By Johnny Steele, Head of Banking at SAS UK.
Businesses in the financial services sector are known for taking a measured approach to adopting new technologies. However, recent trends indicate that this approach is changing, particularly with the advancement of artificial intelligence (AI) and notably generative AI (GenAI).
Wall Street banks, which were once cautious in adopting cloud-based services due to data security concerns, are increasingly recognising the transformative potential of GenAI as a critical tool in their operations. This shift reflects a broader transformation across the financial services sector, where AI is no longer seen as a futuristic add-on but as a vital component of modern banking operations.
According to a recent report by the Bank of England (BoE) and the Financial Conduct Authority (FCA), 75% of UK financial services firms are already using AI with a further 10% planning to use it over the next three years. But the impact of this usage will be felt in the coming years, since respondents expect the median number of use cases to more than double over the next three years.
Adopting GenAI in Banking
Banks are increasingly leveraging GenAI to enhance efficiency, reduce costs, and provide more personalised services to customers.
Research has revealed 60% of banking leaders are already using GenAI, with 17% having fully implemented it into their regular processes recently. The research by SAS and Coleman Parkes, also revealed 38% of those leaders who are not yet using GenAI plan to do so in the next two years.
Banks are applying GenAI across various departments and business functions. Compared with cross-industry averages, banks use GenAI at a higher rate in:
● Marketing (47%): For generating personalised campaigns and analysing customer engagement.
● IT (39%): To optimise systems and automate responses to technical issues.
● Sales (36%): To create tailored product recommendations and enhance lead management.
● Finance (35%): To forecast trends and automate financial reporting.
● Customer Service (24%): To enhance chatbot interactions and improve self-service tools.
Within this transformation, AI agents are emerging as powerful tools. AI agents are autonomous or semi-autonomous systems designed to perform tasks such as customer interaction, fraud monitoring, and operational support. For example, some banks use AI agents to handle customer inquiries in real time, while others deploy them internally to automate repetitive tasks like report generation or compliance checks. SAS’ Intelligent Decisioning solutions play a pivotal role in this space by providing real-time decision automation capabilities. These solutions ensure that decision-making processes remain transparent, auditable, and compliant with regulations, while also significantly enhancing efficiency.
Synthetic Data: The Future of AI Training
In addition, synthetic data (algorithmically generated data that mimics real-world data and a product of GenAI), is becoming an invaluable asset in this shift. Banks handle large amounts of sensitive data daily, and using real data in AI training models can raise privacy concerns. According to the report by the BoE and FCA, of the top five perceived current risks among UK financial services firms, four are related to data: data privacy and protection, data quality, data security, and data bias and representativeness.
Synthetic data generation addresses these concerns by creating anonymised, realistic data sets that mimic the patterns of actual data without exposing private information. This enables banks to train their AI models more effectively and securely, enhancing decision-making while maintaining strict data governance standards. SAS has just acquired Hazy, a pioneer in synthetic data technology, to expand our capabilities in this area and provide customers with unique opportunities to safely leverage data, experiment with new scenarios, and gain a competitive edge.
The use of synthetic data is also making a big impact in risk management and fraud detection. Unlike traditional AI, which focuses on detecting existing patterns, GenAI can simulate potential fraud scenarios by creating advanced, adaptive models that can not only identify patterns in data but also generate new scenarios to test and improve security measures.
GenAI can generate synthetic transaction data to simulate various types of fraudulent behaviour, which helps models learn from a broader set of examples and become more effective at identifying subtle, emerging patterns in real time. This enhances the ability of banks to detect suspicious activities early and take swift preventive action.
Additionally, GenAI is being used to personalise banking experiences through analysing customer data to offer ever more tailored financial products and services which meet individual needs.
AI Governance, Compliance and Regulatory Reporting
GenAI is being deployed in the industry to streamline compliance processes and ensure banks meet regulatory standards more efficiently.
Traditional compliance methods often involve manual checks and extensive paperwork, which are time-consuming and prone to errors. Whereas GenAI can automate these processes, reducing the risk of non-compliance and allowing banks to stay ahead of regulatory changes.
One of the key areas where GenAI is making a difference is in regulatory reporting. Banks are required to submit regular reports to regulatory bodies, detailing everything from financial transactions to risk assessments. GenAI-driven tools can automate the collection and analysis of data required for these reports, ensuring accuracy and timeliness.
For example, synthetic data can help banks test their compliance systems more rigorously by simulating various regulatory scenarios without relying on sensitive customer information. This not only reduces the administrative burden on banks but also improves the quality of the reports, helping banks to maintain compliance more effectively.
GenAI is being used to monitor compliance in real time too. Whilst traditional AI relies on historical data, GenAI can create new, hypothetical compliance scenarios, training systems to anticipate and recognise evolving patterns of non-compliance, such as insider trading, money laundering or other illegal activities.
When irregularities are detected, GenAI-powered systems can immediately alert compliance officers, who can then investigate and take action, all while continuously learning from new scenarios generated by the model. This proactive approach helps banks to address compliance issues before they become significant problems, reducing the risk of penalties and reputational damage.
However, this increased reliance on AI in compliance necessitates a strong focus on AI governance. Ensuring transparency, accountability, and explainability of GenAI systems is critical to maintaining trust with regulators and customers alike. Governance frameworks must be in place to monitor decisioning outputs, ensure ethical AI usage, and prevent model drift or unintended biases. SAS’ solutions are designed with governance at their core, enabling financial institutions to maintain robust oversight of their GenAI applications while driving innovation.
The Future of GenAI in Banking
As banks continue to integrate GenAI into more of their operations, the technology is expected to play a significant role in shaping the future of the financial services sector.
Synthetic data will continue to evolve, particularly as governance frameworks mature to ensure its responsible use. As GenAI becomes more sophisticated, it will enable banks to offer additional personalised and responsive services, enhancing customer satisfaction and loyalty.
Overall, while banks were initially cautious, they are now embracing GenAI as a critical tool for modernising their operations. As the financial services sector continues to evolve, GenAI will undoubtedly remain at the forefront of this transformation, helping banks to navigate challenges and opportunities.
