Why the Next Disruption in Life Insurance Modelling Won’t Be Regulatory

For most UK life insurers, the last decade of financial modelling has been defined by regulation-driven urgency. Solvency II reshaped capital thinking. IFRS 17 then arrived and demanded industrial-scale reporting engines, new data flows, and an unprecedented level of modelling governance.

Now, with IFRS 17 bedding-in and quietly replacing IFRS 17 panic, a temporary lull has fallen over the life insurance modelling world. But this is likely to be short-lived.

However, the next major disruption in life insurance modelling probably won’t come from regulators. It’s more likely to come from economics, scale, and speed.

IFRS 17 Was a Station on the Journey, Not the Destination

IFRS 17 forced the industry to standardise. That was its great success. Outputs became comparable. Controls became stronger. Modelling teams became operationally mature in ways they simply hadn’t needed to be before. But that maturity came at a cost.

Many firms now operate:

• Multiple tightly governed production models
• High frequency runs across multiple bases
• Expensive, persistent modelling platforms
• Long lead times for even modest scenario extensions

The uncomfortable reality is that IFRS 17 industrialised reporting, not insight.
In many organisations, the modelling estate is now robust, but brittle. Capable, but slow. Accurate, but expensive to extend. That’s all very well until business requirements & questions start changing.

When Modelling Becomes an Economic Decision

Historically, modelling architecture choices were justified on actuarial grounds:

• Prudence
• Auditability
• Familiarity
• Vendor longevity

Increasingly, a new question is being asked, often quietly, often by finance rather than actuarial:

“Why does this cost so much to run?”

Compute costs, licensing models, infrastructure overhead, and specialist support requirements are no longer hidden inside programme budgets. They show up every quarter. And when they do, modelling stops being a purely technical choice and becomes an economic one. This is the point where older assumptions start to creak. Once cost per run becomes visible, so does inefficiency.

The Emerging Divide: Production Modelling vs Strategic Modelling

One of the most important shifts happening right now is rarely discussed explicitly. Firms are starting to realise they have two fundamentally different modelling needs:

1. Production Modelling

• Monthly / quarterly close
• Repeatable, locked-down processes
• Stability over flexibility
• Known questions, known answers

2. Strategic Modelling

• Reinsurance optimisation
• Capital deployment
• New business design
• Management actions
• M&A and portfolio restructuring
• “What if we changed this?”

Yet many firms still try to meet both needs using the same heavyweight engines, platforms, and governance structures. The result is that strategic questions become slow, expensive, and constrained by tooling that was never designed for rapid exploration. Or worse, they don’t get asked at all.

Key Features of Next Generation Modelling

Strip away the buzzwords, and the direction of travel is surprisingly pragmatic. Across the UK market, leading teams are converging on a few clear principles:

Ownership of Logic

Firms want to own their actuarial logic, not just configure it. That means transparent models, readable assumptions, and architectures that don’t lock insight inside opaque engines.

Elastic Compute, Not Permanent Platforms

Strategic modelling is lumpy by nature. You don’t need maximum compute all the time, but you need it quickly when decisions are being made.

Scenario Volume as a Competitive Advantage

The ability to run hundreds or thousands of scenarios isn’t a technical flex, it changes the quality of decisions. More scenarios mean better understanding of second-order effects, not just headline movements.

Time-to-Insight Over Time-to-Run

A model that finishes overnight but takes weeks to adapt is no longer good enough. Speed is measured end-to-end. This is where modelling tools quietly separate.

Technology Aligned to Actual Pain Points

Against this backdrop, platforms like Mo.net are less a “new entrant” and more an inevitable response to how modelling needs have evolved. Not because they replace everything, but because they do something very specific, very well.

Designed for Strategic Modelling Economics

Mo.net’s cost structure and deployment model align naturally with:

• Burst usage
• Scenario-heavy workloads
• Experimentation without permanent overhead

This changes behaviour. Teams run more scenarios simply because they can.

Transparent, Extensible Model Logic

Rather than treating actuarial logic as something to be hidden behind layers of tooling, Mo.net encourages ownership and visibility.

That makes it:

• Easier to explain results
• Easier to adapt models
• Easier to onboard new thinkers, not just operators

A Complement, Not a Threat

Importantly, Mo.net doesn’t position itself as a wholesale replacement for IFRS 17 production engines. It sits where many firms currently struggle:

• Capital optimisation
• Reinsurance analysis
• Forward-looking decision support

In other words, where questions are still fluid.

Future-Facing by Design

As scenario volumes increase and modelling becomes more tightly coupled to decision-making, future versions of Mo.net are well placed to lean further into:

• Parallelism and performance
• Cloud-native scaling
• Integration with broader finance and risk ecosystems

Not as a reporting engine, but as a thinking engine.

The Question Firms Will Soon Have to Answer

IFRS 17 forced firms to ask:
“Are the numbers right?”
The next phase will force a harder question:
“Are the models still fit for how we want to run the business?”
The firms that answer this question early will look different. They won’t just close faster. They’ll explore more options. They’ll test more strategies. They’ll make better decisions, more quickly.
And quietly, without much fanfare, their modelling tools will have changed to match that ambition.