Smart Savings: How AI Can Drive Cost Reduction in Financial Services

The financial services industry faces constant change, from rapid technological advances to new regulations and evolving customer expectations. For financial services companies to succeed today, they need to strike a delicate balance between fostering innovation and cutting costs.

Traditionally, firms used expensive third-party solutions to pinpoint where they can make savings. But the rise of AI and machine learning (ML) is now transforming this process – offering cost-effective ways to improve financial operations while enhancing the quality of services offered.

Taking steps towards cost reduction

Machine Learning and AI – specifically Generative AI using Large Language Models (LLMs) – can automate tasks, improve productivity, and reduce the need for manual labour. By investing in these technologies, financial services companies gain the added benefits of increasing competitive advantage and improving customer experience.

The application of these technologies is constantly evolving and influencing the industry in ways businesses couldn’t have imagined just a few years ago. For instance, some relatively simple AI use cases that can be deployed almost immediately include writing and testing code, helping developers to be more agile and to launch new services and products faster. AI can also be used to provide multilingual customer services that enable customer support representatives to communicate effectively with customers who may not speak a bank’s primary language. The technology can also accurately translate contracts and other business documentation.

In the medium term, AI can further expedite cost reductions in areas such as compliance, risk management, and security. For example, by personalising complex regulatory requirements and extracting key insights from vast volumes of text. This would make it easier for financial services firms to understand and comply with regulations. Additionally, the automation of compliance monitoring not only saves time but mitigates the risks associated with manual compliance. Generative AI can also automate the process of rapidly creating regulatory reports more accurately and consistently than people, helping to cut down time spent manually reporting and the risk of human error.

Once these use cases have been mastered, financial services institutions can use AI to shape long-term strategy. An Enterprise Knowledge Base (EKB) powered by generative AI could take chatbots to the next level, for example. This enables instant and personalised responses to customer queries on transaction history or loan information and even recommending new products, reducing the need for human intervention and helping to sell new features. AI can even automate research and reporting by gathering, analysing, and reporting financial data and market trends for faster decision-making, as well as optimising portfolios by assessing risk, helping to improve returns.

Getting AI-Ready

The rise of Generative AI has sparked discussion about new use cases and business benefits, but to drive real value from the technology, financial services firms must treat their data as a foundational resource. Ensuring data quality and accessibility is crucial, and companies need to start thinking of ‘data as a product’ and treating it as such. This means making data more portable and giving AI access to a complete set of quality data, regardless of whether it sits in on-premises data centres or in the cloud. This will build the reliable data sources and pipelines that AI needs to thrive – because it’s only possible to get accurate output from LLMs if the input is of suitable quality.

Overcoming Business Challenges with AI

Whether it’s automating processes to improve efficiency, or AI-driven chatbots to enhance customer service – it’s clear AI will play a crucial role in cost reduction within the finance sector. But to achieve the desired outcomes from AI and drive value from it, it’s essential to train models with data that enables AI to flourish. Deploying a modern data architecture will be a key step on the journey to AI cost reduction. This will enable financial services firms to integrate AI into their decision-making processes, and enable it to help shape short-, medium- and long-term strategy.