How financial institutions can harness the power of AI and ML

Overcoming the challenges associated with these technologies will have big benefits for organisations.

The financial industry has long been dependant on technology innovations. And today, artificial intelligence (AI) and machine learning (ML) are the latest causing a major shift in the sector. As such, business leaders recognise that adopting this technology is no longer optional. A 2023 study by Workday found that 86% of global business leaders in financial services agree that leveraging AI and ML is a requirement to keep their businesses competitive.

“AI and ML are huge innovations that hold lots of promise for almost every industry,” says Dave Sohigian, chief technology officer of Workday, a leading provider of enterprise cloud applications for finance and human resources. “Companies should absolutely invest in this technology, but more importantly, in their understanding of this technology.”

The wide adoption of AI and ML is happening at a moment when banking is becoming more complex with shifting regulations, increasing digital assets, and fintech competition. Not to mention, consumers expect banking to be easier and more personalised than ever before.  

AI promises new capabilities, revenue opportunities, and cost reductions, but many financial institutions have struggled to scale the technologies across these organisations. 

“This is the nature of a hype cycle around this technology,” says Sohigian. “It’s become difficult to discern how to apply it in a way that is actually useful. Workday has been working with AI and ML for close to a decade, so we understand the challenges and the opportunities of this technology.”

AI pain points

When it comes to integrating AI and ML, some of the obstacles holding financial institutions back include cost, worry about data quality, and a lack of talent with the necessary skills.

For financial institutions today, data is the foundation for everything from revenue forecasting and stock price predictions to risk monitoring and customer experience. At its core, AI enhances an organisation’s ability to leverage the large volumes of data generated in day-to-day business activities, enabling it to identify patterns and make predictions. It can drive value creation by automating routine tasks and streamlining IT processes, freeing up people to focus on more critical work. Yet, 77% of senior decision-makers are concerned that their organisations’ data are neither timely nor reliable enough to use with AI and ML. 

“The quality and quantity of data used to train AI and ML determines the accuracy of predictions made by the algorithms,” says Sohigian. “The old saying, ‘garbage in, garbage out’ applies here.”

Often, financial institutions are relying on legacy enterprise resource planning (ERP) systems with siloed data. This means they lack the capacity and flexibility required to support the data processing and real-time analysis needed to process their vast amounts of data and keep up with the speed of business operations. These data limitations are especially difficult to ignore in a business environment fraught with uncertainty. To proactively manage risks and more broadly support planning, forecasting, and performance management, banks need a unified system.

But, with upgraded systems comes new costs and the need for new workforce skill sets. To keep up with this digital transformation, organisations need to upskill and reskill workers to maintain legacy knowledge, adapt to modern IT systems, and address changing customer needs—and they are finding it a difficult task. In fact, 72% of decision-makers think their organisations lack the skills to fully implement AI and ML. And while AI and ML have great potential to reduce costs by making people and processes more efficient—many organisations are actually incurring unnecessary cost.

“The current hype cycle is also obscuring some of the massive costs of some AI and ML technologies,” says Sohigian. “For example, the many ‘free’ AI-enabled chatbots that have risen to popularity take enormous amounts of power, chips, and data centres to create and maintain the large language models that power them.” 

Building a future-ready organisation

Harnessing the transformative power of AI and ML in finance will depend on building a strong technology and skills foundation. To do this, organisations are investing in a modern, cloud-based system and incorporating AI and ML. Today, leading finance organisations are using AI and ML technologies in Workday to help deliver better employee experiences, improve operational efficiencies, and provide insights for faster data-driven decision-making.

Workday is uniquely well suited to power AI due to its Intelligent Data Core, which combines external and Workday data in one place. This enables companies to take the friction out of enterprise accounting and streamline processes for inbound general ledger integrations. It also allows them to access intelligent and contextual insights, support AI-powered automation, such as accounts payable/accounts receivable matching and reconciliation, generative AI–powered report authoring/generation, and more.

“Workday specialises in finance and human capital management (HCM), which are functions required to run practically every business in the world,” says Sohigian. He explains that this specialisation has several benefits, including that Workday can do the “experimenting” on behalf of customers to find the best uses of ML and AI for finance and HCM, as well as make use of the shared data of its thousands of customers.

Cloud-native enterprise platforms can also provide visibility into workforce skills and capabilities and enable users to leverage that data for upskilling, career development, and more effective performance management. 

“Workday has remained committed to keeping the human at the centre of ML and AI,” says Sohigian. “We are enabling people to be more effective and make more informed decisions. This will ultimately fuel the success of this technology and the success of these institutions.”