Taking Australia’s largest health insurance mobile
With an evolving smartphone market, the client identified a need for a mobile strategy and delivery capability to target a younger demographic and address the gap in their user experience offering.
Mobile client applications for iOS and Android, providing members with access to member services and data sourced from a variety of internal platforms over a mix of technologies and protocols.
A web-standard mobile API that simplifies user interface (UI) and client development cycles, improves customer experience, products and accessibility while building market share.
BIG DATA'S BIG GUNS: PROGRESSIVE INSURANCE
Few insurers are as well-known for their big data efforts as Divakarla’s own Progressive Insurance, which has pioneered insurance telematics and web-based quoting and sales, all of which lean heavily on big data technology. To serve that strategy, the insurer has invested considerably to build a big data infrastructure and a culture.
BIG DATA'S BIG GUNS: MASSMUTUAL
Last year, looking to ramp up its big data and analytics capabilities quickly, MassMutual hired seven graduates from an ambitious new program the insurer created in collaboration with several colleges in the Springfield, Mass., area. MassMutual executives, led by SVP of analytics and research Gareth Ross, worked with faculty and administration to combine extended coursework for graduate students with real-world projects at the company, including machine learning, lead scoring, and natural language processing.
BIG DATA'S BIG GUNS: AMERICAN FAMILY INSURANCE
American Family Insurance has several initiatives around big data, including programs for more intensive testing and efforts to leverage unstructured data from claims notes.
BIG DATA'S BIG GUNS: JOHN HANCOCK
For a long time, life insurers danced around the issue of how wearable technology could impact the way they evaluate the riskiness of applicants. But the technology was of clear interest to an industry that has struggled to attract new buyers due to its typically onerous underwriting process, involving collection of blood and urine samples as well as a long wait time.
BIG DATA'S BIG GUNS: CNA
Among its many data-oriented initiatives, CNA is applying big data technology to workers compensation claims and adjusters’ notes.
“That is a classic, unstructured big data kind of problem,” says Nate Root, SVP of CNA’s shared service organization. “We have hundreds of thousands of workers compensation claims, and claims adjuster notes, and there is tremendous value in those notes.”
BIG DATA'S BIG GUNS: NATIONWIDE
Multiline carrier Nationwide is taking the lead not just on effective use of data and analytics in its customer segments, but exposing the next generation of data professionals to the exciting opportunities afforded by the insurance industry.
BIG DATA'S BIG GUNS: QBE NORTH AMERICA
QBE North America, which is headquartered in New York and provides general insurance and reinsurance through five specialized business units, named Gina Papush chief data and analytics officer in September. Papush brought with her a wealth of data experience from across financial services — she previously ran data functions for Citigroup’s mortgage unit and also worked at GE Cards — and she says that insurance is well-positioned to take a leadership role.
BIG DATA'S BIG GUNS: SWISS RE
Big data is one of Swiss Re’s global strategic initiatives. The insurer is using more public data to improve underwriting results and decrease the number of questions the insurer has to ask consumers to underwrite them. Riccardo Baron, big data and smart analytics lead for Americas at Swiss Re says currently available data opportunities were inconceivable only few years back.
BIG DATA'S BIG GUNS: ALLSTATE
Like some of its peers, Allstate has seen itself as a big data company for a while, according to chief data officer Floyd Yager. But the impetus to do more with the data it had collected over the years has never been greater.
“Allstate has always had very good data, but it does exactly what it was designed to do: make us transactionally efficient on a product basis,” Yager says. “Now it’s about, ‘How do I take that operationally efficient data and turn it into a customer/household view and understand all the products attached to a person?’”