Using AI to Better Manage Closed Blocks

Consolidators that are in the business of acquiring closed life insurance blocks often face challenges. Data and AI strategies offer a way out.

Blurry and angled photo showing a long hallway with blue, purple, and pink lights showing data sets on both sides


--Managing closed blocks is a drain to insurers' capital and leads to a higher price per policy.

--Strategies led by data and AI can let life insurers reimagine closed blocks, improve operational efficiency and enhance the customer experience.


Growth and cost optimization being the twin engines for sustainability, life insurers need to innovate products and services to cater to new realities such as longevity, retirement gaps and holistic wellness but at the same time focus on operational efficiency of legacy or closed blocks for better capital usage and reserve management. As a result, insurers must leverage technology advancements, data & AI strategy to manage closed blocks effectively. 

With insurers adopting various strategies such as reinsurance, outsourcing and consolidation to harness the proportionate locked-in capital, industry deals with complexities of asset liability management, regulatory demands, depreciating returns, administration of siloed products in legacy tech stack, process inefficiencies, etc., leading to higher costs and hurting customer experience.

Organizations can adopt two approaches to unwind the complexities and achieve operational efficiency: 

  • Drive Simplification & Digitization
  • Governance & Controls

Drive Simplification & Digitization

In dealing with the complexity of closed blocks, organizations can drive simplification in the following ways:

Cloud-based data lake – Consolidators that are in the business of acquiring closed blocks often face challenges such as long cycle time for integrating systems, data and the processes of acquired books. Cloud can be used to migrate the acquired book off its legacy platform, streamlining data extraction, data ingestion, processing, etc. Timelines for future acquisitions can be shortened and closed blocks managed effectively.

Low-code, no-code – Monolithic legacy systems often face challenges when it comes to administering closed books because of a lack of the needed skillsets, complexity in product administration, huge development costs for conversion, etc. Low-code, no-code platforms will significantly reduce the development life cycle and maintenance, enabling a transition to modern technology that eases integration with contact center operations and provides a seamless customer experience.

Digital twin – Process inefficiencies in customer servicing such as policy inquiry, FNOL, adjudication, payouts, etc. lead to increased price per policy. Application of process mining tools in conjunction with a digital twin -- a virtual representation of physical entities, data, its relationships and behavior -- will help identify bottlenecks and establish process and persona twins to streamline business process, aid in merging portfolios to achieve economies of scale and reduce cost of operations.

Generative AI – Productivity of IT operations and experience of in-force policy holders can be enhanced by query generation (i.e., converting natural language queries to SQL), data engineering, validation, aid in policy and claim servicing, bordereaux processing and customer service chatbots enabled with features such as claim summarization, document Q&A, etc.

See also: Moving Beyond Data Lakes

Governance & Controls

AI/ML for accelerated rate filing – As the risk profile of the customer changes, insurers managing in-force policies of legacy products need to be agile in filing in-force rate actions to state regulators. Traditional methods of data acquisition and processing of information (placed coverages, incurred claims) and projections for forecasting are time-consuming and often takes months. Automated data discovery, intelligent data extraction, ML-based DQM, centralized assumptions hub, etc. will enable lineage and traceability to regulators and accelerate rate filing and rate revision, thereby improving profitability.

Data governance and KPI model office for performance management - Establishing effective data controls and governance to measure the performance via key performance indicators (KPIs) is critical for enterprises to manage capital effectively. For instance, data quality issues in the customer data domain will affect the ability to merge the portfolios for optimization, establish a unified view of the customer for cross-sell and up-sell strategies, etc.

Way forward

Higher price-per-policy for managing closed blocks in the industry presents a significant opportunity for insurers to harness the combinatorial power of data and AI to improve their operations and governance, enhance customer service through digitized process and make capital management more effective.

Prathap Gokul

Profile picture for user PrathapGokul

Prathap Gokul

Prathap Gokul is a consulting partner with the analytics and insights group in TCS’s banking, financial services and insurance (BFSI) business unit.

He has over 23 years of industry experience in P&C, life and retirement and corporate functions. 



Read More