This one took me a second to get into because it sounds like a soft stakeholder question until you realize they actually want you to get into the numbers.
Frame the problem as an optimization between fraud loss and customer friction, and propose a data-driven approach using experimentation and continuous monitoring. Emphasize collaboration with product and risk teams to define acceptable thresholds and iterate based on metrics.
Pro tip: Propose a multi-armed bandit or reinforcement learning system to dynamically adjust thresholds, showing you understand both the trade-off and scalable solutions. Also, highlight the importance of segmenting customers to apply different risk tolerances.
Work with stakeholders to quantify the costs of false positives (blocking legitimate transactions) and false negatives (allowing fraud), and establish acceptable thresholds for each.
Propose A/B tests or switchback experiments to measure the impact of different blocking aggressiveness on key metrics, ensuring statistical power and avoiding interference.
Choose a balanced set of metrics: fraud rate, false positive rate, customer satisfaction (e.g., NPS), transaction success rate, and revenue impact. Monitor them over time with dashboards and alerts.
Use experiment results to adjust thresholds, potentially employing adaptive algorithms like multi-armed bandits to continuously optimize the trade-off.
Regularly share findings with product and risk teams to maintain alignment and adjust strategies as business goals evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.