Technology
The AI revolution: How financial services can utilise automation
Published by Jessica Weisman-Pitts
Posted on November 1, 2023
5 min readLast updated: January 31, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on November 1, 2023
5 min readLast updated: January 31, 2026
By Devashish Mishra, Head of Solutions – Europe, Persistent Systems
Over the past few years, artificial intelligence (AI) has been reshaping various sectors, including the financial services industry.
With major cloud computing providers like AWS, Google, and Microsoft integrating AI capabilities into their products as standard, automation has become an essential part of everyday business operations – whether workers are aware of it or not. One thing employees are certainly aware of however is the rise of generative AI (GenAI).
The emergence of GenAI platforms in the mainstream – think Claude and ChatGPT– has brought automated technologies to the forefront of the attention of executives and boards.
And with applications spanning marketing, digital services, and operational efficiency, it’s evident that AI is offering banks a wealth of new opportunities for conducting business. Opportunities the financial services sector is eager to invest in.
Enhanced efficiency with AI
So far, AI technologies have primarily been used for swift data processing, helping to expedite data-related tasks that would otherwise require –often lengthy– manual processes. But with the help of GenAI, the use cases for automation increase dramatically.
At the most basic level, corporate functions can benefit through more efficient generation of memos and other internal documents. And for copy-heavy departments, GenAI can lighten their workload significantly.
Take content marketing, for example. Marketing executives previously reliant on manual work can now utilise generative models to create promotional materials at scale. Everything from copy to imagery can be created with just a prompt using large language models like ChatGPT or image generators like Midjourney.
Marketers in the financial sector can also employ GenAI-enabled platforms to streamline routine tasks, such as customer data segmentation and sentiment analysis, making it not only easier to produce but also to comprehend reports.
Depending on the AI model in place, executives can even pose questions to AI bots instead of scrutinising multi-page reports, enabling them to make data-driven decisions more efficiently.
Benefits for customers
In addition to benefits within corporate functions, customer support can also be simplified.
Conversational AIs can help make interactions between the customer and the bank more accessible and convenient, with bots addressing frequently asked questions.
High-net-worth individuals (HNIs) can also receive more valuable insights into their investments, with AI producing investor analysis and reports that quote the data source they are derived from. Access to a bigger wealth of data can make investment decisions easier, faster, and certainly better informed.
AI for personalisation
Looking forward, AI also holds the potential to help banks and financial institutions offer highly personalised products to reach previously underserved segments of the population, as well as the next generation of bank users.
Employing automated processes allows financial institutions to process vast amounts of data collected from sources such as social media, enabling them to create personalised offerings for new users.
As AI continues to progress, so will the level of personalisation, enabling banks to swiftly introduce niche offerings for specific groups of people, unlocking new revenue streams.
Whether the new generation of bank users are looking to start a business or purchase a new car, offerings will be tailored and marketed to them specifically using personalised content provided by large language models.
Ensuring secure AI systems
As financial institutions embrace AI, especially GenAI, discussions have arisen concerning how automation can protect or potentially compromise customer data. For instance, the concern with GenAI lies in its lack of explainability.
The financial services sector has always focused on ensuring AI can justify its decisions, but with large language models, this becomes unattainable.
Until we discover a way to reveal the reasoning behind AI decisions, GenAI will likely remain at the periphery of banking, primarily used for marketing and data segmentation, to mitigate any risk of sharing confidential data.
Ultimately, those interested in integrating AI into their operations will need to establish the appropriate structure and security measures, striking a balance between AI’s capabilities and the protection of user data.
In most cases, this entails creating data silos where AIs only access information intended for a specific purpose, effectively eliminating the risk of unintentionally exposing sensitive information.
The future of the AI revolution in finance
The fast adoption of automation technologies in recent years has challenged the prevailing notion that banks are slow to adapt to digital transformation. It also proves that AI in the financial sector is not a fleeting trend but a transformative influence that is here to stay.
But, in order to fully harness AI’s potential, financial institutions must consider the best uses of AI for their business – AI is not a silver bullet nor a replacement for human workers.
Working with the right solutions partners will allow those who want to embrace AI to find the most beneficial ways of utilising it. Only when AI is integrated successfully will organisations position themselves ahead of the curve and reap the benefits of improved efficiency, enhanced security, and personalised customer experiences.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans, enabling them to perform tasks such as data analysis and decision-making.
Automation in financial services involves using technology to perform tasks without human intervention, improving efficiency and accuracy in processes such as data entry, transaction processing, and customer service.
Customer personalization refers to tailoring products, services, and communications to individual customer preferences and behaviors, often enhanced by data analysis and AI technologies.
Operational efficiency is the ability of an organization to deliver products or services in the most cost-effective manner while maintaining high quality, often achieved through process improvements and technology.
Generative AI platforms are advanced AI systems that can create content, such as text, images, or music, based on input data, enabling applications in marketing, customer service, and more.
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