How Do Data Engineers Prepare Data for AI Use?

Every financial services firm holds vast amounts of operational data: policy records, trade histories, claims files, client correspondence, scanned documents and spreadsheets built up over years.

When an AI initiative is proposed, it is tempting to assume this data is already usable simply because it exists.

In practice, most of it was created for transactional or compliance purposes, not for AI, and has to go through a deliberate preparation process before any AI system can use it safely or effectively.

Understanding what that preparation process involves helps business stakeholders set realistic expectations, ask better questions and spot when data readiness is being overstated.

Why data preparation is a distinct discipline

Data in financial services is generated to support a specific operational task: recording a policy, settling a trade, logging a claim, satisfying a regulatory return.

It is rarely captured with any thought given to how a machine learning model might later use it to recognise patterns.

This creates a gap between what the data was built for and what AI needs from it. A policy administration system, for example, may store free-text notes, inconsistent product codes and fields that have changed meaning over time as the business evolved.

Data engineers exist to close that gap deliberately, rather than leaving it to be discovered mid-project. Treating data preparation as its own discipline, with its own stages and standards, is what separates a well-run AI initiative from one that quietly inherits the flaws of its source systems.

Traditional data preparation approaches

Financial services firms have long prepared data for reporting, analytics and regulatory submissions, and much of this experience remains relevant.

Common established approaches include:

• Manual reconciliation between systems of record.
• Rules-based extract, transform and load (ETL) processes that move and reshape data on a schedule.
• Data warehousing, which consolidates data from multiple sources into a single structure for reporting.
• Data quality checks built around known regulatory or reporting requirements.

These approaches remain essential and are not being replaced. However, they were largely designed to answer predictable, well-defined questions, such as producing a quarterly regulatory return.

AI use cases ask different questions of the data, often looking for patterns across large, varied and historically inconsistent inputs. Traditional preparation gets the data organised, but it does not, on its own, get the data ready for AI.

Where AI changes the data preparation task

AI use cases typically require additional stages beyond traditional cleaning and structuring.

Collection brings together data from multiple sources, such as email inboxes, broker portals and legacy systems, into one accessible pool.

Cleaning standardises formats, removes duplicates and resolves inconsistencies, for example ensuring that a currency or date field means the same thing everywhere it appears.

Labelling involves tagging data with the correct answers or categories so an AI system can learn from examples. A set of historical submissions might be labelled as "accepted," "declined" or "referred," giving a model something concrete to learn from.

Structuring, sometimes called feature engineering, involves converting raw information into a format a model can meaningfully use, such as turning free-text notes into structured indicators.

Validation checks the prepared data for accuracy, completeness and bias before it is used, including whether certain products, regions or time periods are under-represented.

AI tools can now assist data engineers with parts of this work, for example by suggesting labels or flagging likely inconsistencies. This speeds up the process but does not remove the need for a data engineer, and ultimately a business owner, to review and approve the result.

Operational considerations for business stakeholders

Business stakeholders sponsoring an AI initiative do not need to perform data preparation themselves, but should understand enough to ask informed questions.

Useful questions to ask a data engineering team include:

• Which source systems were used, and are any known to be unreliable or incomplete?
• How was the data labelled, and who reviewed the labelling for accuracy?
• Has the data been checked for gaps or bias across products, regions or customer groups?
• What access controls and data protection measures apply throughout preparation, not just at the end?
• How will the prepared data be monitored and refreshed as source systems change?

Data preparation is rarely a one-off task. As source systems, products and regulations change, the underlying data changes with them, so AI use cases typically require ongoing monitoring and re-preparation rather than a single upfront exercise.

Treating data readiness as an ongoing governance responsibility, rather than a box ticked at the start of a project, is what keeps AI outputs defensible over time.

Example

A London-based specialty insurer wants to use AI to help underwriters triage incoming submissions faster.

Before the model can be built, the data engineering team must pull submission data from email inboxes, broker portals and a legacy policy administration system, then clean, label and structure it consistently so the AI can recognise patterns across historically inconsistent submission formats.

The senior underwriter sponsoring the project learns that most of the timeline is spent on data preparation rather than model-building, and works with the data engineer to define what "good" submission data looks like from an underwriting perspective, improving both the AI outcome and the underwriter's confidence in it.

FAQs

• Why can't AI systems just use our existing data as it is?

  Operational data is structured for transactional or compliance purposes, not for pattern recognition. It typically contains inconsistencies, gaps and formats that AI systems cannot interpret reliably without preparation, so this work has to happen before the data can support an AI use case.

• What is data labelling and why does it matter?

  Data labelling means tagging data with the correct answers or categories so an AI system can learn from examples, such as marking historical submissions as accepted, declined or referred. The quality of that labelling has a direct effect on how accurate and trustworthy the resulting AI system is.

• How long does data preparation usually take compared to building the AI model itself?

  Data preparation is often the majority of the effort in an AI project, frequently taking longer than building the model itself. Rushing this stage to reach the model-building phase sooner tends to create risks that surface later, once the system is already in use.