Find laundering across 22 million parties when rules are mandatory and most alerts are false: alert scoring, peer and graph features, below-the-line testing, mule rings and GNNs, on three one-hour boards with the theory.
Machine-learning engineers designing data, training and model systems.
Your approach: Explain the data, learning or model lifecycle in the brief. Support relevant quality and operating targets with evidence.
Design Anti-Money-Laundering Transaction Monitoring. Find laundering across 22 million parties when rules are mandatory and most alerts are false: alert scoring, peer and graph features, below-the-line testing, mule rings and GNNs, on three one-hour boards with the theory. Work from the scoping questions below. State assumptions for any unspecified load, guarantee or target, then trace your design end to end. Explain one difficult case and a credible alternative; the worked example is a reference, not a required implementation.
Resolve the scoping questions for Anti-Money-Laundering Transaction Monitoring. Separate stated behavior from assumptions, and identify what is outside your design.
Declare relevant volume, latency, freshness, quality or cost targets with units. Show calculations or an evaluation plan that can test them; unspecified targets are your assumptions, not hidden pass criteria.
Explain how your guarantees hold in a difficult case relevant to this subject. Address: Explanations? Confidentiality?
Identify users, required behavior and exclusions. Answer: Goal? Scale?
Define the information owned by the system and the inputs, outputs and errors at its boundaries. Resolve: Outputs? Labels?
Estimate the dominant workload and resource demand with units and explicit assumptions. For a learned system, also state how quality is measured and what data is available.
Draw or describe the responsibilities needed for Anti-Money-Laundering Transaction Monitoring. Trace a representative request, event or job from its input to a visible result; identify durable state owners.
Walk through a difficult case step by step, including detection and recovery. Consider: Freshness? Rules? Explanations? Confidentiality?
Compare a credible alternative using your chosen workload and guarantees. Explain a remaining risk, a signal to watch and when you would change the design.