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Gen AI is everywhere, but what exactly is it? From enhanced automation to completely revolutionised customer support and experience, find out how it’s changing the debt industry for good.
Key takeaways:
- Gen AI refers to deep machine learning models that can ‘generate’ content from prompts.
- Gen AI models include text and image outputs, with the most famous being ChatGPT and Dall-E.
- These models can assist businesses with advanced summarisation, task automation and real-time customer support.
- However, there are concerns over data bias and raw data accuracies, which pose a significant threat to some legacy models.
- Within the debt industry, Gen AI offers an exciting development not only from a customer experience perspective but also from regulatory compliance and analytics.
The subject of hundreds of LinkedIn posts, op-eds, blogs and the backbone of numerous budding business ideas — just like any hot new tech thing, generative AI is 2025’s designated boardroom buzzword.
Whilst Twitter wars and the majority of mainstream media coverage continue to be preoccupied with whether or not AI poses an existential threat to humanity, tech giants proceed unperturbed — continuing to develop models at lightning speed.
In July 2024, we saw the release of the largest ever gen AI model from Meta, Llama 3.1, Open AI’s release of Chat GPT 4o mini, and Mistral’s ‘best small model’, Nemo.
Despite their boundary-pushing capabilities (Google’s Gemini 1.5 can summarise a book's worth of text in a matter of seconds) a lot of business executives are still left scratching their heads about how they can best utilise this evolving tech.
From enhanced automation to improved customer service support — in this article, we’ll get into the nitty gritty of generative AI, explaining exactly what it is, how it works and how, in the context of debt resolution, it can supercharge your processes.
What is generative AI?
Generative AI refers to deep machine learning models that can generate content from specific prompts, including text or imagery. Its recent boom is due to rapid advances in Large Language Models (LLMs) that are trained on vast datasets and corpus’ of text.
LLMs often have a chatbot interface, requiring the user to input a prompt. For example ‘Write me a blog post on generative AI’. The model will then respond to this prompt by generating what appears to be ‘new’ content to satisfy the query — in this case, a blog post about itself.
The model learns common linguistic patterns and information pairing from its training data, which allows it to ‘predict’ the information that follows the query. Chatbots may appear like they understand the content, but what they’re doing is simply predicting the data that comes next.
The most famous LLM and foundation model is ChatGPT, launched in 2022. ChatGPT is trained on a very, very large amount of text — around 300 billion words.
To put that into perspective, that’s around 4 million copies of Harry Potter and the Philosopher's Stone, and includes datasets, books and an enormous selection of web pages.
It’s this very large amount of information and data that allows LLMs to fairly accurately predict the information to certain queries and prompts.
What are the benefits of generative AI?
Just like the potentially infinite number of outputs an LLM like ChatGPT can produce, the benefits of adopting this tech for business operations are similarly expansive.
Summarisation
Similar to advanced data querying, gen AI can analyse huge amounts of raw data simultaneously. However, rather than traditional data querying which will only surface the original data, gen AI goes a step further by interpreting, combining and producing new content from the original data.
This is particularly useful for summarisation tasks, where large amounts of data, text or code need to be understood quickly and succinctly. Gen AI models can provide round-ups, overviews and analysis of large amounts of data in a matter of seconds.
Task automation
Gen AI can also drive enhanced automation across a variety of resource-intensive operational and creative tasks. Models can be adapted to automatically generate things like analytics reports, customer interaction summaries and automated communications.
Automating tasks can free up workloads and drive far higher productivity within existing workforces, allowing employees to focus on more strategic work or that which requires lateral thinking.
Real-time customer support
AI-powered chatbots built on top of LLMs have been revolutionary for scaling customer service support. Able to provide 24/7 online support across every timezone simultaneously, chatbots can be trained to analyse and respond to customer queries in real time.
LLMs can also be layered to include vulnerability or fraud detection through nuanced linguistic analysis of customer interactions.
