Ride-Hailing Fare Accuracy: Find the Causes of Overcharged Trips and Prioritize Fixes

You are a product manager at Bolt, a ride-hailing company. In ride-hailing, the price a rider pays is central to the experience. The final fare is calculated when the trip ends, from tracking data the app collects during the ride, so a fair price depends on that data being accurate. Riders tend to tell the company quickly when they believe they paid too much, and that feedback can be used to improve the product. Identify the situations (use cases) in which rides end up overcharged, and propose the top opportunities for reducing the number of overcharged rides. ### Clarifying Questions - How is an overcharged ride defined today: any ride a rider complains about, or one whose charged fare is above a fare recomputed from corrected trip data? - Is the fare purely metered from the tracked distance and time, or do riders also see an upfront estimate that the final fare can exceed? - What happens today after a rider reports overpaying: a manual review by support, an automatic refund, or something else? - Should the answer also consider drivers, whose earnings change when a fare is corrected? ### Part 1 — Map the overcharge cases Describe the situations in which a rider ends up paying more than a fair fare, grouped by cause, and explain how you would use rider reports together with trip data to judge which situations matter most. ```hint Follow the fare from start to finish List every input the final fare depends on, from the location signal during the ride to the rules applied when it ends, and ask how each one can go wrong, through people as well as technology. ``` #### What This Part Should Cover - Causes grouped across location data, trip start and end, route choice, pricing rules and rider expectations. - How reports and trip telemetry are combined to size each case, given that many overcharged riders never report. - The difference between a true overcharge and a correctly priced fare that felt unfair. ### Part 2 — Choose the top opportunities Pick the opportunities you would pursue first to reduce the number of overcharged rides, explain how you prioritized them, and say how you would measure whether they worked. ```hint Where does each fix act? Some fixes stop a wrong fare from being computed, some catch it before the rider pays, and some repair it afterward. Compare what each costs riders, drivers and the company. ``` #### What This Part Should Cover - Opportunities tied to the largest cases from Part 1, prioritized by frequency, size of overcharge and effort. - How rider reports become an input that improves detection, not only a support queue. - Success metrics with guardrails such as driver earnings, wrongly reduced fares and abusive claims. ### What a Strong Answer Covers - A precise, measurable definition of an overcharged ride. - Causes rooted in how the fare is computed from tracking data, not only in driver behavior. - Attention to both riders' trust in prices and drivers' earnings. - Awareness that rider reports are a biased sample of the real problem. ### Follow-up Questions - Overcharge reports jump in one city right after a new app release. How do you investigate? - Some riders discover that reporting an overcharge gets them money back and start filing false reports. How do you respond without penalizing honest riders? - Would guaranteeing a fixed upfront price on every ride solve the problem? What new problems would it create?