Transformations

Turning what you hold into something usable: cleaned tables, structured data out of documents and free text, and synthetic records where the real ones are too few.

Transformations Deliverables

Data Cleaning

We clean and reshape the data you already hold, such as resolving duplicates, imputing what is missing, and joining and merging sources into the tables your reporting and models need. Alongside that we build the checks that tell you whether the data can still be trusted: quality rules that catch erroneous values, anomaly detection, drift reports comparing this run against the last, and distribution monitoring that raises a flag when values shift significantly even though every other check passes. Alerts go to the people who need to act on them.

Enquire

Document Extraction

We turn unstructured sources such as contracts, reports and scanned documents into queryable data, held in a standardised format you can use directly as model variables, refreshed automatically, with a confidence score identifying what needs human review.

Enquire

Sentiment Analysis

We turn free text such as complaints, notes and reviews into quantifiable measures you can track over time.

Enquire

Synthetic Data

We generate realistic artificial data for cases you have too few of, or cannot use directly, such as a set of customer complaints written to test whether the language models in your workflow catch what they should.

Enquire

Data Case Studies

View All Case Studies

Data

A business banking ECL engine the risk team runs itself

A UK bank wanted its business banking risk team to be able to run expected credit loss itself, rather than wait for someone else to run it.

Frequently Asked Questions

This is where most reported figures actually go wrong, so the useful partner is the one who insists on a single definition before writing any code. At Gini we build the layer that turns source records into model-ready inputs: one definition of default, arrears, balance and exposure, applied consistently, with tested logic and a traceable path from raw record to reported number.
Yes, and it is a common starting point. Gini traces each version back to the query that produced it, establishes which definition the reported figure should use, and implements it once in a layer both teams consume. The disagreement is nearly always a definition rather than a data error, which is why fixing it in one place holds.
At Gini we write automated tests that run on every change and on every load: row counts and totals reconciled to source, referential checks between tables, and assertions on the fields a model depends on. A transformation that silently drops five per cent of accounts passes visual inspection and fails a test, which is the whole argument for having them.
Yes. Gini rebuilds them as tested, version-controlled code that reproduces the existing figures exactly, evidenced line by line so you can see it agrees before you retire the old process. What comes out runs unattended and no longer depends on whoever wrote the original file.

Transformations