One of the questions we ask when we're looking for potential bias in our processes, models and...

They should not get in the way of treating your customers fairly.
Race, gender, age and other personal characteristics are not fair game.
Including them in decisions can be prejudicial.
How can you be sure that you have identified all sources of potential bias?
Have you considered proxy indicators and free text fields?
How can you trust that your customers are being treated fairly?
We review your rules and models for bias, at various levels of depth (depending on their nature).
Ensuring you can trust that your automation does not promote unfair results.
One of the questions we ask when we're looking for potential bias in our processes, models and...
In-Flight vs. Post-Implementation reviews: model build and refinement Data-driven (or data-enabled)...
Many operational decisions are now heavily reliant on automation - data, models, rules etc. We’re...
In-depth reviews of key process flows, incl. data, transformations, calculations, models, etc.
Ensuring the accuracy and integrity of your reports, providing comfort that you can rely on the content.