Article

Transforming financial services with AI

Name: Implement Consulting Group
Price range: $

Published

9 January 2025

Financial services stand at a crossroads, where the integration of AI can bridge gaps in efficiency, accuracy, and customer satisfaction.

From our experience working with AI in financial services, we know that apart from understanding the transformative potential, a key challenge for the industry is also understanding where and how to get started, as well as, in some cases, realising the potential already accounted for in future budgets.

This article explores the transformative potential of AI in financial services, providing examples of future possibilities as well as areas where potential can be realised today.

Why integrate AI?

Financial services are uniquely positioned to benefit from AI, as the digitisation and accessibility of vast amounts of data are inherently a part of the financial services industry today. As an industry based on data, yet with a substantial footprint with its customers, the applications are vast. Data sources range from transactional records to customer profiles and exist in all kinds of formats and levels of accessibility.

The abundance of data in financial services means that processes and information can be analysed with high accuracy, which is the playing field of AI. However, historically, these data requirements have posed a processing challenge, with enormous amounts of data needing to be gathered, moved from unstructured sources, and shifted across systems and functions. AI is the solution to all of this and more, promising to transform the industry by automating and enhancing resource-intensive processes.

In financial services, unlike many other industries, the data that AI needs is readily available due to the transparency and documentation requirements within the industry. This makes AI integration an ideal solution for improving efficiency and accuracy in the industry, including workflows related to generating and managing the data itself and the workflows that rely on the data.

In short, AI can streamline the entire end-to-end value chain of financial services, from internal processes, complex data gathering, and analysis all the way to customer interactions. This end-to-end automation reduces the need for human intervention, cutting costs and minimising the risk of errors – all of which allows skilled labour to focus on complex exception handling.

AI across the financial services value chain

The potential of AI today extends from complex tasks to more routine customer service interactions. The next generation of conversational AI, powered by large language models, is here and can handle increasingly complex customer queries across languages, while also integrating seamlessly into backend systems for flow automation. This capability allows banks to offer 24/7 customer support, and in some routine cases even exceed the quality of service delivered by human employees, while freeing human agents to focus on more nuanced issues.

Moreover, AI can streamline entire internal processes by integrating various functions into a cohesive workflow. Historically, financial services has been dominated by processes being split into sub-stages, with a heavy focus on optimising specific sub-steps and handovers. With AI, this can be flipped on its head. AI agents capable of data gathering, analysis, and rules-based execution offer a true one-and-done potential for processes end-to-end, as well as the enhancement of complex processes, cutting costs, and minimising the risk of errors.

Deep-dive

AI use cases

1. Understanding customer behaviour with deep learning

One of the most promising applications of AI in financial services is leveraging deep learning to model customer behaviour. By analysing vast amounts of transactional and relational data, AI can predict behaviours and detect anomalies more accurately than traditional methods.

For instance, using techniques like graph neural networks, banks can gain a better understanding of the relationships between accounts and transactions, leading to more effective fraud detection and Know Your Customer (KYC) compliance.

2. Case study: Enhancing fraud detection

A mid-sized bank implemented a deep learning system to monitor transactions in real time. The AI model was trained on historical transaction data and could identify suspicious activities with high precision. Within months, the bank saw a significant reduction in fraudulent transactions, saving millions of dollars and enhancing customer trust.

Immediate applications and future prospects

While the full automation of banking processes is a long-term goal, financial institutions can adopt several immediate applications of AI today. For example, AI can enhance fraud detection and KYC processes to reduce the burden on compliance teams. Additionally, customer service can be significantly improved with AI-powered chatbots and voice bots, providing personalised and efficient support.

However, regulatory compliance and transparency remain critical concerns. Financial institutions must ensure that AI systems are not only accurate but also explainable. This means developing models that provide clear, understandable reasons for their decisions, which is crucial for maintaining regulatory compliance and customer trust.

Conclusion: Embracing the AI future

The integration of AI in financial services is not just a possibility; it is an inevitability. Financial institutions must recognise the transformative potential of AI and strategically adopt these technologies to stay competitive. From enhancing customer service to streamlining internal processes, AI offers numerous benefits that can drive growth and efficiency.

As AI continues to evolve, financial services must keep pace, ensuring that their AI systems are transparent, compliant, and capable of delivering superior value. By doing so, they can harness the full potential of AI and lead the way in the future of financial services.