Dean Berry
Dean Berry, Head of Trading & Banking Solutions at LSEG, reflects on the impact of AI in financial services.
- Artificial intelligence is poised to bring sweeping changes to the financial services sector.
- With its capacity to process vast datasets, glean insights and automate complex tasks, AI presents both opportunities and challenges.
- This blog covers some of the main themes that emerged during a recent event at which LSEG brought together industry experts for a discussion on AI in financial services.
Everyone is a student when it comes to AI. We’re collectively shaping how we manage the risks while making the most of the transformative opportunities ahead
Emily Prince
Panelists:
- Adrian Crockett – General Manager for Microsoft Cloud for Industry – Capital Markets
- EJ Achtner – Managing Director, Office of Applied AI at HSBC
- Stephen Flaherty – Group Chief Technology Officer and Head of Group Technology Infrastructure Services at Barclays
- Dr Biswa Sengupta – Managing Director and General Manager of AI Products and Cloud Platform for Corporate & Investment Bank (CIB) at JPMorgan Chase
- Emily Prince – Group Head of Analytics at LSEG
Generative artificial intelligence (GenAI) – of which ChatGPT is one example – is trained to create or generate new content such as text, images or even entire pieces of software without having been programmed by humans. It relies on deep learning models to understand patterns and structures within existing data and use that understanding to produce new, similar data. GenAI is a powerful tool for problem-solving in multiple industries, automating tasks like content generation, data augmentation and even assisting in drug discovery.
Short-term efficiency gains and long-term possibilities
In the short term, the panellists agreed, AI is expected to boost efficiency as never before. Automated customer service systems and chatbots are already streamlining operations, enhancing customer experience and reducing costs.
Financial institutions are also benefitting from AI large language models’ (LLMs) capabilities to summarise text and process language. (LLMs are programs that have been trained on a massive amount of text so that they can understand and generate human-like language.) This rapid analysis of financial reports, news articles and market data allow professionals to make informed decisions quickly. The ability to sift through vast amounts of information and distil key insights is transforming how analysts and portfolio managers work.
Looking ahead, the speakers highlighted certain long-term benefits for finance from AI, such as personalisation and cross-industry innovation. AI-powered personalisation can revolutionise customer engagement by tailoring financial products and services to individual preferences.
Creative thinking and experimentation with AI open avenues to cross-industry innovation. Drawing inspiration from other industries could spark further innovations – customer segmentation methodologies from retail could be applied to financial services to offer tailored investment options, or techniques for disease prediction and prevention could be adapted to detect fraudulent activities or assess credit risk. By thinking beyond traditional boundaries, financial institutions can capitalise fully on AI’s transformative potential.
One speaker also explained that hybrid intelligence – collaboration between teams of humans and AI – can achieve results far superior to anything either could accomplish alone. Organisations should invest in educating their teams and fostering AI literacy to prepare their people for a changing landscape and give themselves a competitive advantage.
The power of data
Another of the participants emphasised that data lies at the heart of AI’s transformative potential. In the financial services sector, data comes in many forms – among them structured financial data, unstructured customer interactions and unstructured market sentiment data from news and social media (which itself opens new avenues for predictive analytics and risk assessment). GenAI’s ability to process this wealth of information with speed and precision has profound implications for many aspects of the industry.
Data quality, accessibility and diversity are paramount – they are the fuel that powers AI algorithms. Financial organisations are investing heavily in data collection, storage and management systems in a bid to harness the full potential of AI.
Risk management and responsible AI
Risk management is at the forefront of AI adoption, with an emphasis on robust risk assessment, validation and compliance processes when deploying AI models. Responsible AI practices, including transparency, fairness and ethical considerations, are key.
Several of the speakers cautioned that the opaque nature of GenAI models risks returning results whose accuracy and provenance cannot easily be articulated – if at all. For this reason, explainability – the transparency of individual decisions or predictions made using AI models – and interpretability – the overall comprehensibility of the AI system, making it accessible to non-experts – is paramount. AI-driven decisions should be understood and justified by users, one participant said, not only to satisfy regulations but also to maintain customer trust.
EJ Achtner, Managing Director, Office of Applied AI at HSBC.
"A/GAI technology has potential to be transformative in a positive way, but there is also the risk of unintended consequences as the technology is evolving rapidly. In order to ensure responsible and ethical uses that create beneficial outcomes, we must be disciplined around a range of measures, as example control frameworks and thoughtful regulation, combined with a high degree of transparency and collaboration.
As the broader community, we have the ability to consider a series of shared standards and principles that will go a long way towards providing comfort to a range of stakeholders, which will be necessary to demonstrate an emerging products and capabilities move into scalable production-grade solutions that customers find value in"
A balanced approach
Taking a balanced approach to AI adoption is crucial, an opinion echoed by all the panellists. By succumbing to hype for the sake of chasing trends, organisations risk failing to solve specific business problems. LLMs’ capacity for efficiency gains offers substantial benefits in the short term, but for other applications the shift will require well-thought out, effective change management processes. However ground-breaking the tools and technologies available, it is important not to forget that the problems the industry is solving for are fundamentally the same as ever: operational efficiency, customer experience and regulatory transparency.
In summing up, the panel agreed that when harnessed effectively, AI has the potential to enhance operations, elevate customer experiences and drive innovation in the financial services sector. By aiming for data quality, responsible AI practices, collaboration and a clear understanding of AI’s problem-solving capacity, financial institutions can successfully navigate this dynamic landscape.
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