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BLOG. 3 min read

5 Lessons Learned from Insurers Migrating to Modern Investment Technology

Our SS&C Singularity team has now helped more than 50 companies migrate to new investment technology using a variety of deployment models, including co-sourcing, full outsourcing and SaaS. Along the way, we’ve made some discoveries and learned a few lessons about how to support clients as they seek to modernize their investment infrastructure, increase revenue and reduce operating costs.

Lesson 1: Legacy systems or applications often don’t support easy or flexible integration with new investment technologies, creating a roadblock for many larger firms trying to modernize.

The limitations of legacy systems can include

  • Challenges and limitations with accommodating real-time vs. batch-based processing
  • Constrained interoperability with upstream and downstream systems and counterparties for data transfer, communication, reconciliation and synchronization
  • Limited flexibility related to data inquiries, reporting and transparency into critical operational processes
  • Ability to only accommodate specific asset types (fixed income, derivatives, loans, etc.)

Proper due diligence is required to choose the right technology that can help consolidate processing, accounting and reporting across all asset types in the investment portfolio.

Lesson 2. It takes more than amazing tech to address today’s evolving challenges.

  • It is just as critical to have the business acumen and relevant domain expertise to understand and validate the inputs and outputs that modern technology applications can handle.
  • The operating and deployment models for these new technologies need to be flexible enough to leverage both in-house and external/outsourced expertise and resources where required. 
  • Combining modern technology with access to the right domain experts will ensure an efficiently scalable and flexible operating model going forward, as business and investment needs continue to change over time.

Lesson 3. Front-to-back processes are important. However, it’s equally important to incorporate an innovative “back-to-front” approach to operations because the information that middle and back-office processes generates drives front-office portfolio modeling and investment decisions.  

  • This information needs to be as timely, accurate and actionable as possible.
  • To accelerate middle and back-office processing, you need integrated data feeds that drive real-time updates to holdings valuations and cash, along with embedded reconciliation and exceptions management tools.
  • Of increasing importance are also tools to efficiently collect, read and process non-standardized documents and data—particularly relating to alternative investments—in a timely and scalable manner.

Lesson 4:  More sophisticated analytics tools are needed as investment portfolios are becoming increasingly diverse and complex.

  • To better manage risk and make informed investment decisions, analytical tools that can provide holistic views of the investment portfolio, incorporating both traditional and alternative asset types, are needed.
  • Such tools should offer shock, sensitivity and what-if scenarios that clearly highlight areas of exposure based on key variables or macro-economic situations.
  • The analytics should also be able to account for correlations between and across different investment types.

Lesson 5: Artificial Intelligence technologies are playing an increasingly critical role in helping to achieve incremental operational efficiency gains. 

These efficiency gains are primarily being driven through:

  • Increased automation of processes such as market data and event processing, security master updates and general ledger journal entry creation and posting activities, using robotic/intelligent automation tools
  • Increased use of Optical Character Recognition (OCR) and Natural Language Processing (NLP) models to collect, scan, read and process event notices related to complex/alternative assets such as partnership investments, loans, etc.
  • Increased use of machine learning to assist with matching and reconciliation activities, as well as to dynamically adapt tolerance rules and settings

SS&C Singularity’s AI-driven investment accounting, operations capabilities, broad asset coverage and advanced analytics are helping modernize insurers’ middle and back offices. Using machine learning, natural language processing, intelligent workflow tools and predictive analytics backed by SS&C’s insurance-specific service experts, our clients are able to efficiently broaden their investment horizons and grow their portfolios while reducing operational cost and complexity. Singularity provides automation, operational transparency, data access, reporting flexibility and analytical insights via a cloud-based, web and mobile-accessible user interface.

Contact us to set up a demo.

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