Life After COVID-19: Charting a Course to Actuarial Transformation

COVID-19 has impacted almost every nation, disrupting the normal functioning of society and business alike. The Insurance sector, particularly Life & Pensions, faces disruption across claim demands, underwriting risk, and investment due to economic uncertainty. Given its primary objective to manage risk, the Actuarial function is heavily impacted within the insurance organisation. This paper focuses on the lessons learned during the pandemic and how the actuarial function at Life insurance firms might best evolve for effectiveness in a post-COVID-19 world.

No other pandemic in the 21st Century has impacted the globe on the same scale as COVID-19. The state-imposed lockdowns and shelter-at-home restrictions slowed economic activity drastically. As a measure to mitigate the impact of disruption, the Bank of England cut base rates from 0.75% to 0.25%. Investors were unsure of what to expect next, as the market volatility indicator reached an all-time high and capital outflows from emerging markets hit levels comparable to the 2008 financial crisis.

With uncertainty around the extent of damage that the pandemic could cause, the number of deaths, and the duration until restrictions may be lifted, insurance organisations rely on their actuarial function to effectively model exposure to different types of risks and perform analyses to ensure that the business is not financially stressed and that asset liability mismatch is efficiently managed.

To modernise, actuarial departments have begun upgrading systems and financial models to comply with IFRS17 reporting changes. These regulatory and transformation initiatives have also underlined the need to enhance existing capabilities by utilising data and technology to their fullest. The rapidly evolving global economic and financial conditions due to COVID-19 have reaffirmed that automation and data analytics are the best ways forward to manage uncertain conditions ahead.

Change in Exposure to Risk Factors Due to COVID-19

Risks for a life insurance and pension business can be largely classified as tactical or strategic. Tactical risks are the type that are actively managed by the organisation through internal risk models. Strategic risks are driven by either regulatory changes, or technological disruptors, legal or other external change factors.

The above chart describes the exposure of life insurance businesses towards various risks due to COVID-19 that actuaries must monitor:

  • Investment Risk: The value and cash flow of investment assets to support contract holder liabilities is exposed to higher losses during the pandemic due to unfavorable economic and adverse market conditions, high volatility of public equity and GBP FX yield curve inversions. High market volatility has reduced the ability of insurers to react to market events as effectively as they would prefer and might be forced to sell their investment holdings at a lower price.
  • Insurance Risk: As a consequence of the COVID-19 infection spread, morbidity and mortality levels have elevated significantly. The short-term implications of increased mortality rates force insurers to liquidate assets ahead maturity periods. There are also risks due to increased claims, fee waivers for lapse and surrenders as recommended by state regulators which could disrupt the Asset Liability Management (ALM) structure.
  • Market and Liquidity Risk: Low interest rates and credit spreads, negative yields and high market volatility all impact the ability to raise market capital, thereby affecting overall liquidity. This also puts immense pressure on the financials to serve short-term obligations, mortality and morbidity adversities, and challenges related to withdrawals.
  • Operational Risk: Forced remote working arrangements impact sales, employee productivity and information security, due to technology and operational related challenges. Market dislocations also impact the availability of assumptions and estimates for accurate reserve calculations.
  • Regulatory Changes: IFRS-17 that is set to go-live in the beginning of 2023, substantial government regulations through PRA/FCA also tend to disrupt the smooth functioning of actuarial organisations.

Impact on the “business-as-usual” actuarial reporting process

As exposure to the risks listed above increases, the current, “business-as-usual” actuarial process must be upgraded to account for new scenarios, manual adjustments, additional sensitivity runs, deep dive analyses, controls and audits.

The “business as usual” actuarial process can be largely classified into five stages: input data consolidation, model preparation, model runs, post valuation processing and reporting.

Actuarial has always been a highly interlinked system which requires various stakeholders and systems to work in perfect harmony. Respectively, these include data partners, finance, corporate controllers and chief actuaries, working with databases, cash flow models and various reporting tools. Any disruption to the process reduces the time available for the business to analyze the data and make effective business decisions.

  • Input Data Consolidation: For each production cycle run, input data is received from different sources: policy administration systems, finance team, corporate assumptions team and ledger. The data includes information related to contracts (inforce and new business), premiums, transactions and fund values. COVID-19 impacts the timing of input arrival from different sources, either due to additional checks and controls or due to staffing constraints. It could be particularly challenging to manage any new products that are launched during this period.

