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  "documentTitle": "AI and Next Wave Transformation GAM",
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  "notes": "The slide discusses the shift from traditional ALM/SAA processes to AI-driven models due to regulatory pressures (IFRS 17/9) and market volatility.",
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      "text": "In one step, AI models can compare portfolio holdings and their risk levels, optimize runoff profiles, and establish new risk thresholds.",
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      "text": "Regulations are one source of pressure. The International Financial Reporting Standards (IFRS) amendments 17 and 9 that took effect in Europe and Asia in 2023 have brought a new set of accounting practices. The IFRS requirements for standardized performance metrics have compelled many asset managers serving insurance clients in those regions to rethink their previous asset allocation strategies so that they can achieve their objectives.",
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      "text": "The rapid turnaround makes it possible to provide insurance clients with a transparent, multidimensional assessment of which assets are being stacked against which liabilities—and the analysis can be performed monthly or even weekly. On the liability side, AI has proven itself able to forecast insurance policy lapse rates. This is a capability that was previously available only to managers of larger portfolios with the means to build analytical models to scale. Now, however, GenAI models, which can systematize vast amounts of scattered data from both structured and unstructured sources, can make this capability available to asset managers of any size.",
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      "text": "GenAI models provide the ability to develop and automate many reporting exercises. For example, reconciling the market performance of portfolio holdings with the accounting figures is still largely a manual exercise, but it will not be for much longer. Using GenAI, an asset manager can connect automatically with all requisite data platforms and quickly download a full report. In this case, too, the system provides a fully transparent disclosure of where the numbers come from.",
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      "text": "To drive performance and lessen risk, insurance asset managers have long relied on two main analytical processes. Asset and liability management (ALM) is used to inform the investment team about the commitments made through policies, while strategic asset allocation (SAA) helps determine how to maximize investment upside while minimizing risks for the insurer.",
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      "text": "In the insurance industry, investment income can represent as much as 30% to 50% of a company’s earnings. Each day, billions of dollars from insurance portfolios move through the financial markets. As a result, any small change in performance outcome, even if it’s only a matter of decimal points, can add or destroy significant amounts of value.",
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      "text": "By using AI- and GenAI-powered risk and allocation analytics, portfolio managers also gain access to a wider scope of investment parameters and data, with the ability to react quickly when market changes call for adjustments. These advanced systems are going to become a source of competitive advantage in the next few years—and it will be mandatory for insurance asset managers to embrace AI to continue producing winning investment results and cost efficiencies.",
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      "text": "Now, however, asset managers are under pressure to perform these processes in much greater detail and far more often.",
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      "text": "But even greater pressures have arisen from the geopolitical turmoil and resulting market uncertainties that affect every part of the world. In this chaotic climate, insurance portfolio managers need to be prepared with in-depth market intelligence so that they are ready to make adjustments far more frequently. Whereas it used to be typical to conduct ALM and SAA reviews once or twice a year, quarterly reviews are now considered the minimum requirement, and some firms are starting to conduct the process every month, leveraging a greater amount of internal and external data.",
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      "text": "AI models are able to boost the efficiency of both the ALM and SAA processes in a number of ways. Currently, most reviews are still performed using mathematical models that optimize the results using only one variable at a time. An AI model, however, can compare portfolio holdings and their risk levels, optimize runoff profiles, and establish new risk thresholds all in one step. With the present-day systems, portfolio managers typically lack the resources to perform the cumbersome task of analyzing underlying assets from third-party sources, such as managers of funds of funds or ETFs, more often than two or three times a year. AI, however, can quickly detect and analyze that data.",
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      "text": "The most advanced players are starting to use AI and its generative AI (GenAI) subset to perform their ALM and SAA processes with the depth and frequency that’s now required, and it is becoming clear that this is where the future lies for insurance investment management. All models are especially effective at combining large amounts of data, including unstructured data from multiple sources, to inform the decision-making task. These models make it possible to extract more value from the analytical processes and reduce the associated costs by 5% to 15%. Moreover, with the exponentially greater efficiency gained from AI, some players have been able to achieve risk-adjusted returns that have been 10 to 20 basis points higher than previous performance.",
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