Written by Chris Livesey, CEO at AutoRek
Financial services firms are facing a growing challenge. As transaction and data volumes continue to rise year-on-year, 79% of firms report struggling with capacity constraints that traditional operational methods simply can’t handle. When volumes increase exponentially but processing capabilities don’t, something has to give.
The solution lies in technology that scales with demand. AI agents offer a practical solution to this problem.
Autonomous agents bring a new approach to financial reconciliation – systems that can evolve, learn and strengthen when handling complex data scenarios. This represents the next evolution in financial operations, and as adoption accelerates across the industry, the window for competitive advantage is narrowing.
Spreadsheet Dependency Persists
Despite automation efforts, spreadsheet dependency prevails. A recent AutoRek report found that 90% of organisations still rely on these labour-intensive processes as a key component of operations. The challenge is, as companies increase in size and complexity, they hit an ‘automation ceiling’ – the point at which traditional robotic process automation (RPA) and workflow tools prove insufficient, causing diseconomies of scale and decreasing ROI.
Continuing to rely on traditional methods not only causes operational issues but exposes organisations to significant compliance risks. This is a growing concern as most professionals anticipate a tightening regulatory environment in the next two years as the FCA extends their CASS regulation scope to include e-money and payments firms.
The associated increased pressure on safeguarding and data reconciliation processes means that firms have an obligation to modernise their operational infrastructure.
Beyond Basic Automation
The solution to current shortfalls lies in organisations harnessing the power of modern agentic solutions. While current RPA tools are better equipped to deal with routine tasks than manual spreadsheet input, solving complex analytical processes remains beyond their capabilities. A recent report by McKinsey suggests that using AI to redesign workflows is the best way to approach this challenge, with autonomous agents marking a significant transformation in industry operations.
AI agents can autonomously interpret, learn and adapt to nuanced scenarios in real-time, functioning as “digital colleagues” that can process increasing volumes of complex data without human intervention. Their ability to seamlessly scale operations in line with business growth is a key benefit – maintaining compliance without the need for ongoing investment in headcount.
Scalable Architecture
The question remains: what are these AI agents capable of?
- Staying compliant: Scalable cloud infrastructure allows banks to deploy AI agents without compromising on security or regulatory requirements. AI agents can be equipped with built-in compliance and audit technology compatible with CASS, MiFID II and DORA regulation. This allows businesses to stay up to date with essential safeguarding regulations.
- Integration without disruption: APIs and connectors allow seamless integration with existing core banking systems, eliminating the need for costly infrastructure overhauls. Their ability to continuously learn from reconciliation patterns supports accuracy as businesses scale, preparing them for mergers, acquisitions and changes in ownership.
- Real-time processing at scale: AI agents can handle millions of data points simultaneously, processing complex data reconciliations in seconds rather than hours. These real-time insights allow organisations to identify operational issues as they happen, using built-in escalation protocols to prevent costly setbacks.
The Competitive Edge
With more than 50% of companies planning to use autonomous AI within the next two years, the potential benefits can often feel overwhelming. Utilising technology for technology’s sake has never ended well, which is why a clear business purpose is fundamental to successfully leveraging AI for long-term gain.
Agentic AI is not simply a cost-saving tool. For firms hoping to achieve autonomous reconciliation even with volatile, unstructured, high value data, value creation must underpin all business objectives.
- Customer Experience: When 94% of people believe customer experience is essential to business success, it becomes a critical priority for organisational growth. AI-powered automated reconciliation tools deliver on this priority, providing instant settlement confirmation and real-time account updates that directly improve customer satisfaction.
- Speed to Market: Banks that use AI agents can deploy new products and services faster than their less-adaptive counterparts, as their back-office operations scale automatically. This reduces time-to-market and ensures that organisations remain at the forefront of industry demand – improving both financial outcomes and customer relationships.
- Risk Reduction: AI agents are essential to risk reduction, being able to identify anomalies and compliance issues in real-time. This reduction in human error has moved from being a competitive advantage to a standard expectation.
- Future-Proof: Agent-based systems represent scalable, future-proof infrastructure. Their ability to continuously adapt to new regulations and market requirements without manual reconfiguration sets them apart from traditional systems. By investing in this technology, organisations ensure long-term competitiveness, avoiding costly overhauls as they scale.
The Bottom Line
Ultimately, banks that embrace agent-powered operations today will be better positioned for tomorrow. The goal isn’t to undervalue human expertise, but instead to amplify it and allow financial professionals to collaborate with AI agents across the business ecosystem. Interoperability underscores the need for seamless human-AI collaboration, and the transition is already underway.