This March, The Fintech Times is shifting its spotlight towards insurtech, such as how advanced data analysis is driving efficiencies across the insurance sector.
From enhancing risk assessment accuracy to personalising products and services, insurers are leveraging data analytics to optimise decision-making processes, mitigate risks and cater to evolving consumer needs.
Industry experts share insights into the pivotal role of data in reshaping insurance operations and strategies worldwide.
Huge impact
Data analysis, particularly predictive analytics, have made major strides to improving risk assessment in insurance, says Ashleigh Gwilliam, director of insurance industry growth at customer lifecycle intelligence platform FullCircl.
“Vast amounts of information can be analysed allowing for more accurate pricing, individualisation of policies and mitigation of future losses.
“Algorithmic analysis is also having a huge impact on actuarial departments, identifying hidden trends in data that can uncover the real reasons for claims, advanced analytics can also identify key trigger moments when a claim is likely.
“Fraudulent claims are a key concern for every insurance company. Data analytics can improve detection rates – analysing documents and information for areas of potential miss-representation and inaccuracy – whilst ensuring claims are paid quickly and cost effectively.
“In BIBA’s 2024 Manifesto, it encourages brokers to “meet the needs of the modern economy” and “respond to emerging risks”. Advanced data analytics will have an increasingly important role in modern insurance decision, identifying trends and customer needs and driving innovation in products and services.”
Streamlining workflows
Rajeev Gupta, co-founder and chief product officer of Cowbell, an adaptive cyber insurance company, suggests that advanced data analytics, particularly in cyber risk assessment, enables businesses to gain clarity, simplify processes and improve efficiency and accuracy.
“Data analytics is not new to insurance. Actuaries have been analysing data for pricing and claims reserving for decades. However, the landscape has evolved lately, with advanced data analytics now used by insurers in all other areas of the business – from risk assessment and fraud detection to improving operational efficiency and even refining product roadmap.
“At Cowbell, we are actively assessing the cyber risk posture of over 39 million businesses in the US and the UK. With our rich data pool, we enable businesses to gain clarity about their cyber risk posture in relation to their industry peers in just minutes, while also simplifying and expediting the quoting process for agents, and making the underwriting process more objective.
“By automating processes and streamlining workflows, they are able to reduce costs, improve speed, and increase accuracy across various workflows.”
Improved efficiency
AI-driven tools are enabling personalised quoting, dynamic policy management and streamlined claims processing, according to Scott Logie, chief commercial officer at independent UK consultancy.
“Data analytics, particularly tools underpinned by AI, is powering more efficient, intelligent decision-making across the insurance pipeline and customer lifecycle.
“At the beginning of customers’ journeys, analytics is making quotes generation more personalised. Historically, insurers grouped customers into broad segments based on basic profiles. Now, machine learning models enable more datapoints to be analysed, creating accurate risk profiles for designing bespoke offers. We see this technology in action on comparison sites, which are underpinned by models trained on existing quotes for age, location, house type, and car make or model.
“Analytics is also used by insurers to manage ongoing policies. AI tools review customer data and suggest edits to premiums when a customer’s situation changes. For example, moving into a higher risk area or buying a more expensive car.
“Finally, AI is making claims decisions more efficient. Often, the basics around who, why, when and what are now dealt with automatically based on machine learning models trained on past claims. With fewer manual tasks, insurance advisors and experts can dedicate more time to complex cases and tasks that bring more value to the business.”
Better policy decisions
For Sarah Carver, head of retail banking, wealth and insurance at global financial services provider Delta Capita, insurers can make better, more informed decisions, optimise their internal processes and create value for both the business and the end customers by leveraging advanced data analysis.
“We see particular value driven in three key areas:
- Risk: Insurers can use advanced data analysis to both evaluate risk and then personalise risk assessments using both historical data and predictive scenario based modelling to predict future behaviour. This can also be meshed with individual behaviour patterns allowing for an enhanced risk picture and better policy decisions.
- Customer insights and servicing: Data-driven insights can help insurers understand customer preferences, behaviours, and needs better leading to much better servicing of these customers whether on a micro level of individual servicing or on a macro level to future product development and marketing strategies. Advanced data analysis can also help identify and prevent fraud before it occurs saving cost and retaining customer trust.
