For both individuals and companies, debt levels have been steadily rising. The New York Fed reported that household debt increased by $87 billion in the third quarter of 2020, continuing an upward trend since 2013.
Unfortunately, not all borrowers can meet their obligations, leading to debt collection. The economic crisis caused by Covid-19 has exacerbated these concerns. Suppressed economic activity has caused millions of individuals and businesses to struggle or fail to make monthly payments with many relying on private financing and government support to try to stay afloat. Tightening lending standards for new financing in 2020 only highlights the recognition of the high delinquency risk for many existing mortgages, business loans and other types of credit.
A major contributor to lending losses is an outdated approach to debt collection. Traditional risk models are based on limited data and old formulas that fail to reflect changing economic conditions, including those caused by Covid-19. The traditional model’s inability to predict which debts will go into collections puts lenders behind the curve. Even worse, when accounts fall behind, this approach applies one-size-fits-all responses that let too many borrowers slip into default.
However, artificial intelligence and machine learning are starting to modernize debt collection. These technologies can analyze an immense amount of data from diverse sources, revealing new insights about delinquency risk and how to manage at-risk accounts. In addition, machine learning can be regularly retrained, upgrading its accuracy in dynamic situations as new information comes to light.
Early adopters of these technologies are already seeing meaningful payoffs in their lending portfolios. The growing use of AI and machine learning in lending is ushering in a new era in debt collection, one that includes an early warning for delinquency, refined methods of categorizing borrowers and optimized strategies for customer engagement to reduce defaults.
Early Warning System
Historically, debt collection has been predominantly reactive, trying to recoup losses after a borrower has become delinquent. Machine learning changes this paradigm by creating opportunities to proactively identify at-risk accounts before they fall behind on payments.
Machine learning’s computational power allows it to analyze vast quantities of varied data types to discover previously unidentified factors that portend delinquency. Recognizing these patterns gives lenders a more reliable basis for evaluating risk that goes beyond rough indicators, such as credit bureau risk scores. As conditions change, such as during the pandemic, machine learning can rapidly incorporate new data, updating analysis in real-time in ways that are impossible with traditional risk models.
Machine learning can also be used to determine the probability of delinquency for specific borrowers. This early warning system allows lenders to focus their energies on at-risk clients to prevent their accounts from becoming delinquent in the first place.
Understanding And Categorizing Borrowers
AI and machine learning promise to change how lenders understand their borrowers. Unlike the traditional model that assigns borrowers to a sector-based category, data-driven machine learning can highlight what makes a borrower unique within distinct market segments.
A fine-tuned understanding of borrowers takes on heightened importance in complex economic circumstances. The uneven impacts of Covid-19 demonstrate the nuances that exist within economic sectors. In categories such as restaurants, car dealerships and retail shops, certain businesses are better suited to delivery or online shopping. Geographic differences in virus severity and economic restrictions have also caused disparate effects within economic sectors.
Taking these and a multitude of other factors into account to understand a borrower’s status is critical to effective debt collection. Through AI and machine learning, lenders can build a nuanced customer profile to recognize which borrowers are likely to resolve delinquencies on their own (self-cure) and which need proactive intervention, such as loan restructuring or modified repayment terms.
Given the magnitude of household and corporate debt, even marginal improvements in customer categorization can generate substantial returns. As AI keeps learning and account profiles become even more precise, lenders become increasingly adept at evaluating clients based on their specific characteristics rather than broad market sectors.
Optimized Customer Engagement
Phone calls are the traditional intervention that lenders have used to resolve payment problems. While automated messages and live agents can be useful, phone calls are a blunt instrument with diminishing returns as fewer people rely on them for communication and financial transactions.
Lenders today have more ways than ever before to engage with borrowers. In-person meetings, calls, emails, text messages, social media, website chat and mobile apps are all at their disposal, but in my experience, few lenders are taking maximum advantage of these tools.
Optimizing debt collection is not just about having these outreach methods available; it requires properly choosing which method to use, knowing when to reach out and crafting an effective message. These elements are context-dependent and influenced by numerous variables, making them ideally suited for analysis by machine learning to enhance customer engagement.
For instance, activity on the lender’s mobile app and website that helps identify a preferred method and time of engagement can be integrated with demographic and financial information to design a customized outreach strategy. As client response data is fed back into the algorithm, customer contact becomes progressively tailored and effective.
Artificial intelligence can also enhance engagement in novel ways. AI can analyze audio from customer calls to determine how different scripts or offers impact customer response and collections status. That information can guide future training and ongoing optimization to prevent or resolve the delinquency.
The Benefits Of Modernization In Debt Collections
As AI and machine learning increasingly modernize debt collection, both lenders and borrowers can see impressive benefits. The enhanced ability to understand, identify and interact with borrowers can reduce exposure to losses by both preventing delinquency and more effectively addressing past-due accounts. Data-driven efficiencies can also deliver significant savings in operations. In addition, more proactive and productive customer outreach can help both household and business borrowers better manage debt to avoid collections, averting extra fees, credit markdowns and potential insolvency.
About Kollect Systems
Kollect Systems is an innovative tech platform provider with BankTech and FinTech software solutions which leverage AI based decisioning and workflow technologies to help lenders perform Debt Collections & Recovery (BankTech) processes effectively and for mid-size to large scale enterprise companies (FinTech), to automate Receivables, e-Invoicing & Payments better.
Kollect’s Solutions :
- KollectApps for Lenders (BankTech)
- KollectValley for Finance (FinTech)
- Data Integration & Analytics
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