1. Compliance-led Capital Efficiency

This is a powerful lever to fine-tune capital reserves and unlock funds for re-investment while staying aligned with evolving regulatory frameworks. The EU’s Solvency II framework, for instance, introduces two capital thresholds – the Solvency Capital Requirement (SCR) and the Minimum Capital Requirement (MCR) to ensure timely intervention by supervisory authorities and support reinsurance as a risk mitigation instrument. By adopting internal models along with scenario-based assessments, insurers can better capture their risk exposure and ensure accurate capital allocations.

Take the example of a multinational insurance and asset management company that adopted an internal model under Solvency II. The result was an accurate reflection of its risk profile and over GBP 1.2 Bn freed for re-investment. Besides, insurers are using smart matching of assets and liabilities to balance risk, ensure liquidity and minimize capital buffers.³

Another focus area is the alignment of reporting systems with IFRS-17 to assess profitability and avoid over-capitalization. Leading firms are embedding IFRS-17-compliant models to distinguish between Contractual Service Margin (CSM) and loss components, enhancing transparency and reducing excessive capital holdbacks.

Additionally, structured re-insurance mechanisms, such as quota share and stop-loss arrangements, continue to allow insurers to cede a fixed percentage of premiums and losses to a reinsurer, smoothing the volatility in earnings and freeing up capital. A global insurance company implemented quota share re-insurance for its French P&C business, leading to a capital release of €1 Bn.⁴

2. Data-led Allocation

How insurers deploy capital is transforming, from static budget planning to agile, intelligence-driven strategies that prioritize risk-adjusted returns. Artificial Intelligence (AI) and Machine Learning (ML) powered pricing models enable data-driven pricing based on real-time factors such as market conditions, customer behavior and capital costs.

An Illinois-headquartered insurance company, for instance, integrated ML into its pricing and claims models, resulting in a 3.5-point improvement in its combined ratio and more efficient capital allocation to higher-margin segments.⁵

With Generative AI (Gen AI), CFOs now have access to synthetic data to improve demand forecasting, extend dataset diversity and rapidly adapt to market shifts. A joint MIT and BCG Henderson Institute survey showed the impact of deploying AI for pricing transformation. It found that companies with over USD 10 Bn in annual revenue that implemented AI-driven pricing transformations saw more than USD 100 Mn in revenue improvement – 70 percent more than their peers.⁶

Moreover, dynamic portfolio steering is empowering insurers to channel capital into the most productive areas of the business. A leading Swiss insurance company’s Group Risk Appetite Framework, for example, enables the company to continuously assess return-on-capital across business lines and reallocate capital accordingly, driving a five percent uplift in Return on Equity (ROE) over three years.⁷

Meanwhile, advanced actuarial models and AI-driven analytics are revolutionizing reserve optimization. Predictive analytics provides insurers with unprecedented foresight – enabling more precise claims reserving, reducing redundancies and unlocking capital for strategic use. A leading provider of specialty lines insurance and re-insurance, for example, released USD 200 Mn in excess reserves using predictive analytics to refine its reserve strategy.⁸

3. Transformation-led Re-allocation

This focuses on freeing up capital through digitization, automation and modernization, turning operational efficiencies into strategic financial gains. AI-powered underwriting uses ML to analyze vast datasets, improving risk assessment and accelerating decision-making. Unlike traditional underwriting, AI can ingest sources like e-mails, claims history and even social media to uncover patterns invisible to human analysts. Besides more precise, personalized risk profiles, it enables insurers to tailor policies while improving loss ratios and reducing capital strain. AI-enabled underwriting can reduce processing time by up to 70 percent while cutting operational costs by 40 percent.⁹

Legacy system modernization is another high-impact lever. Outdated platforms cost organizations USD 2.41 Tn a year.¹⁰ Besides, vulnerabilities in legacy systems contribute to rising risks, with the average cost of a data breach now reaching USD 4.9 Mn.¹¹ Hence, the decision to modernize is one of strategic necessity.

A financial services company serving markets across the globe offers a compelling example: By outsourcing the administration of closed books and monetizing in-force blocks, the firm improved return on equity and freed up capital for next-generation product innovation.¹²

Ultimately, transformation-led reallocation allows insurers to re-direct trapped or underutilized capital into high-impact areas, fueling innovation and positioning their organization for sustained growth and success in an increasingly digital world.

4. Operations-led Efficiency

These initiatives are re-shaping core insurance functions, driving agility across the enterprise and delivering measurable financial impact. Zero-based budgeting and shared service models are helping insurers take control of operating expenses. For instance, a multinational finance and insurance corporation centralized its finance and claims operations, unlocking USD 500 Mn in tied-up capital and reducing its cost-to-income ratio by eight percent.¹³

AI-led tools are delivering operational gains while addressing the rising incidence of fraud. In the US alone, insurers lose at least USD 308 Bn every year, with about 10 percent of property-casualty insurance losses due to insurance fraud.¹⁴ Intelligent claim systems can identify subtle fraud patterns that humans miss, while constantly learning from updated data to improve detection accuracy. Moreover, AI is transforming First Notice of Loss (FNOL) processes, automating submissions, tracking progress and managing customer inquiries for faster and accurate claims handling

Another important area is working capital management, which ensures companies have sufficient cash and equivalents to meet short-term obligations, while optimizing current assets to fund business growth. The ideal ratio is between 1.2 and 2.0, striking a balance between adequate cash flow and efficient use of assets.¹⁵ For example, a UK-based insurer digitized its collections and reconciliation processes, which not only improved capital turnover but also reduced uncollected premiums by 18 percent.¹⁶