- Efficiency and precision: Whether in processing claims more efficiently through analysis to determine where time should be spent, setting precise pricing by using better data to offer both competitive pricing but also less generic ‘one size fits all’ approaches.”
Getting ahead
Alex Littlejohn, executive VP at US insurance brokerage Alliant Retail P&C, says that with the insurance industry’s increasing ability to understand analytics and uses for collected data, analytics are taking on a larger role in how underwriters review, charge and provide capacity on insurance programmes, in addition to leveraging claims analytics to understand how losses impact coverages.
“Assessments conducted based on insureds’ data affects how the insurance community rates and evaluates risks.
“From an insured perspective, analytics allow them to get ahead of underwriters’ decisions, enabling decision-making on limits and deductibles for programme optimisation, both prior to shopping the insurance market and then evaluating conditions they receive back from the market.
“We’ll always move forward in terms of how data and analytics impact decision-making, both in how clients decide to buy risk and mitigate their risk, and how insurance companies decide to provide capacity and charge for the risk.”
Better price risk
Insurance companies worldwide are adopting AI to understand better how insureds drive, says Rashid Galadanci, CEO and co-founder of Driver Technologies, an AI-based mobility tech company
“Specifically leveraging video telematics-based scoring, insurance companies can now underwrite and classify the risk based on how an individual, or even a whole fleet, really drives instead of traditional factors like credit scores or motion-only telematics, which miss critical factors like tailgating and traffic sign adherence.
“Telematics with video analysis is also incredibly valuable for the claims process for users as visual ground truth cuts substantial time and costs from the claims lifecycle and, in many cases, can eliminate any need for arbitration.
“Additionally, to assess and design safer communities, we must understand our current road infrastructure by studying anonymous road safety and road risk information to develop insights into the types of improvements we need.
“By analysing real-world, location-specific road risks derived from regular and image-based road segment data (RSD) using telematics and computer vision data, insurance companies can better price risk while educating their insureds with insights into the most dangerous intersections and best roadways to keep them safe.”
Better predictions
David Bairstow, chief product officer at EagleView, a provider of aerial insights for insurance companies in the US, underscores the critical role of data and analytics in helping insurers address significant challenges such as talent retention, increasing population density in disaster-prone areas, and economic pressures.
“The insurance industry is facing significant challenges. Employees with deep experience are retiring. Attracting new talent is proving difficult. Externally, more people now reside in areas often affected by severe events, increasing pressure on insurers to more effectively underwrite those property risks.
“Further, large-scale events also present challenges in effectively servicing insureds after such events occur. And recent inflationary trends continue to damage insurer economics.
“To stay competitive, carriers will need to use data and analytics to pro-actively assess climate risk and model property portfolio exposure. Being able to better predict catastrophic impact and forecast maximum exposure value – before events even occur – will help insurers manage their underwriting and pricing strategies.
“In the aftermath of large-scale events, property intelligence and analytics can be critical tools to help insurers better service their customers. For example, leveraging timely, high-resolution aerial imagery captured at scale across affected areas can help insurers to begin processing claims much faster and, in many cases, before First Notice of Loss (FNOL) is even filed by the insureds.
“Innovative data, analytics, and technology approaches like these will help insurers better serve their customers while also helping improve the structure and financial performance of the carriers’ property insurance portfolios.”
Improving accuracy
James Harrison, global head of insurance at Dun & Bradstreet, a business intelligence and data company.
“In today’s insurance landscape, the power of data analytics cannot be overstated. Advanced data analysis techniques are revolutionising how insurers assess risk, price policies, and make strategic decisions within the industry.
“One primary way data analysis is reshaping the insurance sector is through improving the accuracy of risk assessment. By leveraging vast amounts of data from multiple sources, insurers are better positioned to conduct real-time risk analysis for individual and systemic risks, which leads to precise underwriting decisions and reduced exposure to losses in a volatile risk environment.
“Moreover, data analytics enables insurers to personalise products and services to meet the evolving needs of customers. By analysing data like customer demographics, behaviour and preferences, lifestyle habits; insurers can tailor offerings, pricing, and coverage options according to the consumers; thus enhancing customer satisfaction and loyalty.
“Data analytics is empowering the insurance industry by utilising data-driven decision-making to optimise the entire value chain. In a competitive landscape today, insurers who embrace these technologies and leverage the power of data will not only simply survive but also set a precedent for a new era of more customer centric innovation.”
