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Transforming insurance pricing while improving driver safety
技术
- 分析与建模 - 实时分析
- 功能应用 - 远程监控系统
- 传感器 - 相机/视频系统
适用行业
- 金融与保险
适用功能
- 物流运输
用例
- 驾驶员表现监测
- 基于使用的保险
挑战
自汽车推出以来，物联网 (IoT) 正在以前所未有的规模彻底改变汽车保险行业。几十年来，保费一直使用基于代理的风险评估模型和历史数据来计算。如今，在远程信息处理技术和智能跟踪设备的进步的推动下，越来越多的创新公司，例如总部位于魁北克的 Industrielle Alliance，正在转向基于使用的保险 (UBI) 模式。
客户
基线远程信息处理
关于客户
基于使用的保险解决方案提供商
解决方案
Industrielle Alliance 与领先的保险远程信息处理解决方案开发商 Baseline Telematics 合作，帮助改变他们定价和销售汽车保险的方式，并设计创新计划以帮助提高驾驶员安全。车内的联网设备收集有关驾驶员行为和驾驶模式的信息，这些信息可以通过私人门户或每周电子邮件更新实时查看。
收集的数据
Driver Behaviour, Driver Performance Scores, Driving Patterns, Insurance Claims, Vehicle Status
运营影响
数量效益
相关案例.
Case Study
Real-time In-vehicle Monitoring
The telematic solution provides this vital premium-adjusting information. The solution also helps detect and deter vehicle or trailer theft – as soon as a theft occurs, monitoring personnel can alert the appropriate authorities, providing an exact location.“With more and more insurance companies and major fleet operators interested in monitoring driver behaviour on the grounds of road safety, efficient logistics and costs, the market for this type of device and associated e-business services is growing rapidly within Italy and the rest of Europe,” says Franco.“The insurance companies are especially interested in the pay-per-use and pay-as-you-drive applications while other organisations employ the technology for road user charging.”“One million vehicles in Italy currently carry such devices and forecasts indicate that the European market will increase tenfold by 2014.However, for our technology to work effectively, we needed a highly reliable wireless data network to carry the information between the vehicles and monitoring stations.”
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Safety First with Folksam
The competitiveness of the car insurance market is driving UBI growth as a means for insurance companies to differentiate their customer propositions as well as improving operational efficiency. An insurance model - usage-based insurance ("UBI") - offers possibilities for insurers to do more efficient market segmentation and accurate risk assessment and pricing. Insurers require an IoT solution for the purpose of data collection and performance analysis
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SPR Packaging, an American supplier of packaging solutions, was in search of an automated pallet labeling solution that could meet their immediate and future needs. They aimed to equip their lines with automatic printer applicators, but also required a solution that could interface with their accounting software. The challenge was to find a system that could read a 2D code on pallets at the stretch wrapper, track the pallet, and flag any pallets with unread barcodes for inspection. The pallets could be single or double stacked, and the system needed to be able to differentiate between the two. SPR Packaging sought a system integrator with extensive experience in advanced printing and tracking solutions to provide a complete traceability system.
Case Study
MasterCard Improves Customer Experience Through Self-Service Data Prep
Derek Madison, Leader of Business Financial Support at MasterCard, oversees the validation of transactions and cash between two systems, whether they’re MasterCard owned or not. He was charged with identifying new ways to increase efficiency and improve MasterCard processes. At the outset, the 13-person team had to manually reconcile system interfaces using reports that resided on the company’s mainframe. Their first order of business each day was to print 20-30 individual, multi-page reports. Using a ruler to keep their place within each report, they would then hand-key the relevant data, line by line, into Excel for validation. “We’re talking about a task that took 40-80 hours each week,” recalls Madison, “As a growing company with rapidly expanding product offerings, we had to find a better way to prepare this data for analysis.”
Case Study
Detecting and Reducing Cost of Fraud Rings
A leading insurer wanted to reduce its multi-billion dollar expenditures on auto insurance claims, which is its largest annual expense. Currently, the company avoids paying out only about 0.33% of the predicted 10% of fraudulent claims. The insurer uses both manual (60-65%) and automated systems (30-35%) to flag questionable claims. These claims are passed on to the investigative case managers who read adjuster and other member notes, explore the fraud watch list, and search the web. Their existing approach cannot easily discover fraud rings or, more importantly, collusion among other fraud rings. Moreover, the investigative team typically decides in 30 days or less on whether to pay or deny a claim. This is due to heavy caseloads and expectations for a quick turnaround in claim processing in order to keep customers satisfied.
