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Engineering · Data Science · Product
Road localisation in GrabMaps
With GrabMaps powering the Grab superapp we have the opportunity to improve our services and enhance our map with hyperlocal data. No matter the use case, road localisation plays an important role in Grab’s map-making process. However, road localisation entails handling a substantial volume of data, making it a costly and time-consuming endeavour. In this article, we explore the strategies we have implemented to drive down costs and reduce processing times associated with road localisation. -
Engineering · Security
Graph modelling guidelines
Graphs are powerful data representations that detect relationships and data linkages between devices. This is very helpful in revealing fraudulent or malicious users. Graph modelling is the key to leveraging graph capabilities. Read to find out how the GrabDefence team performs graph modelling to create graphs that can help discover potentially malicious data linkages. -
Engineering · Data Science
Scaling marketing for merchants with targeted and intelligent promos
Apart from ensuring advertisements reach the right audience, it is also important to make promos by merchants more targeted and intelligent to help scale their marketing. With Grab’s innovative AI tool, merchants can boost sales while cutting costs. Dive into this game-changing tool that’s reshaping the future of marketing and find out how the Data Science team at Grab used automation and made promo assignments a more seamless and intelligent process. -
Engineering · Data Science
Stepping up marketing for advertisers: Scalable lookalike audience
A key challenge in advertising is reaching the right audience who are most likely to use your product. Read this article to find out how the Data Science team improved advertising effectiveness by using lookalike audiences to identify individuals who share similar characteristics with an existing consumer base. -
Engineering · Data Science · Product
Building hyperlocal GrabMaps
Being hyperlocal is a key advantage for GrabMaps. In this article we will explain what being hyperlocal means and how it helps GrabMaps bring value to our driver-partners and passengers through the Grab platform. -
Engineering
Streamlining Grab's Segmentation Platform with faster creation and lower latency
Since 2019, Grab's Segmentation Platform has served as a comprehensive solution for user segmentation and audience creation across all business verticals. This article offers an insider look at the platform's design and the team's efforts to optimise segment storage, ultimately reducing read latency and unlocking new segmentation possibilities. -
Data Science · Security
Unsupervised graph anomaly detection - Catching new fraudulent behaviours
As fraudsters continue to evolve, it becomes more challenging to automatically detect new fraudulent behaviours. At Grab, we are committed to continuously improving our security measures and ensuring our users are protected from fraudsters. Find out how Grab’s Data Science team designed a machine learning model that has the ability to discover new fraud patterns without the need for label supervision. -
Engineering · Security
Zero traffic cost for Kafka consumers
Grab's data streaming infrastructure runs in the cloud across multiple Availability Zones for high availability and resilience, but this also incurs staggering network traffic cost. In this article, we describe how enabling our Kafka consumers to fetch from the closest replica helped significantly improve the cost efficiency of our design.