Allstate Applies Knapsack Problem to Home Insurance Risks

Allstate and IBM are tackling a fundamental challenge in insurance, balancing risk across entire portfolios, by applying solutions from computer science. The collaboration addresses a computational hurdle that becomes insurmountable with increasing complexity, particularly when assessing the interconnected risks inherent in homeowner’s insurance. A single event, such as a tornado impacting a community, illustrates the scale of the problem, potentially requiring payouts for every building in the affected area. “On the homeowner side, it really requires us to be thinking from a portfolio perspective and not just from an individual risk perspective,” stated Eric Huls, Allstate’s Chief Analytics Officer and Chief Data Officer, signaling a shift toward holistic risk management beyond individual policies. The companies believe quantum computing may offer a new approach for solving this complex version of the knapsack problem, currently addressed through extensive classical simulations.

Knapsack Problem Applied to Complex Insurance Portfolios

The abstract concept from computer science now underpins a practical challenge for Allstate and IBM: effectively managing complex insurance portfolios where risks are deeply interconnected. This computational hurdle, traditionally difficult to solve with classical computing when scaled to realistic scenarios, is forcing a re-evaluation of how insurers assess and balance risk across vast numbers of policies. The core issue isn’t simply calculating individual risk, but optimizing a portfolio to withstand correlated events, situations where a single disaster triggers widespread claims. Allstate’s approach is shifting towards holistic portfolio management, recognizing the limitations of assessing each property in isolation. Traditional risk models, designed for independent events like car accidents, fall short when applied to widespread, correlated perils like wildfires or hurricanes. Jean Utke, Data Scientist and Technical Director in Allstate’s Insurance Product organization, noted that these events represent some of the biggest factors impacting homeowner policies across a portfolio.

Currently, Allstate relies on classical simulations, running tens of thousands of scenarios to estimate potential outcomes. However, accurately assessing the impact of rare, high-cost events, those in the lowest one percent of simulations, becomes increasingly difficult due to statistical uncertainty. The company’s research with IBM focuses on leveraging quantum computing to move beyond approximation and simulation. Vaibhaw Kumar, an IBM quantum researcher and co-author of the recent paper published to arXiv in May, explained that current classical approaches “either rely on simulating multiple scenarios based on the uncertainties (accurate but computationally expensive) or by considering the worst-case range (safe but overly cautious).” The team’s quantum approach aims to generate candidate solutions that balance value and risk, refining them through a combination of quantum computation and classical processing. This iterative process, they hope, will yield more robust and efficient portfolio optimization as quantum hardware continues to improve.

Allstate & IBM’s Hybrid Quantum-Classical Workflow

Allstate and IBM are developing a novel approach to insurance portfolio optimization, leveraging the strengths of both quantum and classical computing to address challenges exceeding the capacity of current systems. While classical algorithms dominate the industry presently, the increasing complexity of interconnected risks necessitates exploration of alternative computational paradigms. The core of their collaborative effort, detailed in research published to arXiv in May, centers on tackling a well-known computer science challenge with significant implications for insurance companies seeking to balance risk and reward. This problem directly mirrors the task of constructing an insurance portfolio; insurers aim to maximize coverage while responsibly managing potential losses. Unlike isolated risks like individual car accidents, events such as widespread natural disasters introduce strong correlations, demanding a holistic view of portfolio exposure. Traditional methods rely heavily on simulations, but accurately modeling rare, high-impact events proves computationally expensive, and there’s a fair amount of uncertainty within those estimates.

To circumvent these limitations, Allstate and IBM developed a hybrid workflow. Their approach utilizes a quantum circuit running on IBM Quantum Heron to generate potential portfolio combinations, prioritizing both value and adherence to risk budgets. A subsequent classical processing step refines these candidates, correcting budgetary imbalances and incorporating learned patterns to improve future iterations. This iterative process creates a virtuous cycle, enhancing solution quality with each pass. “If the noise on the hardware keeps getting lesser and lesser,” Kumar said, “the workload on the classical side will get smaller and smaller.”

Quantum Circuit Training and Scalability Improvements

Researchers at IBM and Allstate are actively refining quantum circuit training techniques to address a critical hurdle in scaling these systems for practical applications, specifically within the complex realm of insurance portfolio optimization. A key innovation lies in a transfer learning approach, where the quantum circuit is initially trained on a smaller, more manageable problem before applying that learned knowledge to a larger, more complex scenario. This strategy circumvents a common issue in quantum machine learning; the learning signal often diminishes as the problem scales, hindering the circuit’s ability to effectively converge on a solution. The researchers rigorously tested their hybrid quantum-classical method against established classical approximation techniques, including parallel tempering, tabu search, simulated annealing, and genetic algorithms. On problems involving up to 75 items, the quantum approach demonstrated comparable performance, even matching the accuracy of exact solvers.

Importantly, as problem complexity increased, the quantum-classical method remained competitive with the strongest classical heuristic, offering a marginal advantage under stringent conditions. This suggests a promising trajectory for tackling the truly challenging, large-scale instances relevant to real-world insurance applications. Kumar anticipates further improvements, stating that the team has made their code and data publicly available, fostering transparency and encouraging further exploration within the quantum computing community.

Benchmarking Quantum Results Against Classical Algorithms

The pursuit of quantum advantage in practical applications extends beyond simply demonstrating a quantum computer can outperform classical methods; it demands rigorous benchmarking against established algorithms, particularly when tackling problems of real-world scale. Allstate and IBM are actively addressing this challenge within the complex domain of insurance portfolio management, a field where correlated risks present a significant computational hurdle. The core of their investigation lies in the knapsack problem from computer science, a challenge that becomes computationally intractable as the number of items, in this case, insured properties, increases dramatically. Allstate’s approach reflects a necessary shift in perspective. Unlike independent events like car accidents, natural disasters such as wildfires and hurricanes introduce strong correlations; a single event impacting a community can trigger widespread claims, demanding a holistic evaluation of portfolio vulnerability. Classical simulations, while currently employed, struggle with the computational burden of accurately modeling these rare, high-impact scenarios.

To circumvent these limitations, the collaboration developed a hybrid quantum-classical workflow. This approach leverages the strengths of both computing paradigms, with IBM Quantum Heron generating candidate solutions for optimal portfolio construction. A classical component then refines these results, ensuring they adhere to budgetary constraints and incorporating learned patterns to improve subsequent iterations. The team also implemented a technique to train the quantum circuit on smaller problems before scaling up, mitigating the issue of diminishing learning signals. While not yet surpassing classical methods on easily solvable problems, the quantum-classical approach showed promising results under tighter constraints, suggesting potential for significant gains as quantum hardware matures and problem sizes increase.