From Research to Reality: How insureAI's Machine Learning Approach is Transforming Actuarial Reserving | InsureAI Blog

"Using InsureAI's methodology is like witnessing a stroke of genius in action." - Leading MGA Executive

Executive Summary for Non-Actuaries

For insurance executives and financial leaders without an actuarial background, our innovation can be summarized simply: We've developed a data-driven approach that takes the guesswork out of determining how much money to set aside for future claims.

Traditional reserving methods rely heavily on actuarial judgment, making them difficult to validate objectively. Our machine learning methodology:

Reduces reserve volatility by up to 20%
Improves accuracy in predicting future claim payments
Provides clear, defensible evidence for regulatory reporting
Automates the selection of optimal reserving parameters

The result? More accurate financial statements, improved capital efficiency, and reduced operational risk-all while satisfying increasingly stringent regulatory requirements. See the glossary at the end for a definition of all the reserving acronyms used in this post.

Leading the Next Era of Actuarial Science

At insureAI, we take pride in our role as pioneers, seamlessly bridging traditional actuarial methodologies with groundbreaking artificial intelligence. Among our key contributions to the actuarial field is our innovative machine learning approach to IBNR reserving, first presented in the acclaimed research paper, "The Actuary and IBNR Techniques: A Machine Learning Approach," that I was priveleged to write with Caesar Balona.

This approach has sparked a significant paradigm shift, transforming reserving from an intuition-driven art form into a data-driven science, addressing the critical challenge of model and parameter selection-a fundamental concern in insurance operations and regulatory compliance.

Turning the Art of Reserving into a Precise Science

Historically, actuaries have relied heavily on professional judgment and established heuristics to determine Incurred But Not Reported (IBNR) reserves. Despite the array of available reserving methodologies-Chain Ladder (CL), Bornhuetter-Ferguson (BF), and Cape Cod (CC)-the process has remained inherently subjective.

In today's regulatory environment, with standards such as Solvency II and IFRS 17 demanding objective "best estimate" reserves, such subjectivity is no longer sufficient. Insurers must clearly demonstrate the rationale behind their reserving approaches. Yet traditional methods have left actuaries struggling to objectively validate their choices, from selecting loss ratios in BF to determining decay parameters in CC.

Our methodology confronts this subjectivity directly, applying sophisticated machine learning techniques to fundamentally transform how reserving models are chosen and optimized.

Integrating Machine Learning with Actuarial Insight

At its essence, our framework redefines reserving model selection as a supervised learning problem. Rather than relying solely on expert judgment, our approach rigorously assesses model performance based on predictive accuracy and stability. Specifically, we:

Continuously re-reserve historical data by incorporating successive calendar-year experiences.
Analyze previous reserving outcomes against actual claim developments.
Introduce objective performance metrics-Claims Development Result (CDR) and Actual versus Expected (AvE)-to systematically evaluate models.
Optimize the selection of models and parameters to minimize these metrics.

This structured approach allows actuaries to objectively evaluate hundreds of model variants, uncovering the optimal solutions tailored precisely to their data.

What Sets Our Methodology Apart?

Our paper introduces several innovations that markedly differentiate our approach from traditional methodologies:

Objective Metrics for Robust Model Selection

We propose two principal metrics to assess reserving model performance:

Claims Development Result (CDR): Evaluates model stability and predictive capability, penalizing erratic reserve fluctuations.
Actual versus Expected (AvE): Purely predictive, this metric measures the accuracy of forecasting future claims.

Parameter Optimization

Our systematic framework eliminates guesswork from parameter selection. For example:

Chain Ladder Method: Optimizes the choice of development factors and the exclusion of extreme outliers.
Bornhuetter-Ferguson Method: Precisely determines optimal a priori loss ratios.
Cape Cod Method: Fine-tunes credibility allocations through decay parameter optimization.

Robust Testing Across Diverse Data

To validate our approach comprehensively, we tested it across a spectrum of claim triangles:

Stable Swiss liability data
Volatile long-tail quarterly liability data
Short-tail quarterly property data with negative claim developments

This breadth of testing underscores our methodology's adaptability to diverse insurance scenarios and data volatility.

Proven Results and Quantifiable Benefits

Our methodology delivered impressive outcomes:

Swiss Liability Data: Reduced RMSE by 7.8% (CDR-optimized CL method), accurately identified optimal BF loss ratios, and improved CC accuracy by 4%.
Volatile Quarterly Data: AvE optimization reduced RMSE by 19.5%, effectively handling volatility and negative developments, showcasing our method's versatility.

Critically, we demonstrated that different scenarios require tailored metric selections-stable environments benefit from CDR's stability, whereas volatile conditions necessitate AvE's predictive precision.

