Artificial Intelligence is both fascinating and complex, capturing the attention of people worldwide. While conversational AI, with ChatGPT leading the way, is a hot topic, AI's impact extends far further. It is transforming industries, including financial services.
Within the financial services sector, the credit and collections industry has tended to be slow to adopt new technologies. However, the Covid pandemic acted as a catalyst for change with the sudden shift to remote work forcing businesses to adopt new technologies. This leapfrogging of technologies was positive, but now the question is whether this momentum will continue.
What hinders credit and collections companies from adopting new tech?
Entrenched legacy systems and a conservative culture that is wary of innovation are the main culprits stifling digital transformation.
For instance, a fear of something going wrong and attracting negative media attention makes executives nervous of new tech. But it's not just the fear of negative press but also the fear of regulatory non-compliance, being caught "off-side". This cautious approach may leave companies seeing innovation as a cost rather than a growth opportunity.
Also, while technology advances rapidly, the regulatory framework might struggle to keep up with the dynamic landscape of AI and related technologies.
What’s driving AI in credit and collections?
However, the picture is not all gloomy and the industry is starting to awake to AI and tech’s possibilities.
The cost-of-living crisis is making execs rethink tech options
In the eyes of senior executives, credit and collections may not be a flagship for investment. But with the current cost-of-living crisis, there is a shift in focus towards investing in processes, like customer engagement, that were previously overlooked.
Customer adoption of tech will force the hand of companies
There is a surge of excitement surrounding AI tech as it has that magical feel when you see it in action. When people start sharing their experiences with conversational or generative AI, it sparks curiosity and will lead to widespread adoption across society.
As such, customer adoption of this technology will be the driving force behind companies embracing AI solutions, such as conversational AI chatbots. It's like when patients come to doctors with information found on the Internet, so to will customers increasingly come to customer service agents armed with AI-generated data or interact with AI directly. In the end, customer behaviour will ignite AI adoption.
AI and the ChatGPT revolution
The transformative impact of Large Language Models like ChatGPT is compared to the shift to the cloud - it's a big infrastructure move rather than just another point solution. Without a doubt, the technology is here to stay, and regulators should work with organisations to define ethical principles for its use. So, with careful consideration, it can add significant value to the industry.
What is needed to successfully implement new AI tech and digital transformation?
It's true that some organisations managed to set up new processes within 24 hours during the pandemic, but these quick fixes were often held together by manual workarounds. Implementing sustainable changes needs investment, commitment and focus.
Companies should start focusing on automation and data
As we embrace the AI wave, we must focus on getting the fundamentals right and ensuring that the underlying data is accurate and robust. The true benefits and insights of automation stem from a solid foundation of reliable data.
In terms of automation, companies should look for new use cases where AI can be applied. As for data, they need to be vigilant about having the right data and that it's structured properly. Every conversation with customers can be seen as data gathering, so companies must consider how they capture and structure that data.
Businesses are always looking for that big lever, that game-changing element that propels the company forward, and shifting from being process-focused to data-focused can bring these opportunities and innovations.
Generative AI and data governance
Concerns around model drift, the source of data, data governance and ethical AI are valid. Moving ahead, companies need to establish specialised data governance teams to handle these issues properly and foster collaboration with external bodies, such as regulators, to ensure accountability and compliance.
Can you have both growth and sustainability?
Culturally, we've always been focused on how to expand, how to get bigger, and that mindset has dominated the business world.
However, there's an alternative approach that emerged during the Covid pandemic which is more "run" than “grow”. Its main aim is to operate businesses to be stable and effective. The question now is whether we should return to the growth-oriented thinking, which is attractive to many, or instead invest in the "run" mindset, which can lead to better outcomes for our existing customers.
At the executive level, businesses still tend to seek investments that promise growth, whether it's expanding the customer base or increasing customer engagement.
On the other hand, there are some early signs of a shift, particularly in response to recent financial crises, where businesses are focusing on helping existing customers manage their finances to avoid hardships.
Although it's still in its initial stages, some companies are starting to reassess their resources and direct investments towards supporting their current clientele. This may well include adding conversational AI into the customer engagement mix.
Instead of fearing AI technology, the credit and collections industry should embrace all that it promises to bring to this sector, particularly when it comes to helping customers.
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