AI in financial services: 5 insights from the TechFuture panel

Thomson Reuters livestreamed its TechFuture financial services webinar, with business leaders and experts weighing in on the power of artificial intelligence (AI) and data-driven technologies for transformational change.

As the financial services landscape continues to rapidly evolve, one thing is certain: AI is here to stay, and harnessing its power is crucial for future success. A recent McKinsey study estimates that the overall value potential of generative AI in the financial services sector is about 3-5% of global banking revenue, which translates to $200-$340 billion dollars annually.

Reuters’ Editor at Large, Axel Threlfall, guided an insightful and layered discussion with leaders from Challenger Limited, Synechron Technologies, McKinsey & Co and University of Sydney. The Panel shared the opportunities, challenges and use cases of AI to an audience largely made up of C-level executives and Finance and/or Technology professionals.

In this article, we will explore the top five takeaways from the discussion. 

1.     Humans to decide AI’s trajectory

McKinsey Partner and data expert, Sanjna Parasrampuria stated that companies embracing an ecosystem approach for their AI strategy are more likely to thrive, particularly considering that many organisations, especially in financial services, are not inherently tech companies.

The key lies in identifying the right AI model and integrating it effectively within the organisation’s technology architecture. While there are numerous applications, the focus should be on leveraging AI to deliver transformational value, rather than merely focusing on cost-saving and improved productivity.

Moreover, organisations must invest in continuous training and upskillling to align with the changing nature of work.

“We as a race will answer how we put AI to use – whether to augment and accelerate ourselves or to displace ourselves. It’s not about productivity and cost play, but rather how we leverage AI to unlock its true potential.” Sanjna Parasrampuria, McKinsey & Company

2.      AI’s success hinges on effective data governance, not efficiencies

Synechron’s Senior AI Engineer, Ivan Peric, shared that the key conversations they are having with their clients is around the benefits and efficiencies of generative AI. However, he stressed the greater need for cautious and careful evaluation of AI models to address issues related to privacy and data security.

AI models, including generative AI, are not meant to replace humans with efficiencies but rather to assist them. While generative AI has tremendous potential, its reliability, ‘explainability’, and privacy concerns must be addressed to ensure responsible use.

Explainability refers to the ability to track all data back to a reliable source in order to address the issue of hallucinations. Collaborating with academic researchers and practitioners becomes vital in tackling these challenges and promoting the adoption of AI technologies more effectively. Ultimately, AI’s success hinges on the quality, security, and lineage of data.

“AI models, including generative AI, are not here to replace humans but rather to assist them in eliminating mundane tasks and allowing them to focus on more meaningful endeavours.” Ivan Peric, Synechron Innovation

3.     Augmenting human capability and ESG

Challenger Bank’s Chief Executive of Technology, Kate Ingwersen highlighted the inevitable impact of AI on the workforce. While it has the potential to truncate certain roles by automating repetitive and manual tasks, AI does not eliminate the human component entirely.

Organisations should view AI as an assistive tool to augment human capabilities rather than viewing it as a threat to job security. Human accountability and decision-making remain essential, especially in complex and strategic aspects of financial services.

The regulatory environment is an area that will always need a human component to oversee the outputs of the models. With the looming threat AI poses on causing biases with potential negative consequences, Challenger partnered with CSIRO to research the impact of AI on ESG and how a company’s ESG credentials can be assessed for investing.

“AI will change the nature of work by removing mundane tasks, but the human component will always have a vital role to play, especially in decision-making and accountability.” Kate Ingwersen, Challenger Limited

4.      Balancing productivity and people strategy

Sydney University Professor and AI Specialist, Ben Hachey, shared valuable insights from research studies on AI’s impact. There are many claims of productivity boosts” says Ben, “however the first real numbers are only coming through now.”

He revealed details of a Stanford University study, where chat bots were used for customer support. Content was retrieved from a large language model’s (LLMs) knowledge base of well curated facts to help control hallucinations, and then fine-tuned to “recommend a response.”

While the results revealed a 14% boost in productivity across the board on average, Ben suggests the need for a more holistic approach, taking into account not just productivity gains but also factors like customer sentiment, employee retention, and knowledge sharing, all of which also improved.

“AI’s impact extends beyond productivity gains; it influences employee sentiment, knowledge sharing, and customer experience, opening up new employment segments and enhancing overall efficiency.” Ben Hachey, University of Sydney and Ergo AI

5.      The roadmap to successful AI adoption

Adopting AI requires a strategic and collaborative approach. Organisations must foster a culture of innovation and encourage cross-functional collaboration to address challenges, ensure ethical AI deployment, and maximise the potential of AI and data.

The role of technology leaders like CTOs can drive this to help create an environment where AI enhances human potential rather than replacing it. Part of this approach involves an organisation’s investment in upskilling their workforce to adapt to the evolving AI landscape and empower employees to harness AI’s capabilities effectively.

While the demoncratisation of data has AI has removed access constraints previously limited to IT/Tech specialists, you can’t just plug and play these AI tools, particularly within big organisations where legacy systems and privacy has to be considered.

Careful design and evaluation are critical in ensuring AI integration brings positive outcomes, including new employment segments and increased efficiency, while emphasising safety and ethical considerations.

“To secure a competitive advantage and future success, Financial Services are scaling AI and machine learning. Those that succeed can vastly increase their ROI. Those who don’t risk losing their market share.” Sanjna Parasrampuria, McKinsey & Company

Unleashing the power of AI and data in financial services

As the financial services industry continues to experiment with the transformative potential of AI and data, it is crucial to strike a balance between productivity gains and people strategy. 

By educating users and clients on AI’s potential and building trust through responsible and transparent AI applications, organisations can unlock new opportunities to drive innovation, stay ahead in the competitive landscape and shape the future of the industry.

Watch the TechFuture webinar playback: Unleashing the Power of AI and Data in Financial Services 

Most download resources from the webinar: AI and cyber-enabled financial crime tops risk agenda, Thomson Reuters Cost of Compliance 2023 Report and Generative AI regulations considered for China EU, and US

Related reading: Technology trends in financial services: How much are we willing to give up to AI?

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