AI as a whole has been in discussion for a while and in many aspects, has been implemented within businesses and the finance sector already. The newest kid on the block that is garnering the most attention today however, is generative AI.
Forecasts show that generative AI in the fintech market will reach $6.25Bn by 2032, up from $865m in 2022, and will, also in this period, achieve a CAGR of 22.5%.
Generative AI has in ways already entered the space via the biggest tech discussion of recent months, ChatGPT, the conversational chatbot built by OpenAI, but what about more strategic ways it can be used in the FX space specifically?
We look at some of the key areas that generative AI could have a positive impact in FX, as we see it in these early days.
What is Generative AI?
Generative AI is a subsect of AI that uses data it’s been trained on to generate new outputs. These outputs are based on the context and input given by the user making it extremely versatile and capable of handling a wider range of scenarios.
It has the ability to create a wide variety of data including text, audio, images, video and 3D models, and can handle natural language programming tasks like translation and sentiment analysis. Together this means generative AI can produce highly realistic and complex content that can be indistinguishable from human responses and creativity.
Generative AI in FX
While there is some initial scepticism for ChatGPT to enter into the investment banking sector as various big banks have banned the tool, there is much more to generative AI that can serve the industry, particularly in FX.
When it comes to onboarding new clients, Know Your Customer (KYC) and ensuring due diligence is one of the most important yet inefficient aspects of the process due it still being mostly manual and therefore open for error.
Fraud, such as money laundering, continues to be one of the industry’s biggest issues costing Australians around $4bn last year, which is why KYC technologies are an ongoing investment for financial institutions.
Generative AI can help solve this by providing more precise risk assessment and better intelligence for financial institutions (FIs) by using algorithms and behaviours to learn from. Behaviours and conversations can be analysed increasing the transparency of risk, while automation helps to ensure the verification process is efficient without compromise on critical security.
Risk management strategies
When it comes to FX risk management strategies, understanding what works for clients can be a minefield as several elements need to be taken into consideration, beyond just the figures.
Generative AI can be used to absorb non-numerical data, like risk appetite and the experience of the key stakeholders to generate more fit-for-purpose FX risk strategies. In addition, using this type of information, small variables can be added such as different economic situations or slight changes in risk, to offer a few options that would work for the client as they evolve.
Combining the two elements above, FX hedging products can be vastly improved with generative AI. As the technology learns more from more input, it can help clients to better navigate their way through market situations that may require some restructuring for a better result.
For example, self-serve platforms have brought some much-needed transparency to the murky world of OTC (over the counter) FX hedging, but they still require the customer to have a high level of expertise in financial products and risk management strategies. Generative AI can solve for this gap by largely automating hedging ideation andpresenting business owners and CFOs with timelier and more efficient FX hedge trades.
Generative AI remains flexible and adaptable, keeping hedging products not only relevant to the changing demands of the market, but also through greater KYC, financial institutions are able to get a far fuller understanding of their customers and their needs. It can also generate stress-test scenarios that conform with compliance and regulations, while looking at cost reduction for a more strategic approach for FIs.
Essentially, by not being limited to certain inputs, the technology can look at environments and situations more laterally and can therefore produce very relevant suggestions and offerings.
While generative AI is still in its infancy and there has been some opposition to its first iteration from financial institutions, there are plenty of ways that it can provide greater efficiency for them while uplifting the user experience within the FX space.
Of course, more needs to be understood and learnt to ensure that the datasets being used are correct and the algorithms and learning is producing solid and secure results that meet the needs. However, generative AI has huge potential to significantly reduce risk, create more transparency in this very foggy environment and offer greater accessibility and relevance.
It will never be able to predict and offer a fail-safe solution in the FX market, but it can help many to better navigate it with a more personalised and informed product offering.