This piece is by Forbes McKenzie, the Founder and CEO of McKenzie Intelligence Services (MIS).
When insurance companies introduced fixed, assumption-based modelling around thirty years ago, the technologies available to them were limited to the quarterly or annual updates prevalent in the 90s. This meant historical input had to suffice as a basis for decisions. Nowadays, however, data streams flow continuously and customers expect well-informed, instantaneous responses to the many dangers that now shift by the second. Insurers must therefore adapt, updating former baseline models with real-time intelligence to keep pace with modern demand.
Stop freezing risk
Built around specific moments – be it five years, five months, or five minutes ago – static models assume the reality captured in the past remains valid in the present. This creates a concerning gap between what’s expected and what actually happens, effectively reducing risk assessments to mere probabilities rather than the precise insights they should be.
Often stretched way beyond their intended scope, fixed models can leave insurers painfully lagging behind real-world events. A model built one year ago might be dangerously unaware of the many new cyber-, geopolitical- and climate-based risks that have emerged since, for instance, often on a daily basis. Any assumptions made using such models are therefore grounded in errors, leaving insurers unnecessarily exposed.
Beyond the financial liabilities created, there are significant legal dangers here, too. Because real-time data is now readily available, often free of charge, to any party around the globe, those covered by insurance contracts often have access to more up-to-date information than their insurers. If an insured party is able to demonstrate that they had access to more granular, verifiable, contemporary data than their insurer before the courts, proving the insurer relied on outdated models, the latter may face serious pecuniary, legislative, and reputational charges. When disputes emerge, the party with live data almost always has the upper hand, with real-time insurance data models levelling the playing field by ensuring that everyone is working from the same evidence.
Embracing real-time intelligence
By continually incorporating data flows from telematics, IoT sensors, satellites, environmental feeds, performance logs, and digital behaviour streams into existing models, insurers can update their assessments in real time, reducing litigation risk and restoring consumer confidence in fairness. No longer working in periodic batches like before, they’re basing decisions on what’s actually happening right now, rather than what was true in the past, equipping them with much-needed timely accuracy.
The power of this shift becomes clear in the face of today’s major, fast-moving events. Take hurricanes, for example. A traditional model might suggest that a Category-5 storm will cause damage across a specific region. However, if the storm’s path shifts abruptly –as is often the case – or its intensity changes, modelled assumptions are soon rendered useless, often in a matter of minutes.
When Hurricane Katrina struck the Gulf Coast in 2005, for example, forecasts changed dramatically in the final stretch. The storm’s trajectory shifted roughly 170 miles west before landfall, and by the time it hit New Orleans, flooding from storm surges and levee failure took over as major concerns rather than maximum wind speeds as predicted. Insurers that based loss estimates purely on early-stage models in this context would have sorely misjudged true outcomes by a wide margin.
Real-time intelligence prevents that from happening. By pulling in live meteorological and geospatial feeds, insurers can adjust exposures, mobilise response teams, and calculate settlements in accordance with actual events – not just projections.
Practical benefits
In this sense, real-time intelligence does more than just correct forecasts, completely transforming insurers’ day-to-day operations and outcomes.
Underwriting, quotation, claims validation, and fraud detection teams can respond to developments almost instantaneously when real-time data is fed into static models and analysed momentarily using AI, drastically reducing both latency and overheads. Artificial intelligence can process millions of claims in just one afternoon, for instance, identifying anomalies and contexts that merit human attention. This not only improves efficiency but also makes the work itself more engaging, freeing human experts to focus on strategy, judgement, and client relationships rather than repetitive tasks.
Compliance is furthermore strengthened when companies have a consistent, panoramic view of their risks and clients thanks to the global availability of real-time intelligence data. Modern insurance systems can integrate consent management, anonymisation, and access controls directly into their structure. This means that GDPR and similar data-protection regulations, solvency demands, and ESG reporting rules can be met dynamically rather than retroactively – with insurers no longer relying on static data dumps or outdated permissions.
Personalisation likewise improves. With live input layered into models, pricing and coverage reflect each policyholder’s real behaviours and exposure rather than being based on statistical averages. Risk is priced more fairly as a result, meaning fewer customers feel overcharged or misunderstood. The resulting accuracy helps rebuild trust in an industry that has, at times, seemed opaque, potentially driving such low global policy uptake levels.
Closing the insurance gap
Despite decades of industry growth, large portions of the world’s population remain uninsured – with approximately 60% lacking access to basic protection across health, property, and agriculture, according to the World Bank.
Real-time intelligence helps close this gap by improving accuracy, automation, and personalisation as described above, with the savings derived from this allowing insurers to offer more affordable, modular and even micro-policies to previously underserved markets. Premiums can be adjusted dynamically, reflecting true risk rather than conservative estimates inflated by uncertainty, which ultimately means that more people and organisations can afford protection. Likewise, a greater number of communities can recover quickly from disaster rather than falling into poverty, thanks to the improved accuracy and speed of emergency response and issuance of appropriate payouts made possible by the incorporation of real-time intelligence and application of AI analytics and processing to existing models.
In other words, the benefits of updated insurance intelligence are collective as well as individual, with fewer uninsured losses, stronger social resilience, and less strain on official safety nets when crisis hits. This restores faith and generates further business for insurers.
Consistency and caution
Of course, immense opportunity comes with the need for appropriate discipline. If every team processes real-time intelligence differently, results will soon begin to contradict one another. Consistency across the board must therefore be established. Company-wide analytics frameworks help to ensure that all underwriters, actuaries, and claims handlers see and interpret the same intelligence in the same way. Insurers also need systems capable of producing uniform outputs across the various platforms and business units under their umbrella to prevent data-driven decisions from becoming fragmented. This is what makes operations truly scalable, auditable, and compliant, preventing innovation from descending into chaos. Real-time data doesn’t remove the need for governance but makes it more essential.
Decisions at the speed of risk
Thirty years ago, static models revolutionised insurance. Today, real-time intelligence is set to do the same again. This is not to say static frameworks have completely had their day. Whilst they may struggle in a world where the truth can change momentarily, they still serve a purpose in long-term pricing and programme design, providing actuaries and underwriters with the stable basis they need.
Rather than dictating outcomes, however, their role must be limited to foundations upon which new, live data continuously layers, refining assumptions such that decisions – particularly at the point of claim or renewal – are based on what has actually happened, rather than expired forecasts. Static models still have a place as a baseline but must be continuously finessed.
Ultimately, the addition of real-time intelligence allows insurers to deliver accuracy, agility, and accountability – provided it’s used responsibly and consistently. Insurers that make the leap now will safeguard profits and help close the world’s protection gap, strengthening resilience whilst redefining what it means to manage risk in an age where change never stops.
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