Transform financial services with AI: Unlock growth, innovation, and insights

The financial services industry (FSI) has faced mounting challenges in recent years from navigating the rapid acceleration of digital transformation during the COVID-19 pandemic to managing the fallout of economic downturns. These pressures have forced leaders to rethink traditional approaches and find ways to do more with less. A common strategy is the consolidation of tools and investment in technology designed to foster agility and data-driven decision-making. However, despite these efforts, over 70% of leaders still struggle to use data in real time and at scale. 

As AI and generative AI (GenAI) continue to evolve, they offer new opportunities to unlock the value of data — provided organizations can establish robust data foundations. So, how are today’s financial services leaders rising to these challenges and using next-generation AI to drive their data maturity?

We surveyed 1,005 C-suite, business, and technology leaders on the current state of their business with data and results specifically from 158 financial services leaders. The research reveals five key insights about their business challenges, underlying data problems, and investment priorities (AI, GenAI, and automation) as they catapult their organizations to the next level in the next 12 months and beyond.

Here are five lessons from financial services leaders on how to solve business challenges with data and AI.

In financial services, 77% of C-suite leaders and decision-makers believe that their organization is more advanced in data analytics and intelligence than their peers’. This heightened self-confidence can happen when leaders overestimate their progress in their data maturity journey. 

Discrepancies between self-perceived versus actual data maturity:

• 69% of FSI leaders who believe that they were at level 3 or level 4 data maturity have not completed all of the level 1 milestones.

• 61% of FSI leaders who believe they are at level 4 maturity have only completed about half of the level 2 milestones.

• 66% of FSI leaders who believe they are at level 4 maturity have not completed all level 3 milestones.

A data maturity framework offers an objective way to assess your organization’s current capabilities, identify weaknesses, and create a roadmap for aligning data strategies with business goals. Advancing through each level of data maturity is essential, as foundational milestones enable the adoption of advanced technologies like AI and GenAI. Without a robust data foundation, poor data quality can lead to flawed insights and hinder innovation.

Investing in data technology and AI has become a game-changer for businesses, offering more than just operational improvements. While automating tasks and streamlining workflows enhances productivity and reduces costs, the true potential lies in creating new revenue streams. Over 75% of financial services leaders agree that using real-time data ingestion and AI-driven insights can significantly boost revenue, underscoring the critical role of these technologies for business. This consensus highlights the critical importance of data and AI in contributing to the bottom line. 

The benefits extend beyond efficiency. FSI leaders highlight improved employee and customer experiences as top outcomes from data and AI investments. By combining robust infrastructure with advanced analytics, organizations can empower teams to make informed decisions, uncover new opportunities, and deliver exceptional experiences. Embracing AI as a core capability not only addresses current challenges but also positions your business for sustainable growth and long-term leadership in the industry.

To remain competitive, financial services leaders are integrating AI, automation, and analytics into a cohesive strategy. This approach enhances decision-making, streamlines operations, and drives innovation. With almost 90% of leaders prioritizing these technologies, adopting generative AI is essential for sustainable growth and success.

Informed adoption of GenAI can position you ahead of competitors by creating new opportunities and driving innovation. To stay ahead of the adoption curve, you must first have good data ready to go. Then, identify a high-impact use case that can benefit from the value of a large language model (LLM). 

Getting the best results securely requires feeding your proprietary data to a GenAI algorithm using retrieval augmented generation (RAG). This technique contextualizes the output of your organization, resulting in more accurate and relevant results.

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