Insurers can no longer rely solely on the spread between traditional fixed income instruments and their future liabilities to balance. Insurers, and the broader asset management industry, are diversifying their portfolios to provide not only superior returns but increased capital protection against economic volatility.
These new asset types include public equities, structured credit products, private equity and debt, and other alternatives such as derivatives and real estate. While low interest-rate environments accelerated the move into new asset types for insurers, current high-rate environments present even greater challenges when managing common market risks across asset types.
These new assets provide superior returns, but present more robust risk management challenges. Access to these instruments usually necessitates external partnership, the storage and processing of new types of data, new investment and operational processes, and increased operational risks.
Stand-alone investment systems create a fragmented portfolio view
Siloed systems and data are often created in the process to develop or acquire investment capabilities in these new asset types. These systems usually reside upstream or downstream of core investment and accounting systems because of the unique challenges in trading and supporting alternative assets. The challenge to integrate workflows and data across a fragmented architecture has prompted significant investment for technology vendors.
It is common to find technology vendors that promise to be a single system supporting all processes for all assets but also technology vendors that can string disparate systems together for the purposes of workflow or reporting. Transitioning to a single enterprise system is an expensive and risky undertaking and adding workflow tools can create further technology fragmentation over time.
Preventing analytical gaps
Private markets present unique challenges because the availability of quality data is significantly different from public markets and because they require new risk management processes. As an example, private credit products such as asset-backed securities require complex cash flow analysis in order to properly manage market and liquidity risk. The cash forecasting process requires new data types as well as intensive processing power to model potential outcomes, which happens outside of core investment systems.
It’s imperative for the data loaded into this process to be well-understood, high-quality, and consistent with core IBOR and ABOR systems for the output to be relevant. The models used in this process may run multiple times on different assumptions, which increases the importance of business rules to organize multiple sources of data or time-series of data.
As data becomes more complex and is consumed by more systems, the burden of transparency becomes more important. Whereas a small team may have supported data governance in the past, new tools and processes are often necessary for more complex architectures. Insurers who develop the capability to invest in new asset classes often find themselves subsequently overwhelmed by enterprise requirements to create a single source of truth, systemic data governance, and optimized operational processes.
Learn more
Integrating alternative investment data is just one of many data challenges facing insurers. Read more about these challenges – and innovative solutions – in our ebook, Solving Investment Data Challenges for Insurers.
