Traditional artificial intelligence (AI) has been instrumental in changing how accounting and finance professionals approach laborious tasks like fraud detection, marketing optimisation, compliance, claims processing, and more.
The rigid programming of traditional models creates limitations. Traditional AI doesn’t perform well in unexpected situations and lacks adaptability. Seeking more options and flexibility, financial institutions have invested heavily in generative AI — language models that improve customer service, summarisation, content creation, and more.
As generative AI and large language models (LLMs) are further integrated by management accountants and other finance professionals, don’t stop using traditional AI just yet.
In this transforming technology landscape, it’s not about hopping from one emerging trend to the next but constructing robust IT systems that work together to enhance customer, employee, and shareholder satisfaction.
Financial services workflows enhanced
Thanks to its increased accessibility, generative AI has fundamentally changed content creation because of its like-magic capabilities to create text, images, audio, and more. Generative AI powers responses and generates new content, whilst LLMs are trained on vast amounts of data and can predict what words come next in a sequence.
Organisations and financial institutions are already investing massively in generative AI, and there’s room to do more. This technology became popular quickly because it frees management accountants to do other, higher-level, more strategic tasks.
With its many uses, generative AI can continue to revolutionise financial services, as explored in Bain Capital Ventures’ article: The State of Generative AI in Financial Services. Generative AI is an enhancer — best embedded within an organisation’s pre-existing technologies. Unlike traditional AI, generative AI can simplify the complexity of customer questions, exercise judgement, and solve unexpected problems.
For example, generative AI can power a custom-built virtual assistant that specialises in expense reporting or a risk underwriting model that’s integrated with OpenAI. You can use generative AI to write marketing messages better tailored to individual users, enhance the question-and-answer capabilities of a chatbot, or assist with risk modelling, fraud detection, or purchase order reconciliation.
How an organisation implements generative AI is up to them. They can purchase a pre-built application that’s designed for one use, leverage and customise a ready-made LLM, or create an LLM that’s built in-house and trained on a specific set of data. Internal goals, time, and budget influence the choice.
Generative AI: A component, not the whole product
Generative AI can’t replace traditional AI because its programming doesn’t lend well to the same tasks, and language models aren’t good at maths. Quantitative tasks perform better using traditional models, and generative AI has more flexibility in terms of required data inputs and tasks performed. And the technology to run generative AI programmes is more expensive — limit its use so not to waste resources.
Generative AI is another tool in the toolbox and a great compliment to traditional AI. The intent is not to discontinue traditional AI but to fill in the capability gaps and work in tandem.
Although there are a few scenarios in which generative AI may prove disruptive in financial services — too much technology-related debt or if accessing the required amount of data is prohibited by pre-existing data agreements — the benefits outweigh the challenges. Established organisations can harness the power of generative AI by integrating it into their existing technology portfolio.
Generative AI: New era of service expansion
financial services. It’s improving and expanding it. Once effectively included into the system, this kind of generative AI is not disrupting sustaining technology is a performance enhancer for pre-existing technologies. It boosts the customer service experience and delivers a low-cost solution with less human intervention.
Organisations will need trained professionals who are comfortable with both traditional and generative AI models — not to mention other emerging digital technologies.
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