By Russ Banham, contributor
Henna Karna and a team of data innovators are leading the way for giant insurer AXA XL to make data-driven decisions across the company’s global footprint. It’s a momentous undertaking, one predicated on the collection and analysis of intricate data within a sophisticated digital ecosystem.
Under Karna’s leadership as chief data officer, AXA XL’s Enterprise Data Solutions (EDS) organization has developed several data and analytics programs designed to improve every aspect of the company’s operations, including insurance underwriting, claims management, customer interactions, and workforce talent.
“We’re making our firm a much smarter, more connected business,” said Karna, who started her career as an actuary before becoming a predictive modeler, and later a corporate strategist. “Using data and analytics, we’re reallocating our talent to improve our value proposition for customers, tailoring our products and services into optimal insurance solutions.”
Karna’s background — she has a doctorate in applied mathematics from the University of Massachusetts and an MBA from the Massachusetts Institute of Technology —combines deep knowledge of business, strategy, and operations with data and technology. Prior to her current role, she was the global head of data and technology for the actuarial organization at another large insurer, AIG.
Today, her mission at AXA XL is to improve employee access to modeled data, helping teams execute tasks more efficiently and produce greater organization-wide impact. “We’ve digitized most structured data and increasingly unstructured data, giving us the ability to capture, search, exchange, and integrate wide-ranging data elements,” Karna said. “We’re now at the next stage — digitalization — turning this data into information, insights, and intelligence to optimize business decisions.”
Propelling People and Portfolios
Among the EDS organization’s data initiatives is a project that addresses enterprise-wide talent. One goal of the project is to provide project leaders with the right information to quickly put together teams with specific skill sets. Another is to match employees with roles that will motivate them to perform at high levels. “Empowering our people is what keeps me up at night,” said Karna.
To refine the initiative before rolling it out, Karna launched the project on a test basis within EDS. The decision met a key aim of Karna’s — to reduce her reliance on outside contractors like data architects, data scientists, and data engineers. “By measuring the skill sets of salaried staff, talent gaps could be filled through training and upskilling, obviating the need to retain outside contractors and recruit additional staff,” she explained.
The project looked something like this: For one EDS staff self-assessment, each person rated their skill levels across 87 areas from one to five — five being the equivalent of a subject matter expert (SME). “We asked each person what they were good at and what they wanted to be better at,” said Karna. The skills in question ranged from traditional technological expertise to business and analytical acumen, as well as cognitive and emotional competencies.
After the team analyzed the data, metrics pinpointed areas for talent improvement. Karna’s team then developed several training and upskilling programs, in partnership with AXA XL’s HH organization. While the program continues today, already more than one-quarter of the staff has received training in machine learning (ML) and artificial intelligence (AI), to name a few. The classes focus on the application of these technology skills to optimize business processes and, ultimately, improve financial performance.
Another EDS project involves assisting AXA XL divisional underwriters in their risk assessment by providing access to real-time data. “Every insurer seeks to have a balanced portfolio of different risk accumulations, since too much aggregation of similar financial exposures can be perilous,” Karna said.
As a result, the team developed an AI-driven digital platform with a data engine that rapidly searches through requests for insurance (called “submissions”) from brokers. The algorithms then discern granular details about each risk in minutes, identifying potential areas of concern that were difficult to grasp in the past.
Simple details like the address of a company’s facility in a foreign country are correlated with external data on regulatory compliance, weather information, legal trends, and prior insurance performance to paint a more accurate picture of potential risk. “We then take this information and put it through a risk portfolio simulation model we’ve developed to further narrow the determination on how a particular risk will add or detract from a balanced portfolio,” Karna explained.
Armed with this knowledge, underwriters may decide to insure specific categories of risk they had avoided in the past, or take on more risk in an area more traditionally serviced. At the same time, the information assists in the creation of insurance products customized to the risk-transfer needs of each business.
Always On
The pressure to modernize insurance products has increased in recent years, driven in part by innovative InsurTech startups. Among AXA XL’s competitors is Trov, the creator of micro-duration insurance policies that activate and deactivate based on the policyholder’s wishes. An example is more cost-effective automobile insurance for ride-hailing companies like Uber that commence and conclude each time a passenger is picked up and dropped off.
“Today’s 24/7, always-on, tech-savvy world is making all businesses rethink how they provide products and services of authentic value to customers,” said Karna. “In the insurance industry, the winners will be the ones that genuinely care for their customers and are trusted partners. That can only happen when we customize our insurance solutions to buyer needs, developing new on-demand and usage-based solutions and architecting more innovative ways to be of value.”
In these efforts, data and analytics are the cornerstone of success. The better companies are in extracting information from disparate data, the more prepared they will be in making more intelligent customer-focused decisions.