Strengthen your reserving decisions with enhanced algorithmic techniques
ResQ Machine-led Reserving provides advanced, AI-led capabilities within ResQ, applying machine‑led principles and enhanced algorithmic frameworks to automate and optimise actuarial projections. It helps reserving teams improve accuracy, consistency, efficiency and confidence by systematically challenging judgement, reducing bias and evidencing performance, all within familiar ResQ workflows.
Advances in AI are creating new opportunities for reserving teams to improve efficiency, consistency and insight. From automating routine processes to identifying patterns across large and complex datasets, AI can enhance the speed and quality of reserving analyses while maintaining appropriate actuarial control. Used responsibly, AI helps reserving functions evolve from reporting-focused teams into strategic partners that provide earlier and more actionable business insights
27Trusted by 27 industry bodies from primary insurers, reinsurers to regulators
Why ResQ Machine-led Reserving?
Practical AI and machine learning implementation that strengthens actuarial judgement:
ResQ Machine-led Reserving brings data-driven insights to pricing and portfolio management.
Enhanced levels of accuracy and transparency that transform reserving
- Challenging bias and improving decision quality
Reduce the impact of anchoring, implicit prudence and subjective judgement by using machine‑led projections as an independent, unbiased reference during reserving. - Accelerate reserving cycles under time pressure
Provide group and second‑line functions with consistent, independent ultimates to prioritise review, challenge regional results and build trust in first‑line outputs. - Enable more granular and frequent reserving
Move beyond annual or high‑level projections by running reserving analysis at greater granularity and frequency, helping teams respond earlier to emerging claims trends and portfolio mix changes. - Create a single process applicable for multiple functions
Establish a common baseline for reserving, pricing, portfolio management and claims, helping technical teams reconcile differing points of view and focus expert input where it adds most value. - Drive measured innovation in reserving
Use back‑tested machine‑led results to objectively assess whether new methods, assumptions or approaches genuinely improve outcomes over well‑calibrated traditional techniques.
Transform reserving from a compliance task into a source of business value
ResQ Machine‑led Reserving is helping insurers move beyond manual, judgement‑heavy processes by embedding game-changing algorithms and machine-led analysis into the reserving function.
This new functionality enables teams to improve accuracy, strengthen governance and free-up time to focus human expertise where it matters most.
If you would like to find out more about how ResQ Machine-led Reserving can help your reserving team thrive, please get in touch with us – we’d be delighted to discuss or demo this transformative technology with you.
“By embedding machine-led optimisation into familiar actuarial workflows, ResQ helps insurers improve accuracy, efficiency, and governance, without sacrificing transparency or professional judgement.”Bastian KurthGlobal Proposition Leader, Reserving
Frequently Asked Questions
Machine‑led reserving forms another pillar within the actuarial judgement allowing analysts to utilize to get an alternative unbiased view. It automates and objectively optimises parameter selections within established actuarial methods (such as Chain Ladder, Bornhuetter‑Ferguson and Cape Cod), providing a consistent, data‑driven reference that actuaries can challenge, refine and override using professional judgement.
All outputs are fully transparent and auditable. Projections are generated using familiar actuarial methods, with clear traceability of assumptions, parameter choices and performance metrics, supporting governance, second‑line review and regulatory scrutiny.
By systematically testing multiple parameterisations, machine‑led reserving helps challenge anchoring, implicit prudence and subjective judgement. This creates a consistent baseline that improves comparability across teams, business units and valuation cycles.
Yes. Objective optimisation against measurable performance criteria can deliver more stable and reliable reserve projections than manual approaches, while automation enables greater granularity and more frequent analysis without increasing resource pressure, freeing actuaries to focus on higher‑value expert review.