How brokers can unlock AI’s full potential - even with messy data

Artificial Intelligence (AI) is transforming the mortgage industry, helping brokers streamline operations, analyse trends, and improve efficiency. From automating routine tasks to identifying behavioural patterns in client interactions, AI acts as a virtual assistant, allowing brokers to work smarter.

However, AI is only as good as the data it relies on. Without a well-structured, high-quality dataset, even advanced AI systems struggle to deliver meaningful insights. While many brokers manage structured data—such as client details and financial records—unstructured data like meeting notes and call recordings is often overlooked. Yet, when properly organised, it provides deeper client insights and supports better decision-making.

This article breaks down how brokers can more effectively capture, organise and store both structured and unstructured data to fully harness AI’s potential.

Why capturing both structured and unstructured data matters

For AI to generate accurate and meaningful insights, brokers need to manage two key types of data:

• Structured data – This is neatly organised, and often stored in predefined formats like spreadsheets or databases. Examples might include client names, income details, property values, and loan statuses - anything that’s easy to process and analyse.
• Unstructured data – This refers to any information that doesn’t fit neatly into a database, such as email conversations, recorded calls, handwritten meeting notes and customer interactions. AI can still extract helpful insights from unstructured data, but only if it is captured and organised correctly.

AI relies on clear patterns to generate insights; disorganised unstructured data can create confusion that causes AI to misinterpret fragmented information, causing issues like inaccurate loan recommendations or misguided risk assessments. Without structure, valuable insights remain hidden, reducing AI’s effectiveness in improving workflows and client service.

Think of AI as a mortgage underwriter. When a loan application is incomplete ( i.e. missing key financial documents or supporting evidence) the underwriting process slows down or leads to inaccurate decisions. AI operates on the same principle: feed it incomplete, disorganised data, and it can only produce flawed insights.

How structured data turns AI into a powerful tool

When brokers maintain well-structured datasets, on the other hand, AI can:

• Identify patterns – Predict client behaviour, spot market trends, and optimise workflows.
• Streamline processes – Automate time-consuming tasks, such as follow-ups and document collection.
• Enhance risk management – Flag inconsistencies, identify compliance risks, and detect fraudulent activity.

With proper organisation, AI is better able to efficiently process, categorise and extract relevant information that helps brokers identify client needs, predict borrowing patterns and personalise communication, leading to stronger client relationships and higher conversion rates.

How data organisation can help (or hinder) your AI efforts

• Client interactions: AI can analyse meeting transcripts, emails, and call recordings to identify customer preferences, potential deal success and areas of concern.
• Custom AI assistants: AI-powered chatbots and virtual assistants can provide more accurate, personalised responses, especially when trained on well-structured internal documentation.
• Automated insights and trend detection: AI can flag risks, predict client preferences, and assist in compliance processes, though only if it has access to complete and organised data.

Capturing unstructured data effectively

Unstructured data is everywhere - hidden in emails, woven into conversations, gathering dust in call logs. This data holds immense potential, offering deeper client insights, improving automation, and enhancing decision-making. The brokers who harness it effectively will gain a competitive edge, as AI can analyse patterns within these interactions that would otherwise go unnoticed.

AI’s ability to work at scale means it can process vast amounts of data in the blink of an eye—but only if that data is captured and organised efficiently. A practical approach is to use tools that automatically record and transcribe client interactions, including: 

Automation tools – Voice-to-text software can convert spoken interactions into searchable text, making it easier for AI to extract key information.

Centralised data storage – Store all client interactions (calls, emails, notes) in a unified system to ensure easy access and consistency.

Standardised note-taking – Use a structured format for meeting notes, including key takeaways and next steps, to make AI-driven analysis more effective.

Maintaining clean structured data

Even the most advanced AI system can’t compensate for messy, inconsistent data. Without regular maintenance, structured data can become cluttered with duplicate records, incorrect entries and outdated information.

Standardisation is key. Set clear protocols for data entry, ensure contacts are properly tagged, and schedule routine data hygiene checks. These small steps lay the groundwork for making better decisions, faster.

The benefits of organised data for brokers

A well-structured strategy frees up time for other, more high-level tasks like deepening client relationships or identifying opportunities. Leveraging both unstructured and structured data also means you can work more efficiently and provide better service, enabling you to:

• Gain deeper insights into client needs by analysing call transcripts and email conversations.
• Automate follow-ups with AI, drafting responses based on past interactions to ensure timely communication.
• Strengthen compliance and risk management by using AI to flag inconsistencies before they escalate.
• Streamline workflows, reducing admin time and allowing brokers to focus on building stronger client relationships.

Steps to start capturing and managing data today

Begin with a data audit to spot gaps - where is unstructured data slipping through the cracks? Then, introduce tools that simplify data capture and integrate emails, call logs, and meeting notes in one place. Standardise note-taking for consistency across the team, and commit to ongoing maintenance - even the best data strategy needs regular upkeep.

Good AI adoption starts with data discipline, and while its potential is undeniable, it only works if you feed it high-quality, well-structured data. By building better habits today, your future AI tools will do the difficult work for you - like summarising call notes, drafting follow-up emails, and organising client records - so you can focus on your clients’ needs and providing expert advice.

For those looking to deepen their understanding of AI’s role in the mortgage industry, explore our AI Glossary. 

Find more resources on AI on our topic page here.