Many P&C insurers strategise to upgrade their plans for customer-centricity, telematics and claim management effectiveness credit to the continuous progress in insurance analytics. However, studies indicate that the P&C industry still has a long way to go in terms of using advanced analytics to its maximum potential.
The rate of change for P&C insurers may outpace the incentive to innovate in insurance analytics, which could be good news for your business. This means it's not too late to turn insurance analytics into a competitive advantage for your company.
P&C insurance executives have spent the bulk of their careers keeping up with the new insurance analytics trends. The advancement of insurance analytics means more access to consumer data and innovations in cutting-edge technology which can unlock the data's true value. These practices became a necessity as technologies unlock even larger access to knowledge, introducing advanced analytics trends for the P&C insurance trade to require advantage of in 2021 and on the far side. Continuing the discussion further, here are the top insurance analytics trends to watch in 2021
With the widespread adoption of intelligent devices and greater access to their data, the P&C insurance industry continues to monitor the Internet of Things (IoT), making it easier to contextualise policies and monitor risk in real-time.
As most of the major insurers are now using telematics to improve their auto insurance plans. However, as smart home systems become more adaptable to consumers, insurers may have new opportunities. Around 35% of US households want their insurers to send them a proactive risk notice, which can be powered by IoT monitoring that customers opt into.
To gain access to these new streams of consumer data generated by a security camera, smoke alarm, door lock, or thermostat, P&C insurers can form partnerships. This data will help insurers to personalise their property insurance offerings based on safety and energy efficiency the same way they personalise auto insurance policies based on the customer’s driving behaviour. According to McKinsey, “insurers should build up scalable and flexible API-based IT architectures that support quick integration of offerings and create analytics capabilities to leverage these new data sources.”
Though conventional distribution channels such as direct-to-consumer websites/call centres, agent intermediaries, and brokers continue to dominate the P&C insurance industry, there are newer insurance distribution channels that may provide a better customer experience. To unlock the value of an ecosystem delivery model in 2021, a diverse set of insurance analytics technologies and data infrastructure will be needed.
Innovative partners in the mobility ecosystem, which brings together vehicle manufacturers, dealerships, and ride-sharing networks like Uber to provide insurance services to drivers, have already started to demand a slice of the consumer pie from more conventional auto distribution channels. Furthermore, this pattern isn't limited to auto insurance policies. Banks, mortgage brokers, and home security providers are among the holistic players that have started to provide new technologies that are helping ecosystems to turn personal property and homeowners insurance.
According to a McKinsey report, “Ecosystem orchestrators and participants, such as car-sharing service vendors, could also collect and integrate massive amounts of data from different services. Participants can use this data to become and stay relevant in the eyes of customers, offering them an array of services and products tailored to their needs.”
Combining the wealth of your customer data with new partnerships that span across emerging ecosystems can help to bring highlighted customer experiences and revenue streams.
While cyber insurance has quickly gathered momentum as a high-margin P&C product line, increased demand has further felt the need for advanced cyber analytics to manage risk with the rise of claims. As policy demands across the entire industry catapult, customised cyber insurance emerges as a strong revenue opportunity for insurers only if they can achieve the pricing model right.
Cyber risk management is highly difficult for P&C insurers. Unlike auto and homeowners insurance, this is a totally new area with no historical loss records on which to build product lines. Underwriters must manually match the moving target of cyber threats with future liabilities without historical data. This mighty catapult the Premiums, but the danger is high because cyber insurance pricing hasn't been put to the test by large-scale loss events.
However, with cyber analytics solutions built specifically for the insurance industry, P&C insurers can better comprehend a company’s risk profile to further avoid making costly underwriting mistakes.
The majority of the insurers believe advanced analytics either strongly or somewhat positively affects their business. Capitalizing on insurance analytics trends requires a strategy to implement new tools for a competitive advantage
P&C insurers may use "what if" modelling, a form of predictive analytics, to expand on the recent changes in underwriting processes brought about by big data analytics and automation.
When dealing with manual formulas distributed through large spreadsheets, even the most experienced workers may become irritated. Many of the time-consuming activities that slow risk management can be avoided by integrating "what if" modelling into the underwriting process. Instead of manually modelling scenarios, "what if" analytics provides underwriters with drop-down menus of data visualisations, enabling them to make better risk decisions in real-time.
Since most P&C insurers have been using this technology to create dynamic decision trees for years, this is an open-use case for advanced analytics.
P&C insurers can now combine geographic information system (GIS) and CRM data for real-time event tracking thanks to advances in IoT technology. Previously, insurers had little choice but to respond to uncontrollable incidents such as natural disasters. For P&C insurers, natural disaster coverage may be lucrative. Slow responses to incidents, on the other hand, can degrade the customer experience and lead to churn, which hurts your bottom line. With the right insurance analytics software, you can be more vigilant when it comes to loss incidents.
The cornerstone of proactive event tracking is real-time GIS info. By integrating data from IoT devices like weather stations, you can gain a better understanding of the "time" and "where" of events that could affect claims, such as hurricanes and tropical storms, as well as wildfires, droughts, and other natural disasters.
You can take this use case even further by integrating your CRM with the insurance analytics tools you're using to track GIS data. GIS data alone can tell you the "what" and "where" of significant incidents, but CRM data can also tell you "who." The combination helps you to reach out to customers ahead of time and provide guidance that can help them reduce their losses while reducing your risk.
P&C insurers can get the historical data they need to determine risk more reliably by using new cyber insurance analytics tools. As the market for cyber insurance increases and attacks increase in both volume and expense, this must become a core competency for your business.
The global average cost of a data breach is $3.86 million, according to IBM’s Cost of a Data Breach Report 2020, with the United States having the highest average at $8.64 million. Worse, 76 per cent of survey respondents said that the trend toward remote work increases the time taken to detect and contain data breaches, which escalates costs.
To take advantage of the opportunity to protect companies from these costly incidents, a thorough understanding of the connection between attacks and financial loss is needed. Your underwriters and actuaries can gain deep insights into a company's risk profile and coverage needs by implementing a cyber analytics solution.
P&C insurers can reduce adverse selection in the underwriting process by feeding more first-party data from social media and IoT tracking into analytics tools.
To build risk profiles in the past, you had to rely solely on criminal records and credit histories. Big data analytics tools have opened up more first-party data, improving risk assessment accuracy, but there's still space for growth. Part of the issue is that obtaining more data does not always mean major process changes. You're missing out on opportunities to minimise adverse selection if you don't have a way to incorporate all of that data, cross-analyze it, and gain key insights.
Breaking down barriers between legacy IT structures and more advanced insurance analytics tools will help you get more out of your tech investments.
The first step toward getting the most out of increasing amounts of first-party data is to remove data silos in your IT infrastructure. If you've done so, you'll be able to shape better pictures of consumer risk profiles, reducing the risk of adverse selection due to human error in underwriting.
The first step toward getting the most out of increasing amounts of first-party data is to remove data silos in your IT infrastructure. If you've done so, you'll be able to shape better pictures of consumer risk profiles, reducing the risk of adverse selection due to human error in underwriting.As the need to innovate becomes apparent to all parties involved, there is a noticeable shift in mindset among insurance leaders and experts. The industry has grown from a conservative to a new community that is primarily focused on creativity. More creativity, improved customer and employee service, increased agility, and creative applications of existing technology to age-old insuran