Canadian Employer Whitelisting Criteria Development
Budget: $15 – $25 USD
I need a concise, data-backed framework that will let our fraud engines automatically “pass” legitimate Canadian employers and commonly used business addresses without compromising the overall risk posture of the bank. The task breaks down into four tightly connected pieces.
First, please map out a repeatable verification flow that relies on Canada 411 and Yellow Pages as the primary proof points for phone numbers and street addresses. Note any edge-cases where those repositories fall short and suggest secondary look-ups only when absolutely necessary.
Second, design a clear taxonomy for business size. I would like to anchor this on number of employees—define the numerical cut-offs that make sense in the Canadian context and document any regulatory references you use to justify them.
Third, specify the core data points the decision engine must capture so we can weight each entity’s previous fraud history against its verification results. Explain how that history feeds into a risk score and how the score determines whether an employer or address is whitelisted, flagged for manual review, or rejected.
Finally, translate the above into an explicit risk-appetite statement that executives can sign off on, including recommended thresholds and monitoring triggers.
Deliverables
• A written framework (Word or PDF) covering verification flow, size taxonomy, data requirements, scoring model, and risk-appetite statement
• A simple decision matrix or flowchart that compliance teams can follow
• Brief implementation notes for engineering (field names, data types, expected API inputs/outputs)
I will consider the job complete once the framework is clear enough for our developers to code the logic and for compliance to audit the results without further clarification.
First, please map out a repeatable verification flow that relies on Canada 411 and Yellow Pages as the primary proof points for phone numbers and street addresses. Note any edge-cases where those repositories fall short and suggest secondary look-ups only when absolutely necessary.
Second, design a clear taxonomy for business size. I would like to anchor this on number of employees—define the numerical cut-offs that make sense in the Canadian context and document any regulatory references you use to justify them.
Third, specify the core data points the decision engine must capture so we can weight each entity’s previous fraud history against its verification results. Explain how that history feeds into a risk score and how the score determines whether an employer or address is whitelisted, flagged for manual review, or rejected.
Finally, translate the above into an explicit risk-appetite statement that executives can sign off on, including recommended thresholds and monitoring triggers.
Deliverables
• A written framework (Word or PDF) covering verification flow, size taxonomy, data requirements, scoring model, and risk-appetite statement
• A simple decision matrix or flowchart that compliance teams can follow
• Brief implementation notes for engineering (field names, data types, expected API inputs/outputs)
I will consider the job complete once the framework is clear enough for our developers to code the logic and for compliance to audit the results without further clarification.