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Automation and AI in Debt Collection: Understanding the Difference and Their Impact on Customer Conversations

Andy Turner | Business Development Executive

Debt collection practises are changing rapidly with the introduction of automation and artificial intelligence (AI). Oftentimes, these two terms are used interchangeably, but there is a difference between them and how they are used in customer engagement. This blog looks into these differences and their roles in debt collection, and the future of AI in customer services. 

[Listen to the webinar that covers the difference between automation and AI in det collection]

What is Automation? 

Automation refers to the implementation of programmable, structured, and repeatable processes to improve efficiency. This concept isn't new; we first see automation being used at scale in the early twentieth century with the introduction of the automated assembly line for the Model T Ford.

However, automation has its limitations, especially in complex, in-depth customer conversations where a structured approach may falter. 

What is Artificial Intelligence (AI)? 

AI, on the other hand, is characterised by its unstructured nature, intelligent decision-making capabilities, and the use of machine learning. AI systems can learn and make decisions without specific programming, demonstrating a higher level of understanding and acknowledgment.

The fundamental difference between automation and AI lies in their structured versus unstructured approaches.
 

How Automation and AI Differ: An Example 

A practical example of the difference between automation and AI is the Identity and Verification (ID&V) process over SMS. Using automation alone, the process requires separate responses for each piece of information (account number, date of birth, postcode), and any deviation from expected formats can halt the process.  

Automation AI ID&V


AI, however, can handle unstructured data, allowing for a more fluid and efficient ID&V process, leading to a 60% higher success rate. So instead of six questions and answers going back and forth, there are just one question and one answer, making it a lot more efficient. Furthermore, a customer can respond using any format, which means the process doesn’t fail if there is not an exact format match; the AI is smart enough to work out the date of ‘next Monday’.
 

AI automation ID&V


Webio's Approach to Conversational AI: Blending AI and Agents 

Webio has made significant strides in automating customer conversations, with up to 76.7% of interactions being fully or partially automated. The real game-changer, however, lies in blending AI automation with human agents. This approach doesn't rush customers into self-service but rather provides a balanced strategy of AI and human interaction. 

Customised Language Model for Collections

Webio has developed a Custom Language Model (CLM) tailored to the collections industry. Unlike general-purpose AI models like ChatGPT, which might struggle with industry-specific nuances and security issues, Webio's CLM ensures 90% accuracy in understanding industry-specific language for debt collection. (See: Give AI Chatbots the Edge with Custom Language Models) 

The Components of Conversational AI 

Conversational AI in debt collection is built upon entities, intents, and propensities. It involves understanding the identity of the customer, their intent, and predicting the likely outcome of the conversation. This understanding enables better customer journey designs and more effective communication. (For more, see: AI Chatbots: The Three Layers of AI Automation) 

Entities Intents


The Evolving Role of Human Agents 

The perception of agents is shifting from traditional phone-based roles to digital agents. These agents focus on written and digital skills, leading to greater efficiency, improved success rates, higher recruitment success and better agent retention. Digital agents can handle more conversations simultaneously and benefit from a more relaxed, engaging work environment. (For more, see: Evolving Role of AI in Contact Centres: A Copilot for Customer Service Agents) 

Debt collection agents


The Future: Generative AI Co-Pilots and Digital Agents 

The next step for Webio involves integrating generative AI with digital agents. AI will suggest optimal responses based on past successful interactions, with agents acting as co-pilots or advisors, maintaining compliance and accuracy. 

Expected Results and Metrics 

Implementing AI in debt collection is anticipated to yield significant results. These results are from Webio clients: 


AI metrics for debt collection


Conclusion 

The integration of AI and automation in debt collection is not just about technological advancement but also about enhancing customer experience and agent efficiency. By understanding these technologies, companies can better navigate the complexities of debt collection. 

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