Unless you’ve been living under a rock, you’re probably aware that artificial intelligence (AI) is making headlines and radically transforming a variety of industries. The technology’s potential impact on humanity is nearly incomprehensible, touching everything from healthcare to transportation. But let’s bring this conversation back to our wheelhouse: debt collection. Before we explore this highly specialized application of AI, we need to establish some foundational knowledge. That begins with an overview of how AI is set to redefine the broader discipline of credit risk management.
Credit Risk Management: Today’s Landscape vs. Tomorrow’s Horizon
Artificial Intelligence thrives on data. The more quality data you feed into an AI model, the more accurate and effective it becomes. This is particularly relevant in financial sectors where large volumes of data are available, making it feasible for AI to bring about meaningful improvements.
To offer some perspective, let’s consider the existing state of credit and collections in the Philippines. The country has several credit bureaus that provide crucial information to lenders through comprehensive credit reports. These reports play a vital role in underwriting activities, allowing financial institutions and lending companies to make more informed and timely decisions. The data obtained from these bureaus have been instrumental in helping these entities maintain a healthy portfolio, by identifying both high-risk and low-risk borrowers. This, in turn, improves the quality of loans issued and significantly reduces the rates of delinquency, contributing to a more robust financial ecosystem.
During the pre-credit bureau era, credit card issuers considered themselves successful if they managed to keep delinquency rates at around 5%. Thanks to the rich datasets provided by credit bureaus, leading credit card issuers that have incorporated these data into their card acquisition strategies have now decreased their delinquency rates to nearly 3%, representing almost a 40% improvement over past figures.
Now, let’s consider the transformative potential of incorporating AI into credit risk management. An AI system specifically trained in this domain could go far beyond the current level of how we analyze credit histories and scores. With the capability to process and analyze massive datasets, AI could identify nuanced patterns and trends in consumer behavior that human analysts might overlook. These advanced algorithms could then offer predictive insights into how loan and credit card applicants are likely to behave once approved. This will enable better targeting of clients for financial institutions and lending companies, as well as the ability to assign more appropriate credit limits and loan amounts. Furthermore, these insights can also better predict future delinquencies, enabling lenders to take proactive measures and control delinquency in a more timely manner.
Additionally, real-time data sharing, a rapidly advancing frontier in technology, could be seamlessly integrated with AI systems. This integration would allow for immediate adjustments to credit risk assessments, leading to quicker and more accurate lending decisions. It could even limit a company’s exposure to a client at the first sign of trouble. While some systems already provide these capabilities for credit card companies, imagine the increased power and accuracy when they are run by AI. Such AI systems would continuously learn from new data, constantly refining their algorithms to further improve their predictive accuracy.
These advancements in AI could potentially lead to a further decline in delinquency rates, making lending not only safer for financial institutions but also more accessible for consumers. This would represent a win-win situation for all stakeholders involved.
AI’s Impact on the Debt Collection Ecosystem
AI is not just a trending topic. It’s poised to be a transformative force across various industries, including debt collection. While it’s important to note that AI may not completely eliminate delinquency, it offers significant improvements in making the collection process more efficient, streamlined, and ultimately more effective.
AI and Lenders
If you’re a lender, the integration of AI could lead to a seismic shift in how you operate. Firstly, the implementation of AI algorithms can significantly speed up credit decisions, thereby reducing your delinquency rates. This will have a direct impact on your collections team, where a reduced headcount could translate to lower operational costs.
Already, AI has advanced to a stage where it can interact fluently in multiple languages, create images based solely on text input, understand jokes, program code, and much more. Keep in mind that, at the time of this writing, it has only been less than a year since OpenAI publicly released ChatGPT, which has brought mainstream awareness to AI’s capabilities. Given the rapid pace of AI development, just imagine where we could be three years from now or even beyond that. In the field of collections, this technology has the potential to not only enhance your Frontend collections team but also to replace it entirely.
For the teams that handle later stages of collections, AI could serve as an invaluable coach. It can guide your agents in real-time, offering insights on call conduct and settlement offers to debtors. This is neither science fiction nor a speculative prediction. It’s a current capability that AI brings to collections and customer service. I have attended several FinTech events already and have seen companies present how their AI can help coach agents in real-time. The technology is already here. The only question is how long it will take for widespread adoption.
As financial inclusivity grows, thanks in part to the emergence of FinTech companies sharing credit data on the previously “unbanked” population, even traditional lenders are slowly extending their reach into this segment. This could mean a substantial rise in the volume of receivables, albeit with smaller average loan balances. But with AI’s help in targeting the right consumers, lenders can not only extend credit to more people but also maintain a healthy portfolio.
