An embedded analytics solution that is changing the game for insurance firms.
Customer Company Size
Large Corporate
Product
- UnderwriteMe
- Yellowfin
- H2O.ai
Tech Stack
- ETL layers
- Google Analytics
- D3 JavaScript
- R
- Monet
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
Technology Category
- Application Infrastructure & Middleware - Data Visualization
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Quality Assurance
Services
- Data Science Services
- System Integration
About The Customer
UnderwriteMe is a global fintech organisation that uses digital technology to transform the way life insurance is bought and sold. By offering insurers a new way to consume and serve up rich data services, UnderwriteMe delivers significant cost and time efficiencies to existing sales processes. At the heart of UnderwriteMe’s integrated suite of software products is a revolutionary quote and underwriting rules engine, which gives insurers hands-on control of their Protection products. For intermediaries, UnderwriteMe delivers a seamless comparison service that provides clients with access to a single process to quote, underwrite, and buy multiple insurance products from a wide range of providers.
The Challenge
Traditionally, insurers have been starved when it comes to underwriting data. They rely heavily on risk and pricing actuaries to underwrite their Protection products. And in the case of reinsurance firms, they need to provide additional data insights to insurers as part of their reinsurance process. Previously, underwriters might gain access to updated underwriting data on a monthly basis, and this data is crucial to understand how their rules have performed. So being able to analyse and make changes to their pricing rules, and deploy those changes into production, used to take months. Lead Consultant, Rikus van der Merwe of UnderwriteMe, explains, “We turn that feedback loop around in minutes, so underwriters can make changes to the rules engine and deploy those changes once they are tested and are completely happy with them. They can get Management Information (MI) feedback in fifteen minutes, which is previously unheard of.” For intermediaries, UnderwriteMe, wanted to break down the traditional, fractured sales process by delivering real-time underwriting. This means that when customers are comparing prices and products, they know that the premiums they are seeing are accurate, personalised, and fully underwritten by insurers.
The Solution
UnderwriteMe understood the challenges that insurers and intermediaries faced. They worked with key decision makers and potential customers to design an integrated suite of software that allows underwriters to rapidly create brand new rules, or amend rules to suit their own philosophy, interact fully with live cases, and create customised reports and dashboards with drill down capability for more detailed analyses. As part of this suite, Yellowfin was selected as the front-end analytics and data visualisation tool. Yellowfin is a white-labelled, fully embedded solution which means that customers can apply its ‘plug-and-play’ capabilities and completely brand the reporting interface in line with their corporate palette. Furthermore, customers can create and schedule their own reports and maintain full auditability. UnderwriteMe is a feature rich platform that captures purchase data, on web or comparison sites, and augments it with statistically available data such as Mosaic and Google Analytics. All of this rich data flows into Yellowfin, primarily via ETL layers, and it gets populated into non-aggregated data marts. With Yellowfin seamlessly integrated into UnderwriteMe’s rules engine, it becomes incredibly easy for their customers to create their own reports, drill down into individual records, where required, and easily compare different business routes.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
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.”
