Credit Shift

From SMS to Rich Conversations: Exploring RBM in Debt Collection

In this episode of Credit Shift, Mark Oppermann and Graham Bragg dive into the move from traditional SMS to Rich Business Messaging (RBM) — and why it’s a big deal for debt collection and business communication. They chat about how messaging has evolved, what makes RBM stand out, and why branding and trust matter more than ever in customer conversations. From richer content and better engagement to improved security and cost-effectiveness, this episode breaks down the real-world impact of RBM and why it's something every business should be thinking about. Whether you’re in collections, customer service, or just trying to stay ahead of the messaging curve, this one’s worth a listen. KEY TAKEAWAYS * The shift from SMS to RBM represents a significant evolution in messaging. * RBM allows for richer content and branding in business communications. * Every modern smartphone supports RBM, making it widely accessible. * RBM messages can include company branding, enhancing trust and recognition. * Higher engagement rates are observed with branded messages compared to unbranded ones. * RBM provides read receipts, offering insights into message engagement. * The cost of RBM is comparable to SMS, making it a cost-effective solution. * Security is enhanced with RBM, as messages are end-to-end encrypted. * The adoption of RBM is expected to grow rapidly in the coming years. * Businesses can leverage RBM for various applications beyond debt collection. Keywords Credit Shift, SMS, RBM, Rich Business Messaging, Digital Transformation, Debt Collection, Communication Strategies, Customer Engagement, Branding, Security

The Overlooked Potential of SMS in Debt Collection

In this episode of Credit Shift, Mark Oppermann and Graham Bragg chat about the often overlooked power of SMS in digital debt collection and customer engagement. While it might be one of the older technologies in the mix, SMS still has loads to offer – and many businesses aren't using it to its full potential. They dig into how personalisation, automation, and AI can take SMS beyond the basics, helping businesses create smarter, more effective conversations. There’s also a reminder that compliance is key when it comes to messaging strategies. With its broad reach and low costs, SMS remains a seriously effective tool, especially for the debt collection industry. Mark and Graham wrap up by sharing what’s next for SMS, including rich business messaging and other exciting updates on the horizon. Key Takeaways * SMS is still one of the most underused channels for customer engagement. * Younger generations? They’d much rather get a message than answer a phone call. * Adding a personal touch to your SMS can make a big difference in how people respond. * Automation helps keep those conversations flowing smoothly without adding extra work. * AI can take SMS to the next level—making it smarter and keeping things compliant. * It’s affordable, easy to use, and works on pretty much any mobile phone. * People aren’t as fed up with SMS as they once were—message fatigue has really eased off. * Hooking SMS up to your backend systems makes for a much smoother customer journey. * Real-time messaging means customers get what they need, right when they need it. * And looking ahead? Rich business messaging is set to make SMS even more powerful.

Choosing the Right AI for Debt Collections: Custom Language Models vs. Large Language Models

In this episode of Credit Shift, we dive into the world of AI in debt collection. They break down the key differences between custom language models and large language models, tackling the big question—why does it matter? The conversation gets into the challenges of using AI in regulated industries, the importance of truly understanding customer intent, and where AI is headed in customer interactions. We also chat about why one-size-fits-all AI doesn’t cut it in debt collection and how tailored solutions can boost efficiency and compliance. KEY TAKEAWAYS: * Custom language models are built for specific industries, making them more accurate and reliable. * Large language models can sometimes miss the mark, generating irrelevant or incorrect responses. * AI improves customer interactions by recognizing intent and understanding context. * Industry-specific training is essential to ensure AI provides meaningful and compliant responses. * AI hallucinations can be risky, especially in finance, where accuracy is critical. * Recognsing customer vulnerabilities is key to ethical and effective debt collection. * AI isn’t a magic fix—it’s a tool that needs the right setup and oversight. * The future of AI includes smarter features like conversational summaries and co-pilot assistance. * Tailored AI models can dramatically cut down failed conversations in debt collection. Keywords AI, debt collection, custom language models, large language models, digital transformation, finance, generative AI, digital debt collection, NLP, compliance Watch On YouTube https://youtu.be/rqjK9lhXFSM

