We review data-enabled processes for error and bias.

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 (depending on risk and your required level of confidence).

Our approach is based on first principles, starting with your expectations and testing from there.
This differs from the typical approach that focuses purely on the technical details.
We find that many errors stem from translation issues (requirements ≠ functional specs ≠ tech specs ≠ build).

Schedule an introductory call to discuss how we can help you achieve trust and confidence.