Kiva 使用 DataRobot 提高小额贷款融资利率
公司规模
1,000+
地区
- America
国家
- United States
产品
- DataRobot AI for Good program
技术栈
- Machine Learning
实施规模
- Enterprise-wide Deployment
影响指标
- Brand Awareness
- Customer Satisfaction
技术
- 分析与建模 - 机器学习
适用行业
- 金融与保险
适用功能
- 商业运营
服务
- 数据科学服务
关于客户
Kiva 是一家国际非营利组织，成立于 2005 年，其使命是“扩大金融服务渠道，帮助服务不足的社区繁荣发展”。Kiva 通过其国际网络提供众筹贷款来实现其使命。这些贷款用于各种用途，例如帮助妇女创业和发展业务、帮助农民购买设备以及为学生提供贷款以继续深造。该组织总部位于加利福尼亚州旧金山，从事非营利和金融服务行业。
挑战
Kiva 是一家非营利性金融服务机构，利用众筹为传统渠道服务不足的人们提供贷款。世界银行估计，大约有 17 亿人没有银行账户，这意味着他们无法获得零售银行提供的金融服务。这使得许多人无法获得世界上许多人认为理所当然的金融工具，例如信用卡和贷款。替代银行方法往往收费高昂，可能会让需要的人无法负担。这种资金匮乏阻碍了最需要的地方的经济增长、机会和平等。Kiva 使命的关键是确保申请贷款的人能够成功获得资金。
解决方案
为了确保贷款申请人成功获得资金，Kiva 与 DataRobot AI for Good 计划合作开发了一个系统来推广那些未被查看且面临无法获得资金风险的贷款。Kiva 团队使用每笔贷款的数据（包括每日受欢迎程度），利用 DataRobot 构建了 20 多个模型来预测每天将为哪些贷款提供资金。该解决方案旨在直接与 Kiva 网站集成，每天重新排序贷款，以便让任何面临风险的贷款都能被看到。该解决方案促进了不同地理位置的贷款推广，包括有时被资助者忽视的世界部分地区。
运营影响
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.”
