If 2020 will be remembered in history books as the year of a global pandemic, it will also be remembered as a year for transformative working practices for the majority of office-bound workers worldwide. Although many industries already enabled home-working on a regular basis, it’s fair to say that the insurance industry wasn’t one of them.
As thousands of insurance workers adjusted to home-working overnight, the acceleration of digitisation projects happened in parallel. A lot has been written about the digitisation of insurance processes and how insurers can drive underwriting profitability and efficiency through smart underwriting software to maximise data insights.
Artificial Intelligence (AI), Robotic Process Automation (RPA), Blockchain, Mobile Services, Chatbots, Machine Learning (ML), and Predictive and Data Analytics have been some of the leading players in changing processes and the way risk is assessed. Here, we look at just some of the critical technologies making an impact in the commercial insurance risk management landscape.
AI is embedded in so many applications we use daily - Google maps, Siri, Alexa, and many others all rely on the technology. We trust and rely on Google maps' directions without asking too many questions, but can we apply the same approach in the Commercial Insurance space?
The challenge in deploying AI in the commercial insurance space often comes in the teaching and learning and providing advice. Lloyd’s research showed that historical information used to teach and inform many AI engines could be flawed based on biases that may have historically been seen as acceptable. Plus, how do you prevent historical mistakes from being considered by AI engines without guided learning?
How does an AI engine learn from an underwriter’s interaction? What happens if a premium or a deductible amount is proposed to an underwriter and they chose to overrule it? Should the engine take this response and learn from it, or should it ignore the fact and present the same answer next time, potentially upsetting the underwriter? Or should it learn and use the overridden information next time, creating a happy underwriter but introducing more risk? What if AI learns the underwriter’s bad habits or adopts the wrong risk behaviour?
Despite these shortfalls, the benefits of AI-driven insights far outweigh the risks of not incorporating the technology - AI-driven data yields granular insights. With a plethora of data entering the industry at unprecedented levels, from claims history, weather history, location tracking, etc., aligned to the business being written on a daily basis, AI will only be effective if accompanied by robust human control.
It’s no secret that insurers need systems that allow them to innovate quickly and seize on market opportunities as they arise. Adopting a decentralised Microservices architecture enables insurers to introduce features and functions faster and, on a use-case driven basis through small, system-agnostic, and independent components that have clear and defined functions without having to replace their core systems.
The decentralised architecture enables insurers to decompose complex software applications into smaller services that can be evolved and updated independently. These individual components can be purchased, tested, monitored, and maintained individually to create customisable solutions on the go. They can be scaled individually as and when more power is needed allowing insurers the agility to rapidly release, test and update individual products and services with minimal impact.
The explosion of data (in personal and commercial lines), the growth in analytical techniques, and the declining cost of computing power and data storage are prompting companies to invest in data analytics as a means to innovation. Every large carrier is adopting analytics more broadly within the insurance value chain – that’s no surprise. From predictive analytic tools to machine learning, IoT, and biometrics, any insurer worth their salt uses a data analytics tool.
But, implementing the latest insurance business analytics technology and ticking off the ‘digitising processes’ box won’t necessarily pay off. It’s what you do with the data, combining it with additional third-party information, adding intelligence to reach out to other disparate sources that may not initially seem related (uncovering the Butterfly Effect), that’s when the power really does start to show itself.
Implementing analytic tool within legacy core IT systems can cause its own set of challenges in regards to quality, and data granularity. To implement analytics and automation, companies need to bring in different competencies within. The key is in understanding how and where to use data analytics smartly, such as:
It’s debatable if the Cloud can still be considered an ‘innovative technology’ as it’s been around for years; however, Cloud adoption is still relatively new within the insurance market. For an industry so reliant on paper for decades, embracing the Cloud is understandably a big hurdle to overcome. A lot of insurers talk Cloud, but equally are more comfortable with systems on premise. Dealing in risk mitigation has them hard-wired to the risks around data protection, especially customer data. With cyber-attacks on the increase, insurers are quite rightly hyper-sensitive to the risks attached to data being hacked, stolen, or replicated. However, we are starting to see insurers move towards the Cloud, which typically has stronger security than on-premise.
Underwriting discipline have been the buzzwords for the last 12-18 months. Carriers are increasingly using data, predictive analytics, and aligning pricing actions with risk quality to drive underwriting expertise and make better pricing and underwriting decisions. They are also implementing standardised processes to improve both consistency and efficiency and are using robotic process automation (RPA) to do so.
It’s still waiting for its Eureka moment in insurance, but don’t rule out Blockchain. Often spoken about as the perennial solution looking for a problem, Blockchain is gaining momentum and investors. With hundreds of start-ups working on blockchain solutions, it would be foolish to discount this technology stack for areas including claims management, fraud detection, risk prevention, reinsurance.
In our industry, the secure and non-refutable distribution of information across multiple parties makes perfect sense. Automating claims management and managing fraud detection are perfect fits for the technology. One of the great aspects of blockchain is its ability to provide a “single source of truth” and enable the insurance community to access information more efficiently across the entire insurance value chain.
A lot of the existing technology that carriers deploy today is already doing an excellent job of enabling them to gain a competitive edge, taking more insight from data providers, creative rating approaches, and optimising the workload that is presented to the underwriter. The next few years will be about insurers figuring out how to extract the most from innovative technology, and certain technologies considered to be radical or new to the industry will become the accepted norm in the insurance value chain.
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