Description: We explore four plausible scenarios for how AI might reshape the future, focusing on employment as the main driver. At one extreme, AI boosts productivity, sparks entrepreneurship, and leads to near-full employment through human-AI collaboration and reformed education. At the other end of the spectrum AI-induced mass layoffs trigger an economic spiral, with soaring unemployment, creative stagnation, and societal resentment. The middle path presents a fragmented transition with a two-tier workforce, periodic recessions, and variable adaptation success. Lastly AI fizzles due to poor returns and rising costs, causing economic stagnation and investor losses without widespread disruption. Each scenario also includes potential impacts on the insurance industry, ranging from innovation and growth to regulatory strain and investment losses in the others. Proactive scenario planning, by preparing for multiple futures by identifying early warning signals, adapting plans, and strengthening institutional resilience is encouraged. The article closes with four additional speculative scenarios, including government-controlled AI, AI dominance, militarized AI, and mass surveillance. Ultimately, the piece argues that while AI’s future is uncertain, the choices made today will shape whether it becomes a partner, a threat, or a disappointment.

Description: ALM keeps adapting. From duration-matching before 2008, to stress testing in the prolonged low-rate 2010s, to disintermediation risks in the sharp 2021–2023 rate hikes, each shock has reshaped the playbook. In my new paper, I argue for a practical way forward: matching the first 5–7 years of asset and liability cash flows and holding liquidity buffers, a balance between rigid full matching and simple duration management.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: This article offers a comprehensive approach for “lean” model risk management (MRM), i.e., the management of an MRM program with few or possibly one employee devoted to the role. It explores key objectives of an MRM program, essential process elements, tips for efficiency, and examine the use of artificial intelligence (AI) in the models themselves and as a tool to increase efficiency of the program.

Description: The 19th annual Emerging Risks Survey, conducted among over 100 senior insurance and financial services leaders, highlights the key risks expected to impact the industry in the near and longer term. The survey categorizes risks into economic, geopolitical, societal, environmental, and technological domains, providing valuable foresight for actuaries and risk managers to anticipate and plan for evolving challenges.

Description: Building from the Emerging Risk Survey categories, this article addresses ways that infrastructure can support economic growth from the viewpoint of environmental, financial, geopolitical, societal and technological factors.

Description: Asset Liability Management (ALM) is a vital function in insurance operations, focusing on the strategic alignment of assets and liabilities to enhance risk-adjusted returns while ensuring compliance with regulations and fulfilling policyholder commitments. The rise of negative credit spreads poses a fundamental challenge to conventional ALM models, compelling insurance companies to reevaluate their investment strategies, hedging techniques, and risk management practices. This analysis indicates that negative credit spreads bring both considerable challenges and distinct opportunities for insurers. Although they narrow profit margins and complicate traditional spread-based investment approaches, they also offer potential for capital gains and alternative revenue sources for insurers willing to adapt their ALM strategies accordingly.