Solving the unstructured data dilemma in financial services

From improving compliance to reducing manual workloads, today’s AI tools are helping financial institutions transform customer communications into structured, actionable insights, says Paul Bennett, Managing Partner at Madox Square LLP.

I hear you. Not another highbrow article about AI. But stay with me. What follows isn’t abstract speculation — it’s rooted in what’s already unfolding on the ground. In a recent conversation with Richard Huston, Managing Director at VAMOS, a company building AI solutions specifically for financial services, I heard something different. Rather than grand visions of the future, Huston offered a practical, behind-the-scenes look at how AI is quietly tackling one of the sector’s most persistent pain points: unstructured data. His insights prompted the following.

The unstructured data problem

In the fast-paced world of financial services, few challenges are as persistent or as costly as the management of unstructured data. With thousands of finance proposals flooding in from hundreds of brokers to customer service emails requiring complex policy interpretations, financial institutions have long struggled with the manual processing of information that arrives in countless formats and styles. Yet, as in many areas, recent advancements in AI technology are offering promising solutions to this longstanding problem.

The scale of the challenge is staggering. Consider the typical asset finance lender receiving proposals from brokers across the country. Each broker has their own approach to presenting client information; some prefer lengthy email narratives, others send sparse bullet points, while many submit incomplete documentation across multiple attachments. What should be a straightforward evaluation process becomes a laborious exercise in data archaeology, with operations teams spending hours extracting, interpreting and structuring information before any meaningful analysis can begin.

The rule of 80:20

Emerging AI technologies are now capable of interpreting customer communications and providing intelligent suggestions to operations teams. Rather than promising to replace human judgment, these systems aim to enhance it by automating administrative heavy lifting: the 80 allowing human intelligence (HI), the 20 to focus on oversight and complex decision-making.

Whether it’s a broker’s finance proposal or a customer service enquiry, when unstructured data arrives AI can automatically analyse the content, extract key information and present it in a structured format alongside relevant context from existing business systems. For example, when a customer emails to change their payment date, an AI system can analyse the request, fetch the customer’s account information and payment schedule, and provide recommended actions based on policy and account status.

Beyond simple automation

What makes tech such as this particularly powerful is its ability to understand context and apply policy consistently across all interactions. In the previous example, the system doesn’t just extract basic applicant information, it understands the relationship between different data points, identifies potential risk factors and suggests next steps based on established lending criteria.

When a customer sends an email explaining that they are having financial difficulties, AI can analyse the email and understand the individual circumstances, retrieve relevant policies for customers in financial difficulty and offer staff appropriate options such as reduced payments or a payment holiday. This level of contextual understanding represents a significant leap forward from traditional data processing tools.

Compliance and risk management

In an industry where regulatory compliance is paramount, AI’s approach to policy consistency provides significant value. It can ensure all customer interactions are automatically guided according to policies and procedures, while creating a clear audit trail of circumstances considered for taking decisions and enabling monitoring of customer outcomes. This automated compliance capability is particularly valuable for institutions dealing with Consumer Duty obligations and fair treatment requirements. By embedding policy guidance directly into the operational workflow, AI helps ensure that regulatory requirements are consistently met across all customer interactions, reducing compliance risk while improving operational efficiency.

HI – the human factor

Perhaps most importantly, successful AI implementation must account for the human element. It should reduce pressure on frontline staff by simplifying complex customer interactions, providing clear guidance and support while maintaining human oversight of key decisions and customer interactions.

This human-centric approach addresses one of the most common concerns about AI in financial services – that automation will diminish the quality of customer service. Instead, by handling administrative tasks and providing intelligent suggestions, AI enables staff to spend more time on activities that truly require the nuance of human judgment and empathy.

Looking forward

AI’s potential to solve the unstructured data challenge points to a broader transformation in how financial institutions approach operational efficiency. Rather than viewing AI as a replacement for human expertise, forward-thinking organisations are recognising its potential as a powerful augmentation tool that can handle routine processing while preserving human oversight for complex decisions.

For financial institutions still struggling with manual processing of unstructured data, AI offers a compelling vision of the future – one where technology handles the administrative burden while humans focus on building relationships and driving business outcomes. In an industry where efficiency and compliance are both critical to success, this represents not just an operational improvement, but a genuine competitive advantage.

The question for leaders in financial services isn’t whether AI will transform their operations – it’s whether they’ll be early adopters of these solutions or find themselves playing catch-up in an increasingly automated industry.

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Paul Bennett’s expertise keeps Madox Square LLP on course in the ever-shifting automotive landscape. Offering a blend of strategy, collaboration, and a sharp eye for emerging trends, he’s looking to ensure his clients are well-positioned for the future. And if his rowing machine times are anything to go by, he’ll likely cross the finish line ahead of the competition — Rich Tea biscuits in hand.