Core Insurance, Risk Management
Article | August 4, 2022
The COVID-19 pandemic has caused unprecedented disruption to the insurance industry overall, dramatically curtailing business activity, upending the everyday lives of employees and customers, and more. However, companies that derive a substantial portion of their business from motor insurance have enjoyed stronger bottom-line results during the pandemic than in previous years. That’s because when sudden lockdowns kept drivers at home and off the road (see exhibit), claims plunged by 60 to 80 percent almost immediately. As restrictions began to lift, claim volumes subsequently bounced back, although they remain 20 to 30 percent lower than they were before the pandemic. The corresponding drop in payouts for claims was only partially offset by the refunds on premiums that insurers paid to customers to compensate them for traveling fewer miles.
Are motor claims in Europe about to rebound?
As of mid-2021, motor claims volume remains suppressed—at least for the time being. For insurers, this offers a short-term window to pursue or accelerate strategic initiatives aimed at establishing claims excellence, a key driver of profitability. These initiatives include transforming claims processes to improve customer experience, building digital capabilities, leveraging advanced analytics to improve decision-making, and reducing long-standing sources of leakage. Acting now will help insurers be prepared when vaccination rates across Europe accelerate, economies reopen, and both mobility and motor claims rebound.
Even as the pandemic recedes and business returns, insurers are likely to confront three persistent challenges that can be addressed—at least in part—by transforming claims management to improve profitability.
Top-line pressure will continue. Pandemic-related top-line pressure will likely continue for the foreseeable future. If history serves as a guide, commercial lines, which suffered from a temporary halt in business activity in the tourism, aviation, entertainment, and local business sectors, may be slow to recover. During the 2008 financial crisis, for instance, commercial lines took significantly longer to recover than personal lines. As for personal lines today, declines in everyday commuting have altered customers’ perceptions of the value of insurance: if they drive less, they expect to pay less. As noted above, some insurers have proactively offered their customers premium paybacks for reduced car usage—a change that could endure.
Digital is here to stay. Because of the pandemic, people shifted many everyday activities to remote channels and adopted new digital tools. For example, across Europe, 60 to 70 percent of consumers moved some of their shopping online, and most intend to perpetuate the new habit after the pandemic ends. This shift in customer behavior extended to engagement with insurers. In the United Kingdom, claims notifications filed via digital channels doubled during the pandemic, and insurers received 30 percent more digital inquiries than in the past. However, customers’ growing expectations for an end-to-end digital experience—with 24/7 service, instant feedback, and a user-friendly interface—still place most insurers in the position of playing catch-up. The large majority of customers still prefer to place a call rather than use digital self-service; in Europe, for example, more than 50 percent of claims are initiated when a customer contacts an agent. This preference could indicate that insurers have yet to fully digitize the claims handling process.
Inflation will affect claims costs. Insurers anticipate increased pressure on claims costs from multiple sources. First, car repair shops have suffered the knock-on effects of the COVID-19-induced drop in claims volume. Many received government help, but they also responded by increasing labor rates and margins on spare parts. The claims inflation rate currently sits at 4 to 5 percent. Ongoing cost pressure means repair shops are unlikely to reinstate their pre-COVID-19 price levels without some restructuring in the sector. In one scenario, insurers could step into the role of ecosystem orchestrators, significantly consolidating repair volumes and offering strong incentives—including extending insurance services to include maintenance and offering negotiated prices for parts and labor—to repair shops to participate. Meanwhile, insurers can analyze increased volumes of claims data to continually assess the performance of repair shops and then use those insights to guide customers to the best deals.
Even before the pandemic, insurers had made strides in improving the bottom line by increasing productivity and optimizing technical excellence, particularly via pricing. Now is the time to tackle claims. Claims organizations can use this period of lower claims volume to plan their strategic investments in advanced analytics transformation, to devise new digital talent strategies, and to improve their understanding of customer needs and expectations.
A complete suite of analytics and updated process automation—prerequisites for accurate, end-to-end automation—constitute the backbone of the new claims and customer experience model. The tools are evolving, driving automated decision-making along the entire claims handling process: routing, triaging, liability negotiation, cost estimating, deciding to repair or write off damaged vehicles, cash settlements, and fraud detection. All these areas will increasingly use digital and analytics as opposed to manual labor, changing the entire claims operating model.
Responding to customer demands for a seamless claims experience is a top priority. The pandemic has proved that customers are eager for and accepting of new digital experiences. They expect full transparency throughout the claims journey; minimal effort on their part (for example, very little engagement back and forth with the agent to get the claim resolved and receive payment); faster resolution of claims, perhaps including automated payments; and the ability to move seamlessly between the digital and physical worlds.
