The impact of artificial intelligence (AI) on the financial services industry promises to be transformative -- reshaping operations, improving risk management and potentially boosting returns.
Banks and insurers are increasingly adopting AI to help reduce costs including headcount while increasing customer satisfaction, enhancing customer offerings and addressing the myriad challenges in a rapidly evolving market.
What’s most interesting to me as an investor in financial equities is that I believe these businesses will be able to monetise the savings and productivity gains from AI, potentially adding an entirely new channel of returns that is unrelated to the economic cycle and that could support a rerating of shares.
Cost cutter
AI is beginning to revolutionise the operational landscape of financial services by automating repetitive tasks and streamlining processes. Research suggests that DeepSeek AI can lower operational costs by up to 25%1. Robotic process automation (RPA) is widely used to handle data entry and document management, significantly reducing errors and operational costs.
AI tools are optimising workflows in areas like tax compliance, fraud detection and contract review, enabling banks to achieve higher productivity while minimising risks. Additionally, AI-powered tools like natural language processing (NLP) help analyse vast datasets for investment decisions and wealth management.
The payments processor Klarna has reported using AI extensively to cut costs. Its customer service AI assistant, powered by OpenAI, performs the work of 700 full-time agents, reducing average resolution times from 11 minutes to 2 minutes while maintaining high levels of customer satisfaction. This contributed to a $40 million profit improvement in 2024, the company reported2.
Klarna has said it aims to sharply reduce its workforce by leveraging AI for efficiency, allowing it to pay remaining staff higher salaries while preparing for an IPO3.
Better bots
AI has elevated customer engagement by enabling more personalised banking services. Barclays leverages AI to enhance customer experience with chatbots and virtual assistants and analyses customer behaviour to offer tailored financial advice, creating a more user-centric experience. This can potentially foster stronger customer loyalty in an increasingly competitive market.
Fraud fighter
One of AI’s most critical contributions is in risk mitigation and fraud detection. Machine learning algorithms analyse large volumes of transactional data in real time to identify unusual patterns indicative of fraud. These systems enhance security and also reduce false positives, improving the accuracy of fraud detection processes. In credit risk management, AI evaluates diverse data points—such as social media activity and spending patterns—to provide more precise assessments of creditworthiness. It’s widely used to evaluate residential mortgage and personal loan applications, for example.
Quicker loans
AI is enabling the development of new financial products and service-targeted upselling and faster loan approvals to drive higher cross-sell rates and new revenue streams. Banks using AI can generate additional revenue by leveraging its predictive analytics capabilities to identify personalised upsell opportunities, i.e. offering additional products and services to a customer. This highlights AI’s ability to enhance customer engagement and improve profitability.
DeepSeek’s AI-driven credit scoring reduces loan processing times by 40%, enabling quicker decisions through advanced data analysis, including alternative metrics like cash flow and social media activity4. This also potentially improves access to credit for underserved demographics.
In my view, the DeepSeek and OpenAI models make them a potential game-changer for financial institutions by combining cost-effectiveness with cutting-edge performance. These advancements are transforming the way financial institutions approach product development.
Stress tests
To be sure, AI adoption in financial services comes with challenges. Issues such as data privacy concerns, algorithmic biases and regulatory compliance pose significant risks. Ensuring ethical AI use requires robust frameworks to address these challenges while maintaining transparency and accountability. Additionally, the rapid pace of technological change necessitates continuous adaptation by traditional banks and fintech startups.
A Bank of England official said recently, for example, that the expanding use of AI by banks is creating new risks for the financial system and could be incorporated into annual stress tests5.
AI is emerging as a cornerstone of innovation in financial services by enhancing operational efficiency, improving customer service, mitigating risk and driving product innovation. Addressing ethical concerns and regulatory challenges will be crucial, of course. Nevertheless, I am expecting this AI revolution to generate improved financial returns for banks and insurers that can be recognised and rewarded through a rerating of their shares in financial markets.
Footnotes
1Itexus blog, 28.1.2025 DeepSeek AI in Banking: Smarter, Faster, and Safer Solutions
2Klarna, 24.2.24 https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/
3Financial Times, 27.8.24. Klarna aims to halve workforce
4Itexus blog, 28 January 2025. As above
5Financial Times, 31.10. 2024. Banks’ use of AI could be included in stress tests
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