Abgalis.com - 2026 Pricing, Features, Reviews & Alternatives

Abgalis is an enterprise risk management and climate risk intelligence platform designed to transform how organisations identify, quantify, and govern risk across seven domains. It has been developed by risk and capital modelling practitioners and is powered by artificial intelligence grounded in scientific methodologies. The platform acts as a dynamic intelligence layer connecting risk science with capital strategy and board level decision making. Abgalis serves Chief Risk Officers, Chief Actuaries, Chief Executive Officers, Chief Financial Officers, and Non Executive Directors at Lloyds syndicates, London Market insurers, United States and Canadian carriers, Middle Eastern and Asian insurers, global reinsurers, and managing general agents. It operates within major insurance regulatory regimes including Solvency II in Europe, NAIC risk based capital in the United States, OSFI LICAT in Canada, DFSA and CBUAE in the Middle East, MAS, APRA, and IRDAI in Asia Pacific, and supports IFRS seventeen compliance globally.

The platform delivers comprehensive intelligence through seven integrated domains covering market, credit, liquidity, insurance, operational, climate and ESG, and strategic and emerging risks. At its core Abgalis builds a living digital twin of an organisation’s entire risk ecosystem that continuously models risk in real time rather than maintaining a static register or periodic report. The digital twin ingests data across all seven domains and applies stochastic modelling and machine learning to reveal invisible risk propagation patterns such as cascade effects, feedback loops, and amplification dynamics that isolated approaches often overlook. The system performs thousands of simulations every minute to stress test risk portfolios and simulates the impact of emerging risks on insurance entities at the moment of emergence.

For climate risk the platform integrates scenarios from the Network for Greening the Financial System, conducts stochastic climate modelling, and applies advanced machine learning predictive analytics to model both physical risk and transition risk across all asset classes. Abgalis is purpose built for Solvency II compliance and Own Risk and Solvency Assessment preparation with automated generation of full quantitative reporting templates in audit ready format, support for PRA SS five slash two five expectations, and alignment with Lloyds RDS two thousand twenty five standards. Real time regulatory monitoring maintains compliance across all domains. A war gaming capability enables structured exercises against the Solvency II balance sheet in which teams role play competitors, regulators, and market forces while the platform models strategic moves against the solvency capital requirement, technical provisions, and own funds in real time with outcomes integrating into the Own Risk and Solvency Assessment and risk registers. Live Emerging Risk Intelligence continuously ingests global event data in more than one hundred languages, climate APIs, supply chain monitors, and cyber threat feeds to translate raw signals into quantified impacts on Solvency II balance sheets.

Abgalis is delivered as a cloud based solution on Microsoft Azure infrastructure with security measures aligned to ISO twenty seven thousand one and SOC two Type Two standards, including encryption in transit with TLS one point three and encryption at rest with AES two hundred fifty six. The platform integrates with third party data providers for climate scenarios, emerging risk intelligence, and market data feeds under a data governance framework that addresses data quality, relevance, representativeness, and bias assessment. For organisations subject to the Digital Operational Resilience Act the solution meets ICT third party service provider requirements under Article thirty with documented service level agreements, data processing location transparency, defined recovery time objectives and recovery point objectives, regulator cooperation provisions, audit rights, and exit strategies. The AI and machine learning design incorporates human oversight to ensure outputs function as analytical tools supporting human judgement with users retaining responsibility for regulatory submissions, underwriting decisions, capital calculations, and risk management actions based on platform outputs.