Furthermore, insurers can work to reduce leakage and improve the bottom line. Leakage takes many forms, including replacing rather than repairing a vehicle, offering a luxury replacement vehicle rather than a car that matches the customer’s vehicle class, and incurring costs for in-person loss assessments even in obvious cases for which pictures would suffice. Tackling leakage will entail enabling efficient detection of anomalies, selecting claims for detailed review, and empowering the claims organizations to efficiently close claims that cast no doubt.
Accomplishing these critical objectives will entail a shift from a scattered and often siloed approach using unintegrated digital and analytics tools to end-to-end digital- and analytics-enabled claims processes. On the front end, insurers will need to establish tools on par with the top digital services their customers use every day (for example, ride-hailing apps, social media, and digital banks).
On the back end, claims organization will need to invest in a suite of analytics engines to support automated decision-making to cut costs. The opportunity starts with claims prevention—using telematics and the Internet of Things to issue safety warnings and damage prevention tips—and continues throughout the claims processing journey, from providing customers with an easy digital first notice of loss interface and improving claims cost accuracy, to digital selection of a repair shop and automated payment processing and invoice checks. This relative lull in activity also gives insurers a good time to provide teams handling claims with the training they need to learn new processes and operate new digital tools.
Claims are already rebounding, so the clock is ticking for insurers. Building end-to-end digital and analytics solutions requires significant investment and will take substantial time. For claims organizations, it is critical to act now or risk missing the opportunity to emerge from the pandemic stronger than competitors.
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Insurance Technology
Article | July 13, 2022
When building a practical framework, AI holds tremendous potential for insurers. Insurance companies can use AI to make better business decisions and provide differentiated customer experiences. To take advantage of AI, insurers need to know and clear the air about what is possible to do with AI.
Insurance with AI: Understand, Learn & Respond
Here are the ways insurers must use AI in their workforce and build a workable model.
Language: Insurers can use natural language processing using AI to extract legacy unstructured data and convert it into structured data. As a result, organizations can extract information and automatically classify it into different sections. In addition, AI can even learn and guide users to make decisions using machine learning and curtail errors.
Management: AI has emerged as a game-changer in managing the workforce, risks, and insurance functionalities and augmenting flawless products and services. While we talk about workforce management, AI puts tasks in one place, organizes them, and stores them under a data-proof model. So, no more scattered documents and pilling of files! AI is here, and it will transform and respond to businesses more efficiently with solution-driven aspects.
Efficiency: Businesses need to be proactive by having a smart workforce that adds efficiency. Before, the insurance sector had a sloppy work platform. But now, with the passing of time, they need to overcome and be more efficient at work. Using AI in your business will save a lot of time, energy and money. It will lead to faster processes that are error-free, accurate, and predictive, encourage crystal clear communication, and have fewer chances of fraud.
Insights on AI’s Role in Insurance
Existing and start-up insurance businesses will be fortified with the help of AI use cases. Let’s get some insights into AI's potential for businesses.
The global AI market is estimated to grow at a CAGR of 42.2% to $733.7 billion by 2027. The inclusion of AI in insurance records a growth of 56% until 2021.
AI has the potential to save insurance companies up to $390 billion by 2023.
In 2021, more than 40% of insurance businesses increased their expenditure on AI use cases and projects.
Source: PWC
These statistics show that AI in insurance is only going to get bigger. Investments in AI are high on the priority lists of decision-makers.
The Futuristic Hold
The insurance industry is under enormous pressure in terms of digital transformation. The rate of transformation is consistently accelerating. This paints the future of the insurance industry with AI to be more progressive with improved products and services, which will eventually host numerous opportunities for exponential expansion and reach globally.
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Claims
Article | July 15, 2022
Amid COVID-19, banks began offering mortgage deferrals and slashing credit card interest rates in half for cardholders who need relief. Home and auto insurance companies COVID-19 plans are now being released. How will this impact your insurance right now? How can you save on insurance during COVID-19, while making sure to stay protected?
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Insurance Technology
Article | May 20, 2022
A quick Google Trends search on data reveals that data analytics, data and analytics, data analysis, and predictive analytics have steadily grown in popularity among businesses across industries.
These terms peaked when business leaders searched for ways to increase ROI and reduce business costs and tech-based investments. The insurance industry is amongst the industries actively leveraging data analytics.
The rising importance of analytics in insurance has made CMOS take note too. As agility became more important in the insurance industry, more than 85% of global businesses shifted to a data-driven model.
The purpose of taking you back is to emphasize that, as a CMO, now you need to churn accurate data and turn it into relevant information. This is a necessary model to practice to make the right decisions or will improve the decision-making process.
