公司规模
Large Corporate
地区
- America
- Europe
国家
- United States
- United Kingdom
产品
- DataRobot's Managed AI Cloud
- Avant's in-house production system
技术栈
- Amazon Web Services (AWS)
- DataRobot APIs
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Digital Expertise
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 质量预测分析
- 欺诈识别
服务
- 数据科学服务
关于客户
Avant 是一个在线借贷平台,为美国和英国的中等收入消费者提供信贷替代方案。该公司提供无担保个人贷款,金额从 1,000 美元到 35,000 美元不等,最快可在下一个工作日获得资金。Avant 已为全球 60 多万客户提供服务。该公司还通过其“Powered By Avant”产品向银行和非银行合作伙伴提供技术解决方案,为客户提供创新的数字借贷体验。Avant 成立于 2012 年底,已通过该平台筹集了超过 6 亿美元的股本资本并发放了超过 40 亿美元的贷款。
挑战
Avant 是一家在线贷款平台,一直在使用数据和机器学习来做出明智的贷款决策。然而,随着该公司想要扩大业务规模,它面临着保持分析质量和复杂性的挑战。该公司需要一种解决方案,使其分析师和业务用户能够访问业务团队可以利用的数据科学工具。Avant 正在寻找一种易于使用、统计合理、由可靠公司支持且易于与生产系统集成的解决方案。
解决方案
Avant 选择使用 DataRobot 的托管 AI 云产品(由 Amazon Web Services (AWS) 提供支持),以便其业务分析师执行数据科学工作。该平台使该公司能够更快地做出大量预测,从收到付款的可能性到营销响应,再到潜在欺诈。DataRobot 平台使 Avant 能够快速构建模型、执行分析和评估新数据源,从而节省了数据科学家的时间。该平台还通过直接访问 DataRobot API 轻松与 Avant 的内部生产系统集成。
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
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.”
Case Study
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
Case Study
Smooth Transition to Energy Savings
The building was equipped with four end-of-life Trane water cooled chillers, located in the basement. Johnson Controls installed four York water cooled centrifugal chillers with unit mounted variable speed drives and a total installed cooling capacity of 6,8 MW. Each chiller has a capacity of 1,6 MW (variable to 1.9MW depending upon condenser water temperatures). Johnson Controls needed to design the equipment in such way that it would fit the dimensional constraints of the existing plant area and plant access route but also the specific performance requirements of the client. Morgan Stanley required the chiller plant to match the building load profile, turn down to match the low load requirement when needed and provide an improvement in the Energy Efficiency Ratio across the entire operating range. Other requirements were a reduction in the chiller noise level to improve the working environment in the plant room and a wide operating envelope coupled with intelligent controls to allow possible variation in both flow rate and temperature. The latter was needed to leverage increased capacity from a reduced number of machines during the different installation phases and allow future enhancement to a variable primary flow system.
Case Study
Automated Pallet Labeling Solution for SPR Packaging
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
Transforming insurance pricing while improving driver safety
The Internet of Things (IoT) is revolutionizing the car insurance industry on a scale not seen since the introduction of the car itself. For decades, premiums have been calculated using proxy-based risk assessment models and historical data. Today, a growing number of innovative companies such as Quebec-based Industrielle Alliance are moving to usage-based insurance (UBI) models, driven by the advancement of telematics technologies and smart tracking devices.
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.”