AI and Digital Debt Collection in 2026

In this episode of Credit Shift, Mark Oppermann and Graham Bragg discuss the future of AI in digital debt collection, focusing on the advancements expected by 2026. They explore the lessons learned from 2025, the role of AI in enhancing customer experience, and the importance of understanding AI's capabilities and limitations. The conversation also delves into the impact of conversational summaries and co-pilots, navigating compliance and vulnerability detection, and the overall future of AI in the industry. This episode refers to the webinar that goes into more detail on AI and Digital Debt Collection in 2026. To Watch Webinar On-Demand https://www.webio.com/webinar/winning-in-2025-ai-digital-debt-collection-strategies-that-work Takeaways * The use of custom language models is essential in debt collection. * AI can significantly increase the number of conversations handled by agents. * AI will enhance sentiment analysis in conversations. * Customers prefer digital channels when interactions are well-designed, leading to higher engagement & better payment outcomes. * The focus should be on improving customer experience through AI. * Automation will lead to more personalised customer journeys. * AI is not a replacement for agents but a tool to assist them. * Understanding AI's limitations is crucial for effective implementation. * Conversational summaries will improve agent efficiency. * Compliance and vulnerability detection are key concerns in AI deployment. * Businesses should build AI in layers – start simple, measure success, and scale gradually to avoid costly mistakes. Keywords AI, digital debt collection, digital transformation, customer experience, conversational AI, compliance, vulnerability detection, automation, sentiment analysis, technology trends

NatWest's AI Agent Development: A Look Behind the Scenes

Chris Booth, the product owner for NatWest Group's AI assistant Cora, discusses the accessibility work NatWest has been doing and the journey of improving their conversational AI. NatWest started by building their own front-end chat interface to make Cora more accessible and usable, allowing users to control such aspects as font size and typing speed. They are now also exploring dynamic interfaces and voice for accessibility to create a more fluid and conversational experience. Chris talks about the challenges of using large language models in customer-facing environments and he further explores the concept of language models and their role in AI systems. The speakers go on to discuss the use of prompting in language models and the need for tools to control and assure the quality of the prompt and response. The conversation then looks into the validation and oversight of AI systems and the speakers discuss the limitations and boundaries of LLMs and the potential impact of multimodal inputs. Takeaways NatWest has built their own front-end chat interface to make their AI assistant, Cora, more accessible and usable. Using large language models in customer-facing environments requires careful governance and risk management. There is potential for creating a trans-organisational repository of conversational content to improve customer experiences. Personalised experiences are a key focus for NatWest, and they are exploring ways to leverage AI to provide personalised financial guidance. Version control is a challenge in AI systems and the use of smaller, more focused models can help address this issue. Understanding the limitations and boundaries of language models is important when building an AI assistant. Multimodal inputs have the potential to greatly impact the capabilities of language models. Agencies, startups, and small businesses can focus on fine-tuning and RAG stages to stay competitive in the AI space. Sound Bites "We had big ambitions on making Cora far more accessible and usable." "We're doing really early stages exploring with mobile. How do we create a much more dynamic, flexible interface?" "We're using it in a lot of ways at the moment. And I think what's so fun and interesting being with Cora and retail is we have by far the highest bar of governance and risk standards." "Multiple small models or tiny models will actually allow you to control because you can keep them small, you can keep them local and they'll do the job for you." Chapters 00:00 Introduction to Chris Booth 06:50 The Journey to LLMs 14:53 The Idea of Artificial Sentience 35:19 Understanding the Limitations and Boundaries of Language Models 41:31 The Importance of Continuous Analysis and Fit For more: Webio: https://webio.com [https://webio.com] Optima Partners: https://optimapartners.co.uk/ [https://optimapartners.co.uk/] NatWest Group: https://www.natwestgroup.com/ [https://www.natwestgroup.com/]