Without data analytics, you are deciding in a void, and that’s not considered good practice. Forrester reports that 41% of insurance companies faced challenges in extracting data and making decisions based on it in 2020.
Take a look at how and what you can do with insurance analytics to cater to better insights into your decision-making process and, finally, ROI generation.
Bring Data to These Key Levels of Departments
Marketing
Analytics in insurance raises the bar in terms of marketing. As you know, marketing results frequently fluctuate, making data insights challenging to capture. CMOS who base their decisions solely on outcomes usually loses sight of making sound decisions due to unstructured data.
Therefore, it is essential to have an aligned platform for data analysis in insurance. To begin with, marketers must understand the various types of data analytics available. Most insurance marketers employ descriptive, predictive, and prescriptive analytics, among others. This will assist them in strategizing based on continuous data insights from various sources for any given initiative.
Sales
Sales leaders can also improve how they spend their time by using data analytics to create more accurate sales forecasts. However, the question is, how will they do it efficiently?
CRM software is the answer and solution to them. The software performs best because of its analytical capabilities in combination with data visualization, particularly predictive functions. It generates enormous amounts of data on customer interactions, which can then be used to inform decisions. You can assemble relevant data and use it to make some decisions, such as:
Acquisition and management of leads
Lead segmentation
Sales funnel optimization
There is enormous value in optimizing productive data by focusing on prospects likely to become loyal customers.
Operations
Utilizing data analytics in insurance boosts insurance operations. Small changes help to align a wide range of core processes. You can access data obtained from operations, observe key aspects of the overall processes, and make appropriate decisions. A targeted, timely, and data-driven approach will help you make decisions about these key functions, which can lead to business growth in the long run.
Bain's research in 2019 reports that seventy insurers were polled. They say data analytics will reach 58% in the marketing funnel and 45% in business operations.
Begin with Overcoming Barriers to your Decision-Making Process
Use Data to Identify Customer Patterns
Information from data can identify patterns. As mentioned above in the sales section, CRM's predictive modelling and the popular Google Analytics' descriptive overview are the two best platforms for identifying customer patterns.
What is the best way to get pertinent data? Data mining is the answer to it. Do you want to know about it? Then read data mining for pattern evaluation now!
As a CMO, you're probably aware that behavioral patterns are highly predictable and can sometimes result in unsatisfactory outcomes. This occurs when you are unable to obtain relevant data. And you end up performing ineffective marketing activities. To assist you in overcoming it, an AI-enabled platform can reduce the level of effort and provide the necessary data to study your customers' patterns in real-time. This is how you will notice a significant increase in sales.
According to research by McKinsey and Company, automation saves 43% of insurance employees’ time.
Segmenting Sales Plans
Following the establishment of your customers' patterns, segmenting the insurance sales plan is a necessary step. In this process, analytics provide detailed information about customers, allowing you to make decisions about sales functionalities. This will undoubtedly reduce the time, energy, and effort you previously spent.
Accurate customer segmentation and sales forecasting can also help tailor marketing efforts, improve the sales funnel, and keep sales strategies in check.
When Media 7 contacted Vishal Srivastava, Vice President (Model Validation) at Citi, here’s what he said about data segmentation through data analytics.
CMOs must ensure that adequate data quality checks have been performed, The goal is to ensure a scientific approach to data segmentation, sampling methodology, and data outliers, which can significantly impact revenue forecasts.”
Pricing & Savings
Analytics in insurance marketing can help CMOs make cost-cutting decisions and become more cost-effective in marketing efforts. It can set price ranges based on historical, current, and predictive performance. Also, analytics will help you figure out how to price things in the future, which will be good for ROI.
Keep Improving with Data to Stay Abreast with The Decision-Making Process
Better data organization in your business boosts productivity."
Warren Buffett, an American business magnate, investor, and philanthropist.
This phase is best suited to the current business environment. Implementing data analytics in insurance now will open up tremendous opportunities in the future. To make the most of them, you, as a CMO, must stick to a data-driven model for marketing actions.
Aside from that, it appears that the data analytics you select for your business must be capable of informing and driving performance. Performances ranging from risk assessment to sales forecasting and a plethora of actionable insights assist businesses in thriving.
Frequently Asked Question
How are data analytics used in insurance companies?
Data analytics empowers insurers to optimize each function and also assess risks. It also identifies trustworthy customers, which further boosts engagement.
What does data analytics mean in insurance?
Data analytics empowers insurance professionals by providing them with the business intelligence to understand their customers better, build better products and services, and thus, boost business growth.
How are insurance companies using data?
Insurers can use data to gain insights from customers’ profiles. They can review their history, behavioral pattern, and marketing needs to develop strategies and provide marketing services.
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