nGeniusOne：通过识别信用卡交易失败的根本原因来改善客户体验
公司规模
1,000+
地区
- Asia
产品
- nGeniusONE
- Adaptive Service Intelligence
- InfiniStream
技术栈
- Service Assurance platform
- Data center
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Revenue Growth
技术
- 分析与建模 - 实时分析
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
- 欺诈识别
服务
- 数据科学服务
关于客户
客户是一家大型金融服务集团，在亚太地区提供保险、银行和投资服务。该组织在全球拥有超过 5 亿销售代理和超过 25 万名全职员工。该公司业务的一个至关重要的组成部分是发行信用卡和提供销售点 (POS) 终端。
挑战
当商家开始报告卡在销售点 (POS) 终端上间歇性被拒收的问题时，金融服务组织面临着重大挑战。这导致了糟糕的最终用户体验、收入损失和客户满意度问题。IT 员工无法了解公司的网络，因此很难确定交易失败的根本原因。交易被拒可能是实际交易失败的迹象，也可能只是授权失败。如果没有更深入的洞察，解决问题将是一项艰巨的挑战。
解决方案
为了应对这一挑战，该组织采用 NETSCOUT 的先进技术来监控其信用卡交易，以找出某些交易被拒绝的原因。采用 Adaptive Service Intelligence™ (ASI) 技术的 nGeniusONE® 服务保证平台以及部署在公司数据中心的 InfiniStream® 设备，使 IT 能够快速识别信用卡交易失败的根源。利用 nGeniusONE 的深度情报和分析功能，IT 能够识别信用卡被拒绝问题的根本原因，并将其与银行间交易隔离开来。每次信用卡交易被拒绝时，IT 人员都会利用 nGeniusONE 创建警报，使服务组能够快速主动地识别与银行间交易相关的问题，并在最终用户受到影响之前将其上报给支付处理器。
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.”
