In an industry driven by data and innovation, financial services organizations are increasingly turning to artificial intelligence (AI) as a transformative solution to meet evolving demands.
From traditional banking institutions to cutting-edge fintech startups, AI has emerged as a game-changer, revolutionizing the way financial services operate and delivering benefits to businesses and customers.
As the adoption of AI gains momentum, the technology is poised to reshape the landscape of finance, enabling organizations to enhance decision-making processes, streamline operations, mitigate risks and ultimately offer more personalized and efficient services to consumers.
A recent report by JLL shows financial services companies plan to spend $31 billion worldwide by 2025, which will also result in greater demand for IT professionals including data scientists, software developers and machine learning (ML) engineers.
Sarah Bouzarouata, senior manager, research, JLL says it was interesting to find that the banking sector is projected to deliver the largest AI investments in 2023, as this strategy is shaping banks’ commercial real estate portfolios.
“Despite the broader contraction in office space recently, some of the leading banks have also been strategically growing their tech operations by expanding their office presence in markets that offer concentrated pools of niche, tech-centric talent,” she explains.
Bouzarouata says the development and execution of an AI adoption strategy among financial institutions would require the input and alignment of several key roles.
“Depending on the bank, this can include the executive leadership team, head of global tech strategy, chief data officer, head of innovation or AI transformation, and head of AI research,” she says.
Mark Shank, principal, advisory with KPMG, agrees the responsibility for developing and executing an AI adoption strategy in financial businesses typically falls on a cross-functional team led by senior executives, including the chief technology officer (CTO) and CDO.
“This team works closely with stakeholders from various departments such as IT, data analytics, compliance and customer service to align business goals with AI implementation,” he says.
He explains collaboration and coordination among these key stakeholders are crucial to ensure the successful development, deployment and integration of AI technologies within the organization.
Shank adds financial services firms should start adopting and implementing AI because it offers tremendous opportunities for efficiency, accuracy and cost savings.
“AI can automate repetitive tasks, analyze large volumes of data for insights, enhance fraud detection and provide personalized customer experiences,” he says.
Shank says he thinks its impact on the financial services sector will be “extremely significant”, transforming operations, improving risk management, enabling more accurate decision-making and creating new business models and revenue streams.
He points out financial services firms face regulatory hurdles in implementing generative AI due to concerns around data privacy and security.
“The use of generative AI models, which create new data based on existing patterns, raises questions about the ownership and authenticity of generated information, potentially conflicting with data protection regulations,” he says.
Additionally, the lack of interpretability and transparency in generative AI algorithms can make it difficult to comply with regulatory requirements for explainability and auditability of automated decision-making processes.
Amber Schiada, head of Americas work dynamics and industries research for JLL, points to multiple customer-focused use cases for AI adoption among financial institutions.
“Customers are most exposed to AI technologies via customer service channels: Call centers and online client portals,” she says. “AI is revolutionizing the entire customer experience, similar to the way ATMs revolutionized 24-hour access to funds.”
From her perspective, it goes beyond automated call centers – customers can tap into insights on predictive spending habits, budgeting tools, investment platforms and fraud prevention tools, just to name a few.
Shank points out AI systems can optimize costs by automating repetitive tasks, reducing the need for manual labor and increasing operational efficiency.
“This automation can lead to significant cost savings for financial institutions,” he says. “AI can also analyze large volumes of data to identify patterns and insights, enabling better decision-making and resource allocation, further optimizing costs.”
Additionally, AI-powered predictive analytics can help financial institutions identify new market opportunities, customer segments and innovative product offerings, supporting the development of new growth models and revenue streams.
Schiada adds that as AI strategies continue to scale, the need for the right talent to develop and grow these platforms will be imperative.
She points out highly skilled technology talent has become more dispersed over the last decade because of the industry’s rapid expansion and outgrowth from Silicon Valley.
“Financial institutions have been tapping into new markets to scale their technology workforce, and we’ve seen more companies focused on places like Dallas, Austin and Atlanta where the talent and the cost of doing business are more cost effective,” she says.