Moving Beyond Automation to Intelligent Collections: Collection Orchestration Platforms
Collection Orchestration Platforms (COPs) are powered by a large collection model (LCM)- based strategy engine that predicts collection propensity based on consumer demographics and debt details. The engine recommends the best use of each communication channel to maximize outcomes while minimizing time and effort.
But What Are Large Collection Models?
Generative AI models today are capable of creating music, art, and even videos. However, the most significant breakthrough has been the rise of large language models (LLMs)—a subset of Generative AI—designed to process complex inputs and produce human-like text responses. LLMs like Gemini and ChatGPT are already being applied to diverse tasks, from assisting with homework to detecting financial fraud.
Skit.ai’s GenAI-powered omnichannel conversational platform is driving remarkable results in the collections space. By adopting this technology, businesses have achieved a 10X ROI on their GenAI investments, reduced collection costs by 63%, doubled collection rates, and significantly improved connectivity and right-party contact (RPC) rates by 2X and 2.1X, respectively.
Despite these advancements, inefficiencies in collection processes persist. Many collection efforts are still not structured as targeted, personalized campaigns. For instance, some agencies rely heavily on mass voice calls to reach their entire consumer base, a strategy that has become less effective. Many consumers, particularly Gen Z and millennials, prefer digital channels and tend to avoid calls from unknown numbers. This “one size fits all” approach results in suboptimal outcomes and a poor customer experience.
Compounding this issue, many creditors and collection agencies have access to vast amounts of untapped consumer data. While credit scores are often used to gauge risk, they overlook valuable behavioral insights available in an organization’s CRM. These systems can contain decades of data, including optimal contact times, preferred payment methods, and historical engagement patterns. Combining this data with individual details like debt type and age allows large language models to create precise consumer risk profiles.
When these models are tailored for collections—what we call Large Collection Models (LCMs)—they offer two key advantages to creditors and collection agencies: enhanced targeting capabilities and improved efficiency in recovering debts.
How Creditors and Collection Agencies Can Benefit from Large Collection Models
Collection Strategy
Consumer allocation can be categorized into high-risk to low-risk segments based on risk profiles. By establishing an effective strategy at the outset, collection efforts can be better optimized. For “soft conversions,” lesser intrusive channels like SMS and email can be utilized, while “hard conversions” may require agent calls or more direct, personalized interactions. Intermediate cases can benefit from voice automation platforms, providing a balanced approach. These strategies help streamline efforts, reduce time spent on collections, and ultimately lower operational costs.
Collection Execution
Efficient collections hinge on identifying the optimal communication channels and timing for consumer engagement. This targeted approach enables more productive conversations and minimizes inefficiencies. In contrast, unfocused campaigns and repeated contact attempts can decrease agent productivity, escalate outreach costs, and negatively impact customer experience (CX) and satisfaction (CSAT) scores. Large Collection Models play a pivotal role by analyzing data to determine the best engagement methods, resulting in improved collection rates, reduced efforts, and fewer charge-offs.
How Do Collection Orchestration Platforms (COPs) Help
Data-Driven Account Prioritization
Using a Collection Orchestration Platform (COP), agencies can begin each campaign with data-backed account segmentation. By analyzing historical payment patterns, customer behavior, and preferred payment methods, agencies can classify accounts by propensity to pay, risk level, and likely response to specific interventions. This ensures that agents focus on the highest-priority accounts and approach each customer with strategies suited to their profile.
Personalized Contact Strategies
Intelligent campaigns leverage detailed customer data to develop personalized engagement strategies. This could mean adjusting the timing, frequency, and channel of contact based on the customer’s historical responsiveness and preferences. Customers who are more responsive to SMS reminders might receive fewer phone calls, while those who prefer talking with an agent can receive prompt call follow-ups.
Enhanced Agent Support with Contextual Insights
Empowering agents with insights about the customers they are engaging with can significantly improve the effectiveness of each interaction. COPs can provide agents with a summary of past interactions, payment promises (PTPs), payment delays, and risk indicators. This contextual intelligence enables agents to tailor their approach, making each conversation more relevant and empathetic.
Predictive Analytics and Adaptive Campaigns
Predictive analytics allows collection agencies to identify patterns and forecast customer behavior. By analyzing account-level and portfolio-level data, COPs can predict which accounts will respond positively to specific outreach methods. Campaigns can then be adapted in real-time, using feedback loops that analyze customer responses and dynamically adjust strategies.
Omnichannel Engagement with Real-Time Tracking
Intelligent campaigns embrace omnichannel outreach that adapts to customer engagement in real-time. For example, if a customer responds positively to an SMS reminder, the system can prioritize SMS as the preferred communication method. This omnichannel approach ensures customers are reached where they are most comfortable, increasing the likelihood of successful engagement.
Continuous Monitoring and Feedback Loops
Intelligent collections campaigns are iterative. Agencies can improve their strategies by continuously monitoring customer engagement data, payment activity, and other response metrics. Feedback loops ensure that successful tactics are reinforced and ineffective ones are adjusted or replaced, creating a constantly evolving, data-driven campaign.
Conclusion
While automation has simplified many aspects of collections campaigns, more is needed to drive meaningful outcomes in a competitive and evolving landscape. Agencies that adopt intelligent collections campaigns stand to achieve higher recovery rates and a more positive customer experience. By incorporating data-driven prioritization, personalized engagement strategies, real-time adaptability, and continuous improvement, collection agencies can transform their processes from reactive to proactive, moving beyond mere automation toward an intelligent, results-oriented approach.
