Published on January 4th, 2025
Introduction
In today’s data-driven world, financial services companies are eager to harness the power of artificial intelligence (AI). However, many are finding it difficult to align their AI strategies with the right data. For AI to work effectively, organizations need accurate, accessible data. Without it, even the best AI systems will fall short. This article looks at the challenges financial services firms face and the steps they are taking to ensure AI succeeds.
The Data Problem: Why AI Needs the Right Data
Financial services companies are excited about the potential of AI to drive innovation and growth. But a key challenge is ensuring AI models have access to high-quality, well-organized data. A survey by Digital Realty, which polled 362 IT decision-makers in the financial sector, revealed some important insights. While 70% of companies are implementing formal AI strategies, many still face difficulties with the foundational data needs of AI.
“Without the right data in the right place, your AI strategy is broken before it starts,” said Dan Eline, VP of Platform Solutions at Digital Realty. The survey emphasized that a strong data infrastructure is crucial for AI. With data expected to grow rapidly, financial services must prepare for the increasing volume of unstructured data. IDC predicts that unstructured data will grow by 21.2% annually through 2026.
The Importance of Data Localization for AI Success
Data localization is critical for AI success in financial services. Having data stored in the right locations helps reduce latency and meet regulatory standards. IT leaders in the financial sector recognize that AI can only be effective if data is stored close to where it’s needed and can be accessed quickly.
Nearly two-thirds of financial services companies have IT infrastructure spread across multiple global locations. By bringing data closer to where it’s needed, firms can reduce latency, improve access, and generate more valuable insights. But it’s not enough to just store data in these locations—it must comply with local laws and regulations.
Addressing Challenges: Investment, Buy-In, and Infrastructure Upgrades
Despite the potential benefits, financial services organizations face several challenges. The survey found that upgrading data infrastructure is the biggest obstacle for IT leaders in the industry, with 56% identifying it as a top concern. To succeed, financial services must invest in data storage, processing, and analytics infrastructure, and gain leadership support to make it happen.
Data privacy concerns and customer reluctance to share personal data also hinder AI adoption. Over 40% of respondents said these issues slow down AI efforts. While customers are increasingly wary about sharing their data, financial services firms must find a balance between collecting data and respecting privacy.
The Role of AI in Enhancing Financial Services Operations
AI adoption is steadily growing in the financial services sector, despite the challenges. According to the survey, 71% of IT leaders believe AI will improve customer experiences, and 66% expect AI to drive product and service innovation. Additionally, 51% of respondents think AI will help increase operational efficiency.
One area where AI is expected to have a big impact is cybersecurity. Financial services are increasingly using AI to monitor for suspicious activities, analyze security events, and respond to threats like phishing emails. More than half of respondents identified risk mitigation and breach management as key outcomes they expect from AI-driven insights.
Overcoming Data Infrastructure Challenges
For AI to succeed in financial services, companies must invest in better data infrastructure. This includes improving storage capabilities and implementing tools for efficient data processing and analysis. IT leaders must also ensure that their data centers can meet the performance demands of AI, especially when it comes to latency.
“Data must be in the right place where AI can ingest it and create more data in a continuous cycle,” said Eline, highlighting the relationship between data and AI.
Conclusion: Charting the Future of AI in Financial Services
Financial services organizations are at a critical juncture in their AI journey. While many face challenges in aligning data with AI strategies, the industry is making significant progress. By focusing on data localization, investing in infrastructure, and addressing privacy concerns, financial services can unlock the full potential of AI. As the industry evolves, AI will play an increasingly central role in driving innovation, improving operations, and securing sensitive financial data.
The future of AI in financial services is promising, but it requires continued investment in data infrastructure and strategies. Only then can financial services companies truly harness the power of AI to drive growth and innovation, while addressing the industry’s ever-changing needs.