Beyond Spreadsheets: How AI and Data Are Reshaping Insurance Investment Strategies
Imagine an insurance company's investment team managing billions in assets using the same tool as a household budget: Microsoft Excel. For many insurers, this is still the reality. But as investment portfolios grow more complex with alternative assets, ESG requirements, and volatile interest rates, the limitations of manual spreadsheets are becoming a critical business risk. In an exclusive interview, Maxim Pertl, Partner for Asset Owners & Managers DACH at Clearwater Analytics, outlines the urgent need for digital transformation and how Artificial Intelligence (AI) is moving from a buzzword to a core component of modern insurance investment management.
The AI Reality in Insurance: Beyond Basic Chatbots
When asked if there are AI pioneers in insurance, Pertl notes that while many companies have digitalization programs, most AI use is limited to internal ChatGPT-like chatbots. True transformation, he argues, comes from integrating AI deeply into the investment workflow. Clearwater's platform, backed by a $100+ million annual R&D budget, has enabled hundreds of insurance clients to become pioneers. Their "copilots" assist professionals by generating real-time insights and creating complex reports from massive datasets in seconds—a task impossible for manual processes.
The Legacy System Dilemma: Big vs. Small Insurers
Is the legacy system burden heavier for large insurers? Pertl offers a nuanced view. Large, multinational insurers face complexity from multiple regulations, languages, and consolidation needs but have more capital to invest. Smaller insurers have less capital but must meet the same regulatory hurdles. The common enemy for all is "decades-old, historically grown architecture"—some systems are nearly 30 years old—coupled with a severe talent shortage, cost pressure, and an aging society. "Anyone who bets on modernization and digital transformation will be among the winners and create significant competitive advantages," Pertl states.
The Impact of Interest Rates and ESG on Investment Portfolios
The interview highlights two major external forces reshaping insurer portfolios:
| Factor | Impact on Insurance Investments | Technology Implication |
|---|---|---|
| Interest Rate Policy | Drove a massive shift towards alternative asset classes (private equity, infrastructure, real estate) as insurers sought yield to meet long-term guarantee obligations. | Alternatives come with high data complexity. Systems must be adaptable to model and report on these non-traditional assets accurately. |
| ESG Requirements | Influences both internal investments and product offerings (e.g., ESG-focused life insurance). Reporting on ESG metrics is now a core regulatory and customer demand. | Requires processing vast new datasets (carbon footprints, governance scores) and integrating them into risk and performance analytics. |
Pertl notes that in Germany, private debt (Schuldscheindarlehen) remains a core holding, with a focus on investment-grade quality, but ESG-aware investments are growing in volume.
Why Excel Persists and Why It Must Go
Pertl describes spreadsheets as the "first-aid kit of the financial industry"—a planned temporary fix that becomes permanent due to other priorities. The hurdles to effective data management are immense: daily increasing data volume, complexity from new asset classes, and demands for real-time reporting from both regulators and end-clients.
"Is modern portfolio diversification even representable with Excel?" The answer is a qualified yes, but it's no longer modern. Generative AI can provide insights based on gigantic global data sets that a single company could never compile or analyze manually.
The Data Challenge: Volume, Quality, and Processing
Today's investment managers must access and process "vast amounts" of data: master data, pricing data, index data, ratings, benchmarking data, and ESG components. On this data, hundreds of analytics are run for risk views, stress tests, performance, and reporting.
Avoiding data errors is paramount. Pertl emphasizes that data quality is everything. Technology, including machine learning, is a key helper. Clearwater has built nearly 5,000 interfaces to handle the daily data deluge. However, technology alone isn't enough. They back it with a team of over 800 data management experts who handle exceptions. Machine learning and generative AI learn from mistakes, continuously improving Straight-Through Processing (STP) rates over time.
Regulatory Agility: The Solvency II Example
With recent loosening of Solvency II rules, insurers have new freedoms. Pertl advises caution: insurers are not pure profit-maximizers; quality and liquidity remain the focus, combined with ESG criteria. He suggests assuming that most requirements will be enforced in the medium term. The speed of implementing such regulatory changes varies, but for a specialized service provider like Clearwater, serving over 700 insurance clients, the implementation speed has increased dramatically due to scale and focused expertise.
The Path Forward for Insurers
The message is clear: The future of insurance asset management belongs to those who can effectively harness data and AI. The journey from Excel to AI involves:
- Acknowledging the Limitation of Legacy Tools: Spreadsheets are a risk, not a strategy.
- Investing in Integrated Platforms: Seek solutions that unify data, analytics, and reporting across all asset classes, including complex alternatives.
- Embracing AI as a Copilot: Use AI to augment human expertise, providing faster insights and automating repetitive reporting tasks.
- Prioritizing Data Quality: Build or partner for robust data management, as clean data is the fuel for all advanced analytics.
- Building Regulatory Agility: Choose technology partners that can rapidly adapt to changing rules like Solvency II, ESG, and others.
As Pertl concludes, the adage "Data is the new oil" holds truer than ever, forming the essential foundation for the generative AI future that is already transforming how insurers manage money and risk.