    Proposed intervention: An automated data consolidation and reconciliation tool can enable reduction in turnaround time for data processing, and help eliminate the possibility of manual errors. It can act as a single source of truth that is available to all consumers. An auto-scheduler can be enabled that allows fetching data from scattered systems, formats and stakeholders at regular intervals, and run rule-based validations on top of it to ensure that the data is relevant, accurate and real-time accessible.
  • Model Data Preparation: Cash flow models require “in-force” population data, along with assumptions, scenarios and market data. Additional “worst case” scenarios must be generated to adjust for COVID-19 conditions and included in reserve calculations along with traditional deterministic or stochastic scenarios.
    • Different products have varying exposures to different assumptions. For instance, investment, expense and policyholder behavior assumptions, such as lapse/surrender and withdrawals, mortality, and morbidity assumptions impact the annuities, life and retirement products.
    • Actuarial assumptions are generated updated annually. However, due to these adverse developments, experience studies may need to be conducted or monitored more frequently. Cash flow models might have to rerun with every update to reflect the impact of the refreshed assumptions if any.
    Proposed intervention: An integrated assumptions database can store all the assumptions, scenarios and market data at a cohort / seriatim level as prescribed or determined for different reporting standards. This database can channel the model input data flow for each iteration of model run as deemed relevant. All the business units can leverage one single integrated database, which can be autorefreshed during non-peak times. This helps reduce the effort to manually reconcile the seriatim-level allocations and perform separate impact analyses.
  • Model Runs: In general, reserve cash flow models are run an average 20 to 40 times for different blocks of policies or scenarios, which may vary across organisations, business units etc. These model runs happen in batches for reserve calculations and to study the impact of various economic and actuarial assumptions, as well as scenarios that drive change in reserves quarter over quarter. Multiple sensitivity runs help adjust the reserves for shocks. With IFRS 17 requirements, and COVID-19 impact, this requires double the model runs within the same monthly cycle.

    Proposed intervention: Robotics Automation (RPA) based run inventory can enable auto model runs for different scenarios & blocks of contracts. An integrated data mart can aggregate the outputs of these multiple reserve model runs at a seriatim level. Dynamic visualisation dashboard linked with this integrated data mart can help conduct deep dive analyses to compare reserve values for different population sets, input factors and model run iterations.
  • Post Valuation Processing: As COVID-19 scenario evolves at a faster pace, depending on mortality rates and market reactions to global trends, an active post valuation processing system would be required to translate those effects into reserve adjustments. Additional topsides must be made to the reserve calculations for those policies that fall out of the reserve models.

    Proposed intervention: An efficient attribution model can help in attributing the impact due to COVID-19 conditions and regulatory changes on reserve changes. A post valuation tool can aggregate the contracts that fall out of automated model runs either due to deviation in product features or run-offs and separately calculate the reserves. This can reduce the manual effort spent on topside adjustments and maintain a historical record of adjustments to reserves as well.
  • Reporting, Filings and Disclosures: On average, each business unit generates approximately 300- 400 reports for internal analysis or disclosures, which may vary across organisations and functions. Additional requests from across the organisation, such as finance, corporate controllers, etc., will increase to ensure that reserve estimates are adequately maintained. This would help various functional groups make decisions related to capital allocation and reinvestment strategies. Additional regulatory disclosures and reports would also be required to report on operational and financial preparedness.

    Proposed intervention: A full-blown analytics and reporting dashboard can aggregate the data at seriatim level for different products, business units and legal entities. This can serve as a dynamic plug and play dashboard that provides various requestors across the organisation an ability to choose the metrics they would need for their analyses and a standardised output to generate survey reports for different regulators and rating agencies.

Impact on new developments required to support reporting

As the COVID-19 scenario unfolds, actuarial organisations will be required to make frequent updates to existing models and systems to account for emerging conditions. Systems should be enabled to react faster, remain flexible for a shorter “time-to-market” and ensure reliable testing.

