使用 DataRobot AI 应用程序优化贷款预测
地区
- America
国家
- United States
产品
- DataRobot Enterprise AI platform
- DataRobot Optimizer App
技术栈
- Machine Learning
- Data Science
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Revenue Growth
技术
- 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
- 销售与市场营销
用例
- 补货预测
- 欺诈识别
服务
- 数据科学服务
- 软件设计与工程服务
关于客户
客户是一家位于美国的创新型金融科技公司。他们提供传统消费贷款产品的替代品。该公司主要向个人借款人提供 5,000 美元以下的小额消费贷款，以及为商户提供融资选择。这些贷款的催收是公司每月收入的很大一部分，催收团队因此成为公司业务的重中之重。他们有一个由数据科学家和分析师组成的小团队，他们对 DataRobot 平台印象深刻，并发现使用该平台可以大大提高生产力和数据科学。
挑战
这家金融科技公司通过比传统贷款计划更具适应性的替代方案，在销售点为商家和消费者提供消费融资。他们建立了模型来支持公司各个部门的项目，包括承保、会计和催收。然而，他们在催收部门面临着挑战。由于任何时候都有数以万计的拖欠贷款，催收团队需要拨打大量电话。他们拨打的成功电话越多（以行业指标“正确方联系 (RPC)”衡量），他们就越有可能成功收回这些拖欠贷款，从而为公司带来收入。然而，由于要拨打的目标电话数量如此之多，而且在联系到正确人员或团体方面，接通率通常很低，任何类型的优化或效率都可以产生很大的影响。
解决方案
该团队看到了利用 DataRobot 模型的机会，并与 DataRobot 的新 AI 应用程序团队合作，构建了 DataRobot Optimizer 应用程序的测试版。该应用程序由 DataRobot 的模型提供支持，能够预测一天中拨打电话和连接大量拖欠账户的最佳时间，并在 20 分钟内做出这些预测。然后，这些预测被推送到 Collections 团队使用的自动拨号系统，通过优化列表告诉他们在什么时候拨打谁的电话，从而提高工作效率。DataRobot 团队实施了一种新的专门优化算法，吞吐量提高了 220 倍，满足了在 15-18 分钟内完成工作的挑战。这种新算法已集成到 Optimizer 应用程序中，适用于任何具有潜在客户评分、客户流失或追加销售用例的客户。
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.”
