Nov 23, 2021 · Careem is currently working with the AWS team to build and train a deep learning model to more accurately detect fraud on their user identity graph. Testing results for the initial phase are looking promising so far, with a precision of around 85% and a recall of over 50%.

Using around 10 times more historical data, Careem will be able to apply advanced deep learning instead of a simple rules-based approach, and train the system so that it can learn to identify what a fraudulent user looks like on the graph database.

To tackle this issue, Careem decided to implement a graph database, using AWS Neptune, to represent the identity of all their users and their data points. The data is populated with historical data and is updated in real-time as events occur on the app.

Depending on the city in which you use Careem, your ride fare may be calculated based on either Upfront pricing or Ride-end pricing. UPFRONT PRICING; The idea is that you always pay the amount that you see before your ride. This price is based on the following: Promised fare: This is the fixed rate we commit to at the start of the trip. It ...

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Jan 14, 2020 · Uber acquired ride hailing service provider Careem in January 2020. It has decided to operate the brand independently and not merge it under a universal brand name. Several factors may have prompted the company to pursue this strategy, but there may be two main reasons: Careem’s strong brand loyalty and expert management team.

Sep 29, 2024 · Careem’s tailored approach meets diverse needs efficiently. By embracing these technologies and investing in custom mobile software, Careem has become a super app.

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Jan 5, 2025 · Careem utilizes advanced data analytics to track user behavior and preferences. These insights inform personalized marketing efforts, enabling them to send targeted offers and notifications. Technology also supports dynamic pricing strategies, optimizing revenue while providing value to customers.

Careem is currently working with the AWS team to build and train a deep learning model to more accurately detect fraud on their user identity graph. Testing results for the initial phase are looking promising so far, with a precision of around 85% and a recall of over 50%.