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Technology that can analyse vast volumes of data instantaneously is a dream come true for industries that are time-poor and data-heavy. In that context, the financial services industry could have the most to gain from advances in artificial intelligence.
Generative AI (Gen AI) will become a crucial assistant for financial services professionals, helping them stay ahead of market trends, stress-test financial models, and make data-driven decisions in seconds.
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The technology’s potential in this sector is unbound. From enhancing the customer experience, streamlining risk and compliance, to improving market intelligence and portfolio management, artificial intelligence is set to reimagine every corner of the sector.
A workday free of risk and compliance
Following the global financial crisis of 2008, financial institutions are legally bound to comprehensively demonstrate their ability to withstand severe economic crises.
With these increasing regulatory requirements, the ‘paperwork’ has slowly started to dominate the workday for many in the industry, leaving little time for strategy and analysis.
Artificial intelligence, however, could do the heavy lifting and make the workload more manageable, especially for risk and compliance teams.
By generating synthetic data, AI can simulate a wide range of adverse market conditions based on historical events, current market conditions, and potential future risks. This is also an integral tool for stress-testing, helping ensure the validity of financial models.
Essentially, risk teams will have a built-in assistant, ready to help forecast a bank’s exposure to fluctuations in interest rates, credit, liquidity, and even the possibility of default. Further, it could even help detect the warning signs of corporate bankruptcies.
Fighting fire with fire
In recent years, cyber attacks and digital fraud have emerged as one of the biggest risks facing the sector. As fraudulent activity grows in sophistication, particularly as bad actors turn to AI themselves, financial services firms should turn to AI and fight fire with fire.
It’s well known that financial services store crucial and highly confidential data for their customers, with payment information some of the most coveted data cyber criminals target. This, unfortunately, means the industry is constantly in the cyber crime crosshairs.
AI could help mitigate this threat by anticipating new patterns of fraudulent activity that don’t yet exist, enabling security teams to identify previously unknown fraud patterns.
But where the high volume of data could help security teams uncover novel approaches to fraud, it is also the biggest obstacle for any AI implementation.
Without a data strategy, there’s no AI strategy
Because of the sensitive nature of the sector’s data, the industry cannot turn to public-facing large language learning models (LLMs). This means organisations should be implementing private LLMs trained on their own enterprise data.
For customised LLMs to be reliable, however, they need to have access to large volumes of accurate data.
LLMs are exceptionally good at finding patterns in data and predicting what should come next. They do this by analysing the data they have access to and learning from their own responses to previous queries. If the data is too narrow or inaccurate, this compounds the risk of incorrect responses and hallucinations.
Financial institutions, therefore, should look to collapse the silos between their myriad data sources. A modern data strategy is more than just migrating data warehouses to the cloud. Financial services organisations need a model that de-silos data across the enterprise to power many different use cases across lines of business. Here, AI can help with this exact process.
Having a single AI data platform allows governance and access policies to be applied universally while also ensuring any LLM is trained with a complete view of the organisation. By putting meaning behind data, AI can streamline how teams gain access to traditionally untapped data and accelerate the ability to retrieve it.
A further benefit is that generative AI apps can be deployed next to your data. This accelerates innovation as roles and access permissions can be honoured without developers needing to configure them on the back end for every single new use case.
In other words, this allows institutions to bring their apps to their data, rather than the other way around.
This is why an AI strategy should only be implemented alongside an exceptionally strong and unified data strategy.
In the financial services industry, artificial intelligence has the potential to completely reinvent the sector.
While financial organisations have the most to gain from a considered and strategic AI implementation, they also have the most to lose if projects are pursued without a strong data strategy in place.
Theo Hourmouzis is the vice-president – Australia and New Zealand for data cloud applications platform and AI-services provider Snowflake.
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