improved ability to comprehend, identify, and interact with borrowers can lower exposure to losses.
Significant operational savings can also be achieved through data-driven efficiencies. Additionally, more proactive and effective customer outreach can assist borrowers both individuals and businesses in managing their debt better to prevent collections, additional fees, credit markdowns, and potential insolvency.