Real-World Impact: The Atlas Case Study

The pressure of year-end and Q1 valuation periods is a familiar challenge for reserving teams. However, technology offers a pathway to transform this critical process. We recently partnered with "Atlas" (name changed for confidentiality), a prominent personal lines insurer, to demonstrate the power of insureAI's reserving platform during their crucial Q1 valuations.

By leveraging insureAI's machine-led reserving algorithms, Atlas experienced a significant shift:

Enhanced Speed & Efficiency: Manual, time-consuming processes were replaced with rapid reserve calculations, freeing up actuarial resources for strategic analysis rather than repetitive tasks.
Granular Trend Insights: The platform delivered more than just reserve figures; it provided deep dives into underlying frequency and severity trends across the portfolio, enabling proactive understanding and adjustments.
Robust Sufficiency Analysis: Automated Actual versus Expected (AvE) investigations provided objective validation of model performance, bolstering confidence in the final reserve estimates.
Reduced P&L Volatility: The sophisticated, auto-calibrating algorithms and objective methodology ranking resulted in smoother, more predictable reserve movements, minimizing P&L fluctuations and strengthening stakeholder trust.

This transformation extends beyond operational efficiency. The insights generated by our methodology empowered Atlas with more informed upstream business planning and budgeting and data-driven input for optimizing reinsurance strategies.

Atlas's success highlights how modern, AI-driven technology, built upon rigorous actuarial principles, can elevate the reserving function from a compliance exercise to a strategic asset.

Explainability & Governance

Stakeholders value accuracy, but they rely on transparency for decision-making. insureAI embeds explainable-AI diagnostics directly into every reserving run, transforming sophisticated model outputs into clear insights for board-level discussions. For example, feature-level attribution plots highlight the key calendar, accident, or exposure variables driving each reserve period's movement, ranked by their marginal impact. This provides immediate clarity on the reasons behind reserve changes, not just the magnitude. Collectively, these tools ensure that complex machine-learning outcomes become trusted, understandable evidence for actuaries, CROs, and regulators alike.

Place of Origin

Perhaps the most compelling evidence of the methodology's value is its broad adoption across the industry. In the years since publication, we've seen our framework implemented by numerous insurers and, more tellingly, incorporated into competitors' commercial reserving software. While imitation is the sincerest form of flattery, we view this proliferation with mixed feelings. On one hand, we're proud to have influenced industry practice so significantly. On the other, some implementations miss the crucial nuances of the original research, leading to suboptimal results.

As the originators of this approach, we believe we have the deepest understanding of both its theoretical foundations and practical applications. This expertise has allowed us to continue developing and refining the methodology well beyond what was described in the original paper.

Industry Recognition and Influence

Our groundbreaking work has significantly impacted the actuarial community, earning prestigious accolades:

Brian Hey Prize (IFoA)
Highly Commended Paper Recognition (ASSA)

Moreover, our approach has been featured in The Actuary, highlighting our work in an insightful article titled "Objective Automation in Reserving", further validating the practicality and effectiveness of our methodology.

Continuous Evolution of Our Reserving Methodology

At insureAI, our commitment to innovation remains relentless. We've continued to evolve our methodology with advanced techniques, pushing boundaries to address emerging challenges and opportunities. While specifics remain confidential, rest assured that our latest enhancements provide unprecedented predictive accuracy and efficiency, ensuring we stay ahead in the ever-evolving landscape of actuarial science.

The Future of Reserving Has Arrived

insureAI's journey from research to market-ready solution epitomizes our commitment to rigorous scholarship, innovative technology, and practical actuarial solutions. We are proud of how our innovation continues to reshape the actuarial landscape and are eager to bring even more powerful capabilities to our clients.

Take the Next Step

Ready to transform your reserving process? Contact our team today to arrange a demonstration of our methodology and learn how it can be tailored to your specific reserving challenges.

Email: info@insureai.co

At insureAI, the future of actuarial reserving is here-and it's waiting for you to claim it.

Glossary of Key Terms

IBNR (Incurred But Not Reported): Claims that have occurred but haven't yet been reported to the insurer, requiring financial reserves to be set aside.
Chain Ladder (CL): A traditional actuarial method that projects future claim developments based on historical patterns.
Bornhuetter-Ferguson (BF): A reserving method that combines an initial estimate (typically based on exposure) with emerging claims experience.
Cape Cod (CC): A variant of the BF method that determines initial estimates using all available data rather than external sources.
Claims Development Result (CDR): A metric measuring the change in estimated ultimate claims between two evaluation points.
Actual versus Expected (AvE): A comparison between actual observed values and previously forecasted values.
RMSE (Root Mean Square Error): A statistical measure quantifying the accuracy of predictions.