The new developments required to support the production process:

  • Input Databases Update: Annuities business, in particular, are likely to revise their product portfolio to move away from market and interest sensitive. There could also be new products which provide coverage only for the COVID-19. Each new product introduced into the system will have to be coded across different database systems: contracts, inforce, premiums, transactions and fund values to be able to reflect when the sales cycle and onboarding cycle becomes active.
  • Model Data Updates:
    • There may be a need for new population samples to test new products coded into the model for the first time. Additional data sets must be prepared for population samples for testing various scenarios, model upgrades and calculation changes.
    • Actuarial assumptions are generally updated annually. Frequent assumptions updates are required to account for experience studies which will be affected by the prolonged pandemic. The number of assumptions to be developed will vary significantly based on the nature of products and exposure to policyholder behavior changes, mortality and morbidity risk.
  • Model Development and Updates: In addition to regular pricing, reserving and forecasting models already in development to account for IFRS-17 reporting requirements, regular version upgrades and model calculation updates:
    • Models must be developed or enhanced for new products and updated product features.
    • Model development cycles will take longer for both new and existing models, as they must be updated for evolving scenarios, assumptions and/or attributions.

    Proposed intervention: Agile operating model for new enhancements can help efficiently manage the developments in multiple sprints with frequent and timely feedback loops. The operating model can help modeling teams to react quickly and effectively to the evolving conditions through continuous improvements. Scalable & efficient testware / independent validation tool embedded with Artificial Intelligence (AI) based sample generator can help test the model changes with varied sample sets representative of population or entire block of population, as required.
  • Post Valuation Tool Development: Post valuation tools and proxy/challenger models will be needed to analyze the impact of policies dropped out of model runs due to new model updates.

    Proposed intervention: Automated post valuation engine can help reduce the topside adjustments to be made to the reserve output. Product feature changes such as surrender charges removal, delayed premium payments etc owing to regulatory requirements can be automatically ingested into the reserve calculation with this engine with minimal manual intervention.
  • Reporting, Filings and Disclosures: The Prudential Regulatory Authority has initiated a collaborative approach, conducting surveys across the industry by requesting updates on COVID-19-related claim requests and payments. As a result, the necessary reports, files and documents must be prepared to comply.

    Proposed intervention: Integrated multi-functional data mart and standardised reporting format specific to COVID-19 related premiums and claims can reduce the additional effort required to manage the requests from outside the organisation.

Other Operational Impact

  • Finance:
    • Credit spreads have widened making new purchases of corporate bonds possibly more attractive. Active ALM strategy to counter low interest rates and highly volatile markets requires frequent changes to the investment mix between corporate bonds and government bonds
    Proposed intervention: An automated transfer algorithm can optimise the investment mix. This algorithm-based tool can balance the trade-off between policyholder account value and business solvency by redistributing asset allocation in line with overall business goals.
  • Employee Productivity:
    • Due to remote work, employees will be operating at lower productivity, either due to technology-related challenges or reduced workforce owing to the pandemic. It has been observed that employee productivity is impacted by ~20% industry-wide at this time.
    Proposed intervention: Integrated data factory, and RPA / automation based interventions like highlighted above reduces the manual processing of the tasks and help employees focus entirely on core actuarial activities, and enable them to manage the increased workload in lesser time.

How data and technology-driven interventions can help the actuarial organisations

In conclusion, the Life & Pension actuarial function will be impacted significantly due to input data timing issues, new model developments and frequent model runs, as well as topside adjustments, deep dive analyses and reporting requirements related to disclosures and filings.

Impact highlighted above may be more or less depending on BUs, model types etc. In order to design & implement these proposed interventions, organisations can leverage an illustrative tool-kit as below (varying from low-code to extensive coding platforms case on case basis)

By implementing these process management, data, AI/ ML and automation/RPA driven interventions, either for end to end transformation or customisations unique to products and reporting stages, the actuarial function will be transformed into an automation and analytics-driven business better prepared for challenges like COVID-19 in the future.

EXL is a leader in operational management, data, analytics, and digital enhancement. We have extensive experience helping US Insurance carrier actuarial organizations navigate the challenges and benefits of scaled actuarial transformation.

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Contact us to learn more.

Kshitij Jain,
Head of Data & Analytics, UK & Europe
Phone: +44 7494 542881

Siddharth Bhatia,
VP Insurance Analytics, UK & Europe
Phone: +44 7768 092692




Rahul Nawab,
Head of Insurance, Analytics


Deepti Kalra,
L&A Transformation & Analytics Lead



Animesh Sarkar,
Sr.Manager, Analytics

Siddharth Bhatia,
VP Insurance Analytics


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