The Earnix Blog

The Earnix Blog is a community blog for financial industry professionals that share a passion for predictive analytics

11
April

Blog: Can Banks Disrupt the Digital Disruptors?

The year was 2008, and the global financial crisis was well underway. From that point on until today, banks have gone through tremendous turmoil. Competition has been heightened, mergers & acquisitions have significantly increased, interest rates have slumped and are still veryRead More »
10
April

Blog: Three Top Takeaways from the 2019 CAS RPM Seminar

Earnix recently attended the Casualty Actuarial Society Ratemaking, Product, and Modeling Seminar (CAS – RPM) in Boston, Massachusetts. The conference covered many innovative topics and trends in insurance – from telematics, to mobile and driverless vehicles, to usage-based insurance, to company culture,Read More »
20
November

Blog: Real-Time Price Tuning to Fit Dynamic Markets

Real-Time Price Tuning to Fit Dynamic Markets Modern financial services markets are highly dynamic. Prices change often – due to seasonality, changes in competitor pricing and other marketplace variables. However, developing the best pricing strategy for insurance and banking products can takeRead More »
19
November

Blog: Earnix Summit 2018: Key Findings from the Frontlines

Our annual Summit held two weeks ago in London was one of the most successful Earnix events ever! Over two days, 28 presentations were delivered that included financial services industry innovations, best practices, and customer successes. While most of the presentations focusedRead More »
22
October

Blog: Earnix Summit 2018 – Customer Centric Analytics and Innovation

It’s an exciting time of year here at Earnix, as we prepare for our seventh annual Earnix Summit. This year’s theme is all about customer centricity – and how analytics and emerging technologies are enabling innovation and transformation at some of theRead More »
01
October

Blog: DataRobot complements Earnix with Machine Learning Capabilities for Decision Support

The Challenge Many financial institutions today are looking to implement sophisticated machine learning models into their existing analytical systems – for use in analytical pricing and product personalization initiatives. They want to use machine learning libraries that they have often created inRead More »
08
August

Blog: Earnix Recognized by Gartner in 2018 Hype Cycle for Data Science and Machine Learning Capabilities

For organizations to become truly customer centric – analytics is a prerequisite. As consumers become continually short on time and expect more choice than ever, brands are turning to customer analytics software to enable optimal product offerings, price, and choice of delivery channel.Read More »
06
August

Blog: Earnix Version 8.9: The Three Most Exciting Improvements in 2018

We at Earnix have been busy improving the product for our user community, and the purpose of this blog is to share our most recent updates with you! While there are many new features and capabilities in the most recent release, EarnixRead More »
16
April

Blog: Five Insights Gleaned from the Casualty Actuarial Society Ratemaking, Product, and Modeling Seminar

Earnix recently attended the Casualty Actuarial Society Ratemaking, Product, and Modeling Seminar (CAS – RPM) event in Chicago, Illinois. While the conference covered many innovative topics on trends in telematics, usage-based insurance, and regulation – Earnix was there to present  specifically onRead More »
08
March

Blog: Machine Learning for Financial Services: Hype or Reality?

There’s an ongoing debate as to whether new trends in machine learning are mere hype or are actually providing tangible business value and helping shape financial services pricing and offering strategies. A survey of about 200 global insurance professionals conducted by EarnixRead More »
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This first-of-a-kind survey uncovers how insurance executives understand how the industry leverages Machine Learning today and how they expect Machine Learning to impact the industry in the near future.

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