Financial services must deliver value at the speed and scale required by today’s fast-paced market. This involves delivering a wide range of digital transformation priorities, from enhancing customer experience, adopting generative AI, increasing cyber security, and updating legacy systems.

The realisation and harmonisation of data assets is also a necessary initial step towards embarking on the exploration of the power of AI.

Financial services face challenges in continually expanding their customer experience and the range of products available to customers. Data-driven insights enable firms to build closer relationships with their customers by better understanding their needs and tailoring their offerings accordingly.

An iterative approach to generative AI and its challenges

Generative AI has the potential to profoundly alter our professional and personal lives. It can enhance collective team effectiveness, optimise workflows, streamline analysis, and  facilitate decision-making within organisations. Financial institutions are actively experimenting with AI through proof of concepts (PoCs) and pilots across various use cases.

Financial services remain cautious about customer-facing AI use cases due to potential security and data privacy risks. In particular, firms need to minimise the risk of generative AI producing incorrect or inappropriate information to customers.

The industry is taking an iterative approach, starting with less risky customer-facing use cases like chatbots that use sentiment analysis to create more seamless customer interactions. There is also the opportunity to deploy AI into back and middle office areas where there is less risk of an erroneous customer facing engagement.

Are you interested in how businesses can balance urgency and caution when adopting generative AI ? This article from BJSS explores AI governance, the risks of rushing or hesitating, and how companies can make informed decisions. Here we share insights that can help your organisation navigate the ever-evolving world of AI and ensure effective implementation.

Cyber security in modern banking

Cyber security is a critical concern for financial services. Attacks are becoming more sophisticated, with deepfakes, KYC complications, and fraud posing significant risks. Financial services firms are making significant investments in improving security against cyber-attacks, experimenting with AI to correlate data for early warnings of attacks.

Modernising legacy systems

Modernising legacy systems is essential for delivering products to customers faster. Many legacy systems are costly to maintain and require significant effort to support. Financial services are looking to implement generative AI to understand legacy codes that cause translation issues for developers. Moving to cloud-based systems could be an answer, but there are often regulatory issues to address.

As previously mentioned, the realisation of the value of their data assets, is also a first step towards this.

Conclusion

Financial services are addressing the opportunities and challenges posed by adopting AI to deliver their banking services. They see AI as a way of enabling them to deliver personalised and more effective services to their customers while protecting them from the increasing risk of cyber-attacks. The adoption of cloud technology and the management of data assets are crucial for transforming legacy systems and moving on from technical debt.

The BJSS eBook, The Open Finance Opportunity explores how financial services firms can leverage data sharing to drive revenue growth and enhance customer experience.

Hear from Google Cloud directly in our webinar about how to overcome hurdles in AI adoption for Financial Services.