AI and Debt Collection Companies
While lower delinquency rates are good news for lenders, they also have ripple effects on the debt collection ecosystem. Despite the decrease in delinquency rates, the sheer volume of new loans and credit cards being issued could still result in a higher number of accounts needing collections. However, these accounts are likely to have smaller balances, given the advancements in delinquency management.
No Collection No Commission Model
For agencies operating on a “no collection, no commission” model, the evolving landscape requires a heightened focus on operational efficiency, especially as average delinquent balances are likely to decrease. These companies can no longer rely solely on high-balance accounts to pull up their commissions. Instead, they must figure out how to be both efficient and effective in collecting from accounts with lower balances. Agencies that fail to adapt to these changes will find it increasingly difficult to sustain their operations and may eventually close their doors. In contrast, larger and more efficient agencies that can evolve with the times are likely to dominate the marketplace.
Per Seat Model
For those unfamiliar with the term, the “per seat” model refers to a billing structure where financial institutions outsource their collection activities to specialized agencies or outsourcing companies. Each “seat” represents a fixed cost the institution pays for an employee hired by the agency to work on their behalf. This setup is likely to see changes as more advanced AI tools become available. The demand for human agents will likely diminish, as AI takes over routine collection cases, leaving humans to focus on the more complicated scenarios.
This reality underscores the need for outsourcing agencies and debt collection companies to invest heavily in AI technologies. Acting as intermediaries between tech companies specializing in AI for collections and lenders who require these services, these agencies can consolidate the demands of multiple clients. This collective bargaining power enables them to negotiate more favorable terms with AI service providers, thanks to the large volume of potential monthly licenses. Achieving economies of scale makes it feasible for these agencies to offer advanced AI capabilities to lenders at competitive rates. While AI companies could directly engage with financial institutions and lending companies, market expertise will be a key reason why they may prefer to collaborate with outsourcing agencies rather than deal directly with lenders. As software companies, it makes sense for them to focus on their core strength: delivering a platform that service providers will use. Those providers can then tailor the platform’s capabilities to meet the specific needs of each client, allowing the AI companies to focus on continuous development.
Operational efficiency offers another compelling argument in favor of AI adoption. By automating routine tasks in collections, agencies can reduce the need for human agents, who can then be deployed for more nuanced or complicated cases, or for clients who prefer human interaction.
This leads us to another important consideration: the value of extensive field collection capabilities. While AI tools and AI-enabled agents play a significant role, they are only part of the equation for collection outsourcing companies or debt collection agencies. These technological solutions have their limitations, particularly in the later stages of collections. At this point, the importance of extensive field coverage and having a wide network of efficient field agents becomes more critical. In an industry where differentiating your services is vital for sustainability, a comprehensive approach that blends AI with robust field collection efforts can provide a significant competitive advantage. Agencies that are equipped with both advanced technology and a strong network of field agents are well-positioned to excel in the rapidly evolving landscape of the collections market.
Final Thoughts
As artificial intelligence continues to play an ever-increasing role in the debt collection ecosystem, the mandate for both lenders and debt collection agencies to adapt is more pressing than ever. For lenders, the advent of AI opens the door to lots of opportunities, ranging from more nuanced credit risk assessments to managing a growing portfolio with unprecedented efficiency.
For debt collection companies, the incorporation of AI goes beyond mere operational streamlining. In an industry that’s constantly evolving, investment in advanced technology can serve as a cornerstone for staying ahead of the competition. However, we must not lose sight of the fact that technology has its limitations. When it comes to the more complex, later stages of the collection process, human expertise and a comprehensive network of efficient field agents will remain indispensable. In this rapidly changing landscape, those who are agile enough to integrate technological innovations with the essential aspects of human interaction will find themselves best positioned for long-term success.
It’s worth noting that these are just my predictions for the near term, meaning the next 2 to 3 years. Anything beyond that is anybody’s guess, especially given the breakneck pace at which AI and technology are advancing. While significant advancements in technology used to take years, in the realm of AI, that figure has been compressed to months, and for some features, even just weeks. The future is highly unpredictable. If we were to assume that Artificial Super Intelligence (ASI) could be achieved, and its goals were aligned with those of humanity, we might find ourselves in a utopian society where AI takes care of our material needs, leaving humans to focus on living life to the fullest. But for now, that remains the stuff of science fiction and speculation. What’s clear is that the ability to adapt and evolve will be crucial for survival in this ever-changing industry.
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