The introduction of black boxes has completely changed the world of insurance by introducing new services such as discounts on insurance premiums based on driving style, real-time assistance in case of a crash, positioning of cars in case of theft, danger detection on specific streets, and control over speed limit. Data from black boxes is collected in real time in a streaming context, processed, enriched, and either used to provide assistance or stored in order to be usable for analytic analysis. The enrichment process consists of adding information about customers or accidents. Information about accidents is processed in real time to detect false–positive events, using a high-accuracy machine learning algorithm, preventing fraud against the insurance company. The streaming application also acquires data related to trips and car theft.
With more than 4.5 million black boxes, Italian car insurance has the most telematics clients in the world. Riccardo Corbella and Beniamino Del Pizzo explore the data management challenges that occur in a streaming context when the amount of data to process is gigantic and share a data management model capable of providing the scalability and performance needed to support massive growth.
Riccardo Gianpaolo Corbella is a Milan-based consulting big data engineer at Data Reply IT, where he develops effective big data solutions based on open source technologies. Riccardo is interested in data mining and distributed systems. He holds a BSc and an MSc in computer science from the Università degli Studi di Milano.
Beniamino Del Pizzo is a big data engineer at Data Reply IT, where he works on data ingest with a focus on Apache Kafka and Spark applications. Beniamino is passionate about big data, streaming applications, distributed computation, and data analysis. He holds a master’s degree in computer engineering; his thesis outlined an evolutionary approach to using Apache Spark with TSK-fuzzy systems for big data.
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