Quantum computing (QC) is a ground-breaking technology that leverages the principles of quantum mechanics to perform computations in ways that are fundamentally different from traditional computing. This unique capability allows quantum computers to solve complex problems with a greater variety of potential solutions. The accessibility of this technology has grown significantly with the advent of cloud-based quantum computing services such as Azure and AWS. While quantum computing has initially been associated with disrupting cryptography and cybersecurity, this article delves beyond that realm to explore the vast array of opportunities it presents to the insurance industry.

One of the key advantages of quantum computing lies in its ability to solve overly complex problems. This makes it particularly well-suited for tackling optimisation problems, such as finding the most efficient financial portfolios, training high performing propensity or fraud detection models, and more. Quantum computing also holds promise for data analytics, enabling the extraction of valuable insights from convoluted datasets that were previously inaccessible.

Early adopters of quantum computing recognise its significant value in enhancing business process efficiencies, increasing revenue, and achieving competitive advantage. Drawing upon Objectivity's experience in leveraging quantum computing across various industries and insights from pioneers within the insurance market, we explore the transformative potential of quantum computing from the perspective of the insurance value chain.

Pricing and Underwriting (P&U)

This crucial insurance value chain area relies heavily on data analytics, as insurers utilise historical claims data to develop P&U analytical models for risk assessment. However, as historical data is not always available and sufficient, insurers aim to augment their datasets with market-available data and look for efficient means to process such substantial amounts of information.

AXA, an early adopter of quantum computing, has been diligently examining its applications since 2018, with a specific focus on risk analysis and credit risk. Similarly, Lloyd’s, a prestigious insurance and reinsurance marketplace, in their 2021 report, has expressed interest in utilising quantum computing to simulate risk scenarios. In specialty insurance, Lloyd's envisions the potential of quantum computers for precise weather simulations, leading to improved catastrophe management and mitigation of weather-related losses, ultimately bolstering underwriting functions. In property insurance, accurate weather simulations can significantly improve catastrophe modelling, positively impacting pricing, reserving, and policy limit setting.

IBM, a company that constructs quantum hardware themselves, acknowledges in their quantum computing report that prediction modelling holds exciting potential for insurers to create more accurate pricing and risk models. They believe that quantum computing can accelerate risk scenario simulations and enhance risk management capabilities. A widely employed technique in this area is the complex Monte Carlo simulation, used to assess the impact of risk and uncertainty to estimate the probability of various outcomes. By expediting these simulations, insurers can quickly identify and mitigate risks, thereby reducing financial losses resulting from fraud and poor data management, estimated to range from $10 to $40 billion annually. IBM proposes leveraging quantum computing to identify subtle patterns in data to increase profits.

Operations

Statistics on customer loss rate experienced by small- and medium-sized financial institutions indicate that customer experience is the decisive factor for one in four clients willing to switch insurers. Therefore, the process of customer retention through policy renewal holds significant value.

Collecting feedback on operations during every customer interaction is crucial for insurers. To this end, artificial intelligence (AI), specifically natural language processing technologies, can be leveraged to measure emotional behaviour. For instance, in specialty insurance, where the pricing process can be lengthy, decision-makers may experience impatience or even frustration, reducing the likelihood of contract signing. By ensuring smooth and efficient operations, insurers can enhance their professionalism perception and foster brand loyalty.

Fraud

While most insurers are currently prioritising process efficiency in the claims area, fraud remains a persistent challenge in the industry. Lloyd’s has identified fraud detection as a compelling use case where quantum computing can yield substantial returns on investment. By leveraging quantum computing, insurers can train advanced machine learning algorithms for fraud detection to be more efficient and accurate. IBM anticipates significant enhancements in fraud detection by reducing the high false positive rate, which currently stands at 80%. A false positive occurs when a fraud detection system mistakenly identifies a legitimate transaction as fraudulent. Reducing false positives has a profound impact on process efficiency, allowing insurers to focus on genuine fraud cases and better allocate resources.

Product Development, Sales, and Distribution

The customers’ expectations for products that match their exact needs are higher than ever before. To maximise sales, insurers need to effectively retain their existing clients and attract a new generation of customers.

Both Lloyd’s and IBM have highlighted the role of quantum computing in enhancing customer targeting through the utilisation of proprietary and third-party data to create personalised offerings. By analysing clients' core data, such as policy and claim history, as well as behavioural data, such as response time or completed system actions, insurers can create customised products and services, tailored to individual customers. Quantum computing may be the winning technology in this context, as it empowers insurers to leverage the vast complexity of data at their disposal.

Objectivity’s Perspective

The full potential of quantum computing in the insurance industry is yet to be realised, due to the infrastructure limitations, a scarcity of vendors, or resistance from existing IT decision-makers. Additionally, there is a need for increased business and technical awareness of quantum computing and a shortage of in-house skills to fully leverage its capabilities.

Objectivity believes that the successful adoption of quantum computing requires not only a technical understanding of the technology but also a data-driven mindset from product visionaries and product owners.

In the insurance domain, many problems can be expressed as optimisation problems, which align perfectly with the strengths of quantum computing. Unlike classical systems that rely on step-by-step discrimination procedures, quantum computing can natively handle constraints and search for solutions. Quantum machine learning algorithms have been developed specifically to address the computational challenges associated with pattern recognition in data, which is essential for economic forecasting. Furthermore, quantum systems can efficiently sample probability distribution functions, surpassing the speed and applicability limitations of Monte Carlo methods.

Embracing the Potential of Quantum Computing

While the underlying principles of quantum computing may seem intimidating to non-scientists, it is essential to recognise that its capabilities are already available to those who wish to explore or adopt them, without requiring a significant investment upfront. With the accessibility of quantum computational capacity through cloud-based services, starting the journey is much easier than expected. At Objectivity, we anticipate that a successful proof-of-concept project solving a genuine business challenge can take as little as 1 to 3 months.

Several key players on both the business and technology fronts have emerged as thought leaders in the field of quantum computing. The early adopters in the insurance sector recognise the transformative potential of quantum computing.

Quantum computing can enhance data science and, drawing upon our experience from several industries, Objectivity recommends incorporating quantum computing as part of every company's data strategy. Products and services should be built in a data-driven model that is not restricted by computational constraints.

We believe that customer targeting, pricing and underwriting, as well as operational efficiency, can be vastly improved by harnessing technical advancements and the benefits of quantum computing. The insurance industry is dynamic, with fierce competition and growing data volumes, and companies are struggling to keep pace with their products and services. Is quantum computing the game changer the industry needs? With the potential it offers, it could pave the way for further innovation and continued growth.

Michał is a graduate of the University of Cambridge and ETH Zurich, who has dedicated his career to the intersection of quantum computing and business. Drawing from his diverse experience across the UK, USA, Germany, and Switzerland, he focuses on integrating quantum principles into enterprise applications.

Throughout his professional journey, Michał completed internships in Quantum AI groups at globally recognised institutions, such as CERN and Los Alamos National Laboratory in the United States. These experiences have cemented his understanding of cutting-edge developments in quantum computing and their potential application in the business sector.

Passionate about sharing his knowledge and experience, Michał is also a committed quantum educator, aiming to make this complex field accessible to a broader audience.

Grzegorz has over 15 years of experience in building bespoke, business-value-driven solutions, with a strong research background gained in Spain, France, and Poland.

He is passionate about leveraging the latest technologies for business competitive advantage. In his career, he has worked on large digital transformation programmes in several different sectors and is keen on bringing his experience to financial services.

His primary focus currently is streamlining business processes using data analytics in the dynamically digitalising insurance sector.