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Fair and accurate use of data in financial services

Fair and accurate use of data in financial services

We help FS leaders ensure their use of data does not disadvantage or harm customers.


We do this by uncovering potential biases, inaccuracies, and unfair practices in algorithms and data flows.



Operational decisions take many forms.

They can be manual, reliant on reports, semi-automated or fully automated.

They typically look something like these:

  • Reports with data from multiple sources, that you rely on to make operational decisions (e.g., sales KPIs)

  • Regular reports that you use in operating key controls (e.g., reconciliation reports)

  • Regulatory reports (e.g., capital adequacy calculations)

  • 3rd party commission calcs that you use for external payments (e.g., to brokers for loan origination)

  • Fraud risk rules and models, flagging potentially fraudulent interactions (e.g., fraudulent insurance claims)

  • Automated fee or payment calculations without human intervention (e.g., automated bank fee postings).


Think about all the inputs into those – data, data flows, transformations, models, calculations, reports.

There are many components that need to be designed (and programmed) perfectly.

One errant line of code or data quality issue can spell disaster. 

  • How can you be sure that the inputs are correct?

  • Can you trust that the transformations are error-free?

  • How can you confirm that the flows align with their objectives?

  • Do your rules and models promote bias, or are they fair and equitable?

  • How can you be confident that your systems are generating accurate results?


That's where we come in.

We review those flows, at various levels of depth.

We start with understanding how the process is meant to work, then test thoroughly against that.


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Report Validation

Ensuring the accuracy and integrity of your reports, providing comfort that you can rely on the content.

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Process Integrity

In-depth reviews of key process flows, incl. data sources, data transformations, calculations, models, etc.

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Bias Assessments

Identifying potential biases in decision algorithms and processes, to promote fairness and objectivity.


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