PUBLISHED DATE: 2025-08-25 04:57:52

AI and ML in Financial Services: How to Prepare Your Financial Services Organization for a New Era of Innovation

In collaboration with Google Cloud

Contents

Why Financial Services Firms Need to Act on AI

The hype around artificial intelligence has reached fever pitch, with generative AI becoming a hot topic across podcasts and thought leadership pieces. Businesses in every sector are waking up to the opportunities AI can bring, as well as the significant task of capitalizing on them. Few stand to benefit more than financial services institutions, which have the potential to support—or even replace—archaic, inflexible legacy systems with adaptable, AI-enabled products and platforms. This shift could help future-proof financial services organizations, promote innovation, reduce costs, and ultimately create a smoother experience for both colleagues and customers.

However, adopting AI isn’t just about finding the right tools. The success of any AI strategy depends on whether an organization has invested in the right talent with the skills to support implementation. Google Cloud has been investing in AI for the last 10 years. When polled by Google Cloud, 62% of business leaders said they think their organizations lack the most critical skills to execute their AI strategy. Only 4% said they have the skills needed to achieve their AI goals.

Understanding the practical potential of AI is complex enough for uninitiated businesses, let alone the requirements for adoption. While off-the-shelf solutions are available, it will be the institutions with bespoke, start-from-scratch technology that truly leave their rivals behind.

Let’s take a closer look at how AI and ML could become a part of your business, and how to capitalize on the bold but achievable prospect of harnessing the most powerful AI available.

72% of UK financial services firms are using or developing an ML application.

Steps to Success

AI and ML Business Approaches

AI and ML Value Pools

AI and ML Playbook

Your Strategy

AI and ML in Action: The Services with the Biggest Impact

The business potential for using AI and ML can seem overwhelming, given the sheer scope of potential use cases. Once you have identified the right solution, the next step is to decide where to deploy it first. Below is a framework outlining some of the services with the biggest impact on controlling costs, minimizing risk, and increasing revenue.

Customer

Colleague

Function

AI and ML Business Approaches: Identify the AI and ML Solution You Need

From customization to costs and latency requirements, there is no one-size-fits-all approach in AI. To help organizations narrow down their needs and expectations, here are three levels of products and services to consider. The suitability and success of your organization’s choice will depend on your team’s experience, skill, and confidence in this space. The first step is to choose the right solution for you.

  1. Embedded AI
  2. Core AI Services
  3. AI and ML Development Lifecycle Services

Pros and Cons:

Impacts:

AI and ML Playbook: Tailoring to Your Level of Maturity

The pace of AI and ML is so fast that many already feel left behind. The race to adopt these technologies may lead to over-ambitious programs with ineffective outcomes. Large organizations in financial services must pace their programs correctly to avoid failures. Ensure your business and people are ready for your AI strategy to get a fast start.

New Starters:

  1. Begin with a small scope, aim for soft outcomes, low targets, and lessons for the future.
  2. Choose transparent AI solutions that provide insight into how the AI model makes decisions. This is especially important in regulated areas. Explaining all aspects of an advanced analytical model is necessary but challenging for a budding AI model risk management team.
  3. Choose the right AI and ML platform. Cloud infrastructure is essential for training demanding models. Selecting a cloud vendor AI and ML platform removes many integration challenges and narrows the choice of tools and accelerators, allowing business teams to focus on business value rather than building the best platform.

Advanced Users:

Open standards are critical for leveraging AI progress. As models and providers compete for AI supremacy, adhering to open standards ensures your organization has the best options to enable business outcomes.

  1. Scaling AI and ML usage depends heavily on data. Enabling AI and ML should be a top priority in your data strategy. Break down internal data silos—most organizations suffer from fragmented data estates, outdated governance, and inadequate sharing processes. Leveraging Google Cloud capabilities like BigLake and Dataplex unifies disparate data sets onto a common control plane. With Gen App Builder, developers can use foundation models to build generative chat and search apps quickly, without significant data science or coding experience.
  2. Employ external data sets. The demand for external data in AI and ML is huge, ranging from open data to train and test models using client, sales, and marketing statistics. Timely access to external data is crucial, and data marketplaces like Google Analytics Hub allow for easy discovery, purchasing, and access to up-to-date authentic data.
  3. Data virtualization plays a key role in providing data to your AI and ML environment. International financial institutions must deal with data residency constraints, which can mean some data is only available on-premises. Data virtualization helps bring the required data in permissible form to your AI and ML platform. For more in-depth model customization and data center work, Vertex AI offers developer-friendly APIs and an interface that abstracts many complexities of model tuning, prompt engineering, and other tasks traditionally requiring significant data science expertise.

Considerations: The Road Ahead

The AI landscape is evolving rapidly, with new technologies, techniques, and models emerging daily from both commercial and open-source communities. Financial services firms are facing increasing regulations, and the application of AI will need to be carefully initiated, implemented, and monitored. Other areas also require careful attention:

Conclusions

How to Maximize the Value of Your Generative AI Investment

A single generative AI model will not solve all problems. Bigger is not always better. There is no need for a trillion-parameter model to answer simple questions; tools such as distillation and reinforcement learning mean that smaller models may outperform larger models on specific tasks. Google Cloud provides access to the right model at the right time and cost for your use cases.

Karen Huish, Director of Financial Services, Google Cloud UKI

Discover the difference a world-class AI and ML strategy can make to your business. Contact us to get started.

In collaboration with Google Cloud