Just like financial institutions, insurers are no strangers to leveraging advanced technologies in various aspects of the business. Some of the practical applications of machine learning in the insurance industry include managing broker business, optimizing direct marketing, understanding quote conversion, computing optimal pricing, detecting fraud, claims triage, predicting litigation, targeting inspections and audits, forecasting claims, retaining customers, and, finally, recalibrating prices. Extensive research by Satadru Sengupta, General Manager & Data Scientist, Insurance at DataRobot, explores particular ways machine learning can impact operational efficiency. Let's take a closer look at some interesting examples and partnerships. Insurance executives need accurate loss predictions so that they can set reserves appropriately.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.

QBE North America recently announced its investment and multi-year commercial use agreement with HyperScience (New York), a machine learning, enterprise-grade artificial intelligence (AI) solution, which the insurer intends to use drive operational efficiency and unlock new data and insights for underwriting, pricing and claims. The acquisition was the third in a series flowing from a $50 million commitment QBE announced in 2017 to invest in early-stage businesses working on technically-challenging and industry-changing ideas. The company earlier announced its investments in RiskGenius (Overland Park, Kan.), a machine learning platform for analyzing policy wordings, in Oct. 2017, and Cytora, a London-based company that uses open source data to help commercial insurers lower loss ratios, grow premiums and improve expense ratios, in Dec. 2017. David McMillan, QBE's Group COO has characterized the acquisitions as contributing to the company's objective of delivering "Brilliant Basics" in underwriting, pricing and claims. Insurance Innovation Reporter talked with Ted Stuckey, SVP, Managing Director of QBE Ventures, and Head of QBE's Global Innovation Lab, to talk about the acquisitions and how they fit into QBE's broader strategy.

MS Amlin has enlisted the help of artificial intelligence (AI) firm Cytora to enhance its commercial underwriting performance and drive premium growth. Announcing the partnership, the global (re)insurer said it will use Cytora's technology to improve processes and power open market underwriting. Cytora, through its Risk Engine API integration with Acturis, will also facilitate better risk selection and pricing in MS Amlin's small- and medium-sized enterprises automated trading book by providing risk scores. "AI technology is transforming the way insurers do business," noted Dr Paul Taffinder, director of strategy & innovation at MS Amlin. "This new and exciting partnership with Cytora is a testament to MS Amlin's commitment to digital innovation and the use of smart technology, further cementing our position as an insurer of the future." Meanwhile Cytora chief executive Richard Hartley, who described MS Amlin as "an ambitious insurer and proven market leader," said its new partner shares the tech company's vision to make insurance more frictionless and data-driven.

Just like financial institutions, insurers are no strangers to leveraging advanced technologies in various aspects of the business. Some of the practical applications of machine learning in the insurance industry include managing broker business, optimizing direct marketing, understanding quote conversion, computing optimal pricing, detecting fraud, claims triage, predicting litigation, targeting inspections and audits, forecasting claims, retaining customers, and, finally, recalibrating prices. Extensive research by Satadru Sengupta, General Manager & Data Scientist, Insurance at DataRobot, explores particular ways machine learning can impact operational efficiency. Let's take a closer look at some interesting examples and partnerships. Insurance executives need accurate loss predictions so that they can set reserves appropriately.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.

Just like financial institutions, insurers are no strangers to leveraging advanced technologies in various aspects of the business. Some of the practical applications of machine learning in the insurance industry include managing broker business, optimizing direct marketing, understanding quote conversion, computing optimal pricing, detecting fraud, claims triage, predicting litigation, targeting inspections and audits, forecasting claims, retaining customers, and, finally, recalibrating prices. Extensive research by Satadru Sengupta, General Manager & Data Scientist, Insurance at DataRobot, explores particular ways machine learning can impact operational efficiency. Let's take a closer look at some interesting examples and partnerships. Insurance executives need accurate loss predictions so that they can set reserves appropriately.