Gagik Mikaelian, director, sales at FactSet, talks about insurer technology issues – and how AI might help now and in the future.

What kinds of initiatives has FactSet been working on to assist insurers on the asset side?

FactSet aligns most of its initiatives with the Insurance Client Advisory Board (CAB). Established in 2016, the Insurance CAB functions as a collaborative hub where insurance investment, risk, and actuarial teams meet twice a year to discuss and advise on the most pressing issues.

In a commitment to technological advancement, FactSet consistently invests its resources into commercial and in-house technologies and open-source platforms. This infusion of expertise and resources accelerates product development cycle times for complex fixed income, structured, derivative, alternative, and risk analytics by leveraging advanced, high-speed parallel computing grids. Some of our current initiatives encompass massively parallel processing, machine learning (ML), artificial intelligence (AI), big data storage and transformation, JavaScript/HTML5 technologies, and regional content delivery networks. We are also enhancing our production offerings via third-party infrastructures and secure cloud technologies for seamless data storage and distribution.

On the content side, we continue improving the data collection and modeling for alternatives, specifically for risk and regulatory stress testing. Our focus also extends to trading technology and a slew of other services and technologies that aid the financial community in discovering efficient SaaS solutions.

Where do you see technology helping insurer investments the most?

Data and reporting software advancements have empowered insurers to transition from monthly or quarterly retrospective analysis to daily or intraday forward-looking perspectives, incorporating factors such as stress tests and more robust risk measures. Dynamic portfolio updates have paved the way for improved connectivity between the middle and front offices. Moreover, by adopting cloud technologies, insurers have integrated risk, actuarial, portfolio management, and operational functions, harmonising their data across the enterprise. In tight labour markets, technology has become instrumental, with insurers increasingly seeking platforms that automate data integration, enrich data with custom analytics, and offer advisory services to augment their IT and investment operations.

AI and machine learning have been ubiquitous themes in the market recently – are these applicable for insurance investing too – and how?

We see experimentation with AI/ML in insurance sales, underwriting, and policy support functions and less in investment functions. However, there is great anticipation that AI/ML will streamline credit research, trading, and portfolio management workflows. Credit research benefits indirectly from AI/ML by utilising platforms like FactSet with embedded AI/ML capabilities to collect data and condense news or filings to produce signals. Portfolio management will be the next frontier where we see significant opportunities to increase user productivity. With insurance investment data becoming more dynamic and accessible in the cloud, AI can bring a robust co-pilot infrastructure to various portfolio functions. AI can scout for new trades, summarise portfolio performance, execute dynamic hedging, optimise risk-based capital, and incorporate scenario analysis to assist with asset allocation. There are many possibilities for AI and it will significantly benefit all.

How are insurers overcoming the data challenge in their technology use?

We see a few initiatives among insurers to overcome the data challenges. Many are connecting the number of solution providers required for their middle office operations and deploying a SaaS platform, requiring no technical infrastructure and no upgrade to receive new enhancements. We also see insurers governing the data quality and streamlining the distribution to create better collaboration among the investment teams. Insurers want to provide decision-makers with access to trusted data, analytics, and official performance data with as little friction as possible. FactSet helps insurance investment teams overcome data challenges by:

• Integrating Book of Record (ABOR/IBOR) client data for risk and performance calculations.
• Introducing data controls to help clients diagnose data issues pre-calculation, ensuring that only high-quality data is used in analytics enrichment and reporting.
• Deploying FactSet's Portfolio Services technology and people to augment our clients' production teams and automate their end-to-end calculation process – empowering them to do more with fewer dedicated resources.
• Utilising data persist and locking capabilities to analytics result sets to create official book of record.

What do you see as the main issues and challenges for the future in relation to insurer tech needs?

Incorporating actuarial predictions into the asset allocation and portfolio construction process poses a considerable challenge, primarily attributed to the localised operation of many existing actuarial systems. Nonetheless, promoting enhanced collaboration and integration between actuarial and investment systems is crucial as it empowers investment teams and can lower risks and increase returns. This marks the juncture where the strategic coordination of assets and liabilities proves central to reducing overarching risks across the balance sheet.

Adopting ESG and climate risk measurement mandates, advanced data management and sophisticated reporting capabilities is another area where technology can assist.

Finally, incorporating private credit, direct lending, and alternatives into consolidated general account views will drive many technology initiatives in the coming years.