Reduced human error
Human error poses an enormous threat to global cyber-security, with most threats now originating from human-initiated mistakes. Utilising gen AI models for things like automation tasks, summarisation and next-action support can reduce the incidence of human error across both data processing and data-based decision-making.
What are the limitations of generative AI?
Like any technology, generative AI has its drawbacks, although these vary greatly depending on the model and its application. The following points encompass a broader set of considerations that can affect a range of different models regardless of their specific application.
Factual and raw data inaccuracies
The content any gen AI model produces will in essence always be a variation of the content it was trained on. This is because AI can’t apply creative, conceptual or lateral thinking to reinterpret data and create ‘original’ content or thought in the same way a human can.
Therefore models will only ever be a reflection of how factually accurate their original data is. There have been widespread reports of ChatGPT spouting wild factual inaccuracies (it often pulls information from Wikipedia or outdated web pages and sources) and this is one of the main criticisms of foundation generative AI models.
LLMs that build upon foundation models can be trained on additional data-correcting material. You can also provide models with additional relevant information and documents to reference along with specific prompts, known as RAG.
But ensuring raw data is accurate, up to date and factually correct should be a fundamental consideration to anyone utilising generative AI in any capacity.
Systemic bias
There is also of course a huge potential for systemic bias being built into human-written training data. Things like racism, ableism, homophobia, transphobia and misogyny all have the potential to filter down from training texts to generated outputs.
Simply owing to the amount of training data, it would be impossible to detect all discriminatory or potentially harmful content.
This is again one of the main criticisms of foundation models, although it can be mitigated by training models on specific principles, values and belief systems. However, problems still arise when considering complex more nuanced issues, like tackling far more insidious forms of discrimination in the form of unconscious bias.
Prompt quality
The quality of the content that these models can produce also largely depends on the specificity of the prompts used to initiate them. Just like any programming language, one rogue prompt can cause all kinds of issues if a model is being used for tasks like operational automation.
Generative AI and the debt resolution process
Within the context of debt resolution, gen AI is revolutionising the way leading recovery services, like Ophelos, are approaching the entire customer journey — right from initial outreach, to client-side insights and analytics.
Here are just a few of the ways that, here at Ophelos, we’ve leveraged the power of generative AI for debt resolution, alongside other pioneering forms of machine learning.
Vulnerability detection and customer support
Within any financial service detecting and adequately supporting vulnerable customers is imperative from a regulatory and ethical standpoint. Olive 2.0, our proprietary fine-tuned LLM, can detect and flag in real time which customers may be suffering from intersecting vulnerabilities.
By analysing and summarising customer interaction history, Olive can now also recommend the next steps for customer service agents to ensure vulnerable customers are adequately supported.
By flagging up vulnerabilities and handling less complex cases with automatically generated responses, Olive 2.0 ensures customer service agents can spend more time supporting the customers that need it most.
Regulation and compliance
Our models are all trained on the latest regulatory policy and compliance documentation, ensuring all of our customer communications are 100% compliant.
Training a model like a fine-tuned LLM or NLP model is much faster than multiple teams and operations personnel trying to get to grips with regularly updated compliance.
It also completely removes the risk of human error, as long as the training material is accurate and meticulous.
Analytics and insights
Embedded within our analytics platform, gen AI can also be utilised by clients to generate reports, summaries of data and trends and to set up automated alerts for specific KPIs and engagement metrics.
These powerful tools enable our clients to stay ahead of the curve and gain a far deeper understanding of their specific customer base and recovery process.
Getting started with gen AI
Regardless of industry, gen AI has benefits that extend far beyond those imagined by most organisations or executive boards.
Completely revolutionising tasks that span from operational automation to regulation compliance, the benefits of correctly utilising gen AI can drip feed into reduced business costs, improved customer service, personalisation and in turn, customer satisfaction and experience.
With capabilities like tweaking LLMs for your use case, prompt engineering, Retrieval Augmented Generation, and fine-tuning — we’re only just beginning to see the full potential that this pioneering and ever-evolving tech has to offer.
Interested in leveraging Gen AI for your debt resolution process? Let’s chat.
