Akur8, a leading insurtech company, has achieved a significant milestone with the release of their latest research paper titled “Derivative Lasso: Credibility-Based Signal Fitting for GLMs.” This paper addresses a longstanding challenge faced by actuaries – finding the right balance between transparency and complexity when developing Generalized Linear Models (GLMs) for risk assessment. In this article, we will explore the importance of GLMs, the technical innovations presented in the paper, and their impact on the insurance industry.
Striking a Balance Between Transparency and Complexity in GLMs
Actuaries have historically encountered a trade-off between developing transparent but complex GLMs, or opting for automated yet opaque model-building techniques. This dilemma has hindered progress in the field, where robust statistical frameworks can often be overshadowed by the quest for automation. Akur8’s research paper aims to address this challenge head-on.
Generalized Linear Models
Generalized Linear Models have served as the bedrock of actuarial risk assessment for over two decades. Their robust statistical framework allows for clear-cut assumptions and direct output for rating tables. However, traditional GLMs often struggle to capture nonlinear relationships sufficiently. In the research paper, Mattia Casotto from Akur8 sheds light on the innovative approach known as the “derivative lasso” technique. This technique incorporates credibility aspects into GLMs while preserving their standard framework. By integrating nonlinearities into the GLM optimization process, actuaries can effectively model complex relationships without significant alterations.
Maintaining the Integrity of GLMs with Derivative Lasso
One of the key advantages of the derivative lasso technique is its ability to maintain the integrity of GLMs. This technique ensures that the fundamental statistical assumptions underlying GLMs are respected while still allowing for the incorporation of additional factors that influence risk assessment and rate modeling.
Enriching Actuarial Literature
Samuel Falmagne, the co-founder and CEO of Akur8, expresses pride in the team’s collaborative effort to enrich the actuarial literature with this comprehensive research paper. By sharing their insights and innovations, Akur8 aims to make a significant contribution to the advancement of actuarial science.
Automating Risk and Rate Modelling
Akur8’s solution stands out for its user-friendly approach, automating risk assessment and rate modelling. This not only saves valuable time on data preparation and modeling but also provides insurers with transparent GLM outputs and expedites market readiness. Moreover, this automation is achieved without the need for extensive coding expertise, making it accessible to a wide range of professionals.
Transparency and Automation
At its core, Akur8 aims to enhance the actuarial process by bringing transparency and automation to risk assessment and rate modelling. Their research paper and derivative lasso technique demonstrate the company’s commitment to streamlining and optimizing traditional methods while embracing cutting-edge technology.
Credibility-Based Signal Fitting for GLMs” highlights the company’s dedication to advancing actuarial science and providing innovative solutions for the insurance industry. By addressing the challenge of balancing transparency and complexity in GLMs, Akur8’s derivative lasso technique represents a breakthrough in risk assessment and rate modeling. As automation and transparency continue to play a crucial role in this field, Akur8’s commitment to pushing the boundaries of actuarial science is certain to pave the way for future advancements in the insurance industry.