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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.


Many operational decisions can inadvertently disadvantage or harm customers.

These decisions, whether manual or automated, often rely heavily on data or algorithms.

They include:

  • 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).


They can involve complex data flows, transformations, reports and/or algorithms.

Without proper oversight, these processes can inadvertently lead to unfair treatment or harm to customers.

For example:

  • Biased data/models could result in unfair loan rejections for certain demographic groups

  • Inaccurate fee calculations might overcharge/undercharge customers

  • Aggressive fraud detection could wrongly flag legitimate activity.


That's where we come in.

Our role is to examine these decision-making processes in detail.

We analyse the data inputs, algorithmic logic, and outputs to ensure they're fair and accurate.

By doing so, we help you maintain customer trust, protecting them from unintended harm or disadvantage.


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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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