AI Debt Collection: A Quick Primer<\/h2>\n\n\n\n
The use of AI in debt collection can be categorized in a number of ways. One cluster of products and use cases, for example, could be described for the purpose of our discussion as \u201csystematic\u201d applications of AI. These include a number of AI functions that have already been embraced by a tech-forward segment of the industry, including: <\/p>\n\n\n\n
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- Chatbots used to interact with debtors<\/a> for the purpose of answering questions, providing information, and routing inquiries to human agents when necessary.\u00a0<\/li>\n\n\n\n
- Data-analysis tools used to segment a debt portfolio, determining which debtors are most and least likely to make payments, and prioritizing them accordingly. <\/li>\n\n\n\n
- Automating repetitive administrative tasks<\/a> and record-keeping.\u00a0<\/li>\n\n\n\n
- Monitoring verbal and written communications to ensure legal compliance<\/a>.\u00a0<\/li>\n\n\n\n
- Analysis tools to predict consumer behavior as it relates to their handling of debts, and to predict the best channels for communication with those consumers. <\/li>\n<\/ul>\n\n\n\n
At the other end of the scale, we find what can be characterized as \u201csituational\u201d applications of AI tools: those that can be deployed on an ad hoc<\/em> basis by a department or even an individual collections agent. Those might include the use of general-purpose products such as ChatGPT or Claude for less formal tasks such as personalizing communications with debtors and refining or repurposing existing document templates. <\/p>\n\n\n\n
Long-Term vs Short-Term Goals of AI<\/h2>\n\n\n\n
The AI tools we\u2019ve chosen to characterize as \u201csystematic\u201d and \u201csituational\u201d can also be viewed through the lens of their respective ease of implementation and the time and effort necessary to incorporate them into workflows. <\/p>\n\n\n\n
In broad terms, systematic AI tools are best thought of as a long-term investment. Integrating them into your software stack is a serious undertaking, partially because of the technological challenges involved and partially because they represent a significant evolution of existing workflows. Naturally, this requires active involvement from upper management, to define the roles these new tools will play, choose suitable vendors and products, and allocate the appropriate financial and IT resources for deployment. <\/p>\n\n\n\n
In comparison, situational tools can be brought into play at the cost of a modest subscription, and be utilized immediately to address short-term goals and challenges. If anything, this unprecedentedly low bar for the use of AI poses a management challenge in itself. With the cost of a ChatGPT or Claude account falling within the realm of an individual employee\u2019s personal discretionary spending, agents may already be utilizing it without your knowledge. Encouraging an open discussion about the use of such tools, and setting guidelines around that usage, will become increasingly important in 2025.\u00a0<\/p>\n\n\n\n
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<\/figure>\n\n\n\nBenefits of Conversational AI for Debt Collection<\/h2>\n\n\n\n
That said, although managers will want to establish a degree of oversight and control in the use of generative AI, these tools are surprisingly versatile. They\u2019re sometimes described as the Swiss Army knife of software, though lubricants might be a more useful analogy. Like a lubricant, these general-purpose AI tools can be applied as needed to reduce friction throughout the collection process. <\/p>\n\n\n\n
A few representative use cases might include: <\/p>\n\n\n\n
Using AI to Personalize Existing Scripts or Templates<\/h3>\n\n\n\n
It\u2019s a good practice to use a set of standardized scripts and templates for collection-related communications. It ensures on-brand messaging, reduces compliance risk, and enables management to dial in on phrasing that helps ensure positive results. The downside to this level of control is that communications may come across as rote and impersonal<\/a>. That\u2019s a liability when dealing with consumers who are conditioned to expect personalization.\u00a0<\/p>\n\n\n\n
Now, suppose that you\u2019ve identified the debtor\u2019s social media profiles through a search with Spokeo for Business. Your AI tool of choice can be prompted to examine your debtor\u2019s public posts and identify notable interests. It can then take a few of those key details and incorporate them into a standardized script or template, making it feel fresh and personalized. <\/p>\n\n\n\n
Its output might also contain a \u201ccheat sheet\u201d of the debtor\u2019s interests, attitudes, and opinions that agents could refer to during the conversation, as a means of establishing and maintaining common ground. <\/p>\n\n\n\n
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<\/figure>\n\n\n\nUsing AI to Adapt Existing Scripts or Templates to New Channels<\/h3>\n\n\n\n
The script your agency has been using for phone calls, or the templates it uses for demand letters, is probably well-suited to its intended medium. But physical mail and voice calls are increasingly out of sync with a digital-first public, and communicating with debtors through their preferred channels means that you\u2019ll need to adapt those letters to better address both the constraints and the potential of channels such as emails<\/a>, text messages<\/a>, private messaging through social media,<\/a> or (primarily in the case of first-party creditors) in the form of push notifications on an app or popups on an ATM.\u00a0<\/p>\n\n\n\n
Adapting these existing templates to new media can be accomplished by humans through rounds of meetings, discussions, and drafts lasting weeks or months. Or, a generative AI tool can create a draft in just a few minutes that the team can then focus on refining. <\/p>\n\n\n\n
A prompt repurposing a demand letter into an email, for example, might task the AI to address these points: <\/p>\n\n\n\n
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- Condense the information into a more concise form, while retaining the key points (most notably, anything that must be included for compliance purposes). <\/li>\n\n\n\n
- Make the language more conversational to fit the less formal nature of an email.\u00a0<\/li>\n\n\n\n
- Craft a subject line and a first sentence for the email (which will often show in previews) that encourages the debtor to read the email. <\/li>\n\n\n\n
- Uses words and phrases from a supplied whitelist where possible, and avoids those from a supplied blacklist. <\/li>\n<\/ul>\n\n\n\n
Similar prompts can be constructed to address best practices for texts, social media messages, or other forms of communication. <\/p>\n\n\n\n
Data Fuels AI Collections<\/h2>\n\n\n\n
All AI tools, as is the case with data analytics tools in general, are only as good as the data they\u2019re given to work with. This positions Spokeo for Business<\/a> as a powerful complement to most AI tools, whether (as described here) they are \u201csystematic\u201d or \u201csituational.\u201d\u00a0<\/p>\n\n\n\n
Spokeo can also be deployed as part of an enterprise-wide software stack, with its powerful Application Programming Interface (API) used to share data seamlessly, conventional analytics products, and data-management tools from other vendors. Yet its licensing model is flexible enough that it can be implemented effectively on a standalone basis by a small team, or even a single ambitious agent. <\/p>\n\n\n\n
In either case, Spokeo\u2019s simple and intuitive interface, its ability to harness big data from both regulated and unregulated sources, and especially its powerful social media search capabilities, make it an exceptional source of skip tracing data. To learn more about the product, schedule a demonstration, or arrange a no-cost trial of Spokeo for Business at your agency, please reach out to our team using the contact information on our Collections and Skip Tracing<\/a> page.\u00a0<\/p>\n\n\n\n
Sources<\/strong><\/p>\n\n\n\n
Credit and Collections Professionals: Chatbots for Debt Collections<\/a><\/p>\n\n\n\n
Numerous: 10 Effective Ways to Use AI for Administrative Tasks in 2024<\/a><\/p>\n\n\n\n