Case Studies | FinTech

Turning unstructured data into clean, usable records

LLM Powered Data Transformation

About

A FinTech business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The challenge had three focus areas, each concerned with getting messy input into a dependable, structured output.

Ingesting many unstructured formats

Data arrives from API calls, CSV files and web scraping, with no shared structure between them. The tool needed to accept all of these inputs and interpret them consistently, rather than requiring a bespoke parser for every new source.

Finding and merging duplicates

The same entity often appears more than once across sources, in slightly different forms. The solution needed to detect duplication and merge those records intelligently, so downstream data reflects one accurate version rather than several conflicting ones.

Condensing and normalising fields

Beyond de-duplication, fields needed to be condensed and standardised into a consistent shape, turning verbose or inconsistent inputs into concise, usable records.

Solution

At the centre of the design is a large language model running on Amazon Bedrock. Unstructured inputs from the client's various sources are passed to the model, which interprets each format without a hand-written parser for every case and extracts the meaningful content.

Changes

The engagement scoped a generative AI pipeline that addresses all three focus areas, replacing manual clean-up with an automated, model-driven transformation step. The outcomes below describe the intended capability of the solution as designed with the client.

  • Format-agnostic ingestion — A single tool that accepts API, CSV and web-scraped inputs, removing the need to build and maintain a separate parser for each source.
  • Automated de-duplication — Duplicate records are detected and merged by the model, improving the accuracy of the underlying data.
  • Consistent, condensed records — Fields are normalised into a predictable structure, so downstream systems and teams work from clean data.
  • Reduced manual effort (projected) — By automating clean-up that was previously manual, the pipeline is expected to free engineering and operations time for higher-value work.

With the transformation pipeline defined, the client has a repeatable pattern for onboarding new data sources: point the tool at a new format and let the model handle interpretation, de-duplication and normalisation, rather than commissioning bespoke engineering each time.

AWS Stack

Amazon Bedrock

For the large language model that interprets unstructured inputs and performs de-duplication, merging and field condensing.

Amazon S3

For durable storage of source inputs and the cleaned, structured output.

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