通过 AutoML 实现投资银行业务的创新
地区
- Asia
产品
- DataRobot
技术栈
- Automated Machine Learning
- Enterprise AI
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 大数据分析
- 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
TC Capital 是一家领先的泛亚精品投资公司，专门从事并购和协议资本投资。该公司由首席执行官 Tommy Tan 领导，他在亚洲投资银行业拥有 30 多年的经验。TC Capital 致力于利用尖端技术和数据来改进其估值方法。他们拥有一个庞大的数据集，其中包含来自 43,000 家公司的信息，每家公司都有 560 个变量。该公司还在开发一款名为 CeeSuite 的应用程序，帮助高管对上市公司和私营公司进行估值。
挑战
Tommy Tan 是 TC Capital 的首席执行官，该公司是一家领先的泛亚精品投资公司，专门从事并购和协商资本投资。他对投资银行使用的传统公司估值方法感到不满。这些方法包括比较过去的并购、查看类似公司的股票市场估值以及折现现金流模型，这些方法需要大量人工，并且存在很高的人为错误风险。它们还可能导致高度主观的估值。Tommy 和他的团队希望建立自己的估值方法，这种方法既能利用尖端技术，又能充分利用当今银行家可用的大量数据。
解决方案
Tommy 和他的团队转向 DataRobot 的自动化机器学习和企业 AI。他们可以访问一个大型数据集，其中包含来自 43,000 家公司的信息，每家公司有 560 个变量。TC Capital 花了九个月的时间才收集到这个数据集。借助 DataRobot，他们能够自动处理这个大型数据集并生成数十个高质量、可靠的模型，这些模型提供了高度准确的估值。在回测中，这些模型比市场高出近 3 倍。DataRobot 的预测非常一致和准确，以至于 Tommy 在估值委员会投票时很快就依赖这些估值。TC Capital 还推出了一款应用程序 CeeSuite，该应用程序利用机器学习和数据科学帮助高管对上市公司和私营公司进行估值。
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.”
