By: Dr. Ahmad “Jordan” Al-Zu’Bi
Jordan Daily - His Majesty King Abdullah II’s state visit to China has created unusual momentum around investment, technology and strategic cooperation. China has described the visit as an opportunity to deepen mutually beneficial cooperation and take the strategic partnership to a new level. Jordan should use that momentum for something less visible but potentially just as consequential: regulatory learning. The risks entering modern economies are more complex, interconnected and faster-moving than the regulatory systems originally designed to govern them. Climate risk now interacts with agriculture, infrastructure and public finance; artificial intelligence with underwriting, claims, privacy and liability; electric and autonomous vehicles with batteries, software, repairability and product liability; longevity with healthcare, pensions and social-security obligations. This is the New Era of Risk. The challenge is no longer simply whether regulators have enough rules. It is whether they have enough knowledge, data and institutional flexibility to understand risk before the risk itself changes.
Economic Modernization Without Regulatory Modernization Is Incomplete.
The economics of insurance regulation begin with a dual public-policy mandate. Regulators must protect policyholders and market integrity while preserving financially sound insurers capable of underwriting risk, innovating and paying claims. Too little oversight undermines trust; too much or poorly designed oversight can suppress competition, distort prices and discourage innovation. Good regulation therefore cannot simply be stricter. It must be modern, flexible, resilient and data-driven, changing as information, technology and risk change.
China offers Jordan an unusually valuable laboratory. Its National Financial Regulatory Administration (NFRA) increasingly differentiates supervisory intensity according to risk, while its approaches to motor insurance, agricultural insurance and artificial intelligence rely on risk classification, experience data and continuing recalibration. The underlying economic principle matters more than any individual Chinese rule: regulatory resources should follow risk. Institutions, technologies and insurance products that create different expected harms should not automatically receive identical supervisory treatment.
Innovation And Policyholder Protection Must Move Together.
China’s June 2026 framework for artificial intelligence in banking and insurance shows what this balance can look like. NFRA requires institutions to classify AI applications according to risk, govern models across their life cycle, control data and cybersecurity risk, monitor performance and remain responsible for their deployment. The philosophy is neither technological laissez-faire nor regulatory paralysis.
Jordan should adopt the same economic logic. A chatbot, fraud-detection algorithm and model determining underwriting eligibility or claims outcomes do not create equivalent potential harm. High-impact applications require stronger model validation, fairness testing, explainability, human review and testing for proxy discrimination, because an algorithm can omit a protected characteristic while reproducing its effects through correlated variables.
That requires regulatory talent as much as regulatory authority. Modern insurance supervision cannot be staffed only by lawyers and compliance specialists; it needs the brains and technical muscle of trained insurance economists, actuaries and data scientists. These specialists understand selection, incentives, risk classification, model validation and the economic consequences of intervention. AI regulation is partly legal, but fundamentally it is also an insurance and risk-economics problem.
Beijing provides a useful metaphor. A remarkable share of daily life runs through QR codes, integrated payments and super-apps. Beijing shows what happens when data, payment and service become almost frictionless: markets reorganize around the information infrastructure underneath them. Insurance is moving in the same direction, and regulation must understand not only the product, but the data and algorithms underneath it.
Fairness Does Not Mean Sameness.
Insurance economics starts with risk classification: heterogeneous risks generate heterogeneous expected losses. A policyholder with higher expected claim frequency or severity should, all else equal, pay more than a lower-risk policyholder. An insurance premium should therefore be actuarially sound and aligned with the underlying risk profile.
Jordan’s compulsory motor tariff recognizes risk differences, but only coarsely. Current Central Bank of Jordan instructions establish administratively determined premiums, including a 15% discount where no traffic violation is recorded and a JD12 surcharge once for qualifying passenger vehicles where one or more violations occur. A driver with one violation and another with several can therefore face the same surcharge, while mileage, claims history, severity, nighttime exposure, route risk and actual driving behavior remain largely outside the tariff.
This is rate compression, and excessive rate compression is a clear violation of risk-based pricing.
Its distributional consequence is cross-subsidization. When prices cannot reflect heterogeneous expected costs, lower-risk policyholders can pay more relative to their risk while higher-risk policyholders pay less. In economic terms, the safer driver finances part of the loss cost of the riskier driver.
Jordan can do better without abandoning accessibility or regulatory oversight. Risk profiles can incorporate claims history, violations, mileage, vehicle characteristics and validated behavioral variables; where several individuals regularly drive a vehicle, their risk profiles can be incorporated rather than treating the vehicle itself as the complete exposure.
China has moved commercial motor insurance toward greater risk differentiation, recalibrating pure-risk premiums, widening insurer pricing discretion, using longer claims histories and encouraging mileage-based UBI. Chinese InsurTech systems are also incorporating speeding, hard braking, nighttime driving, sharp cornering and other behavioral indicators into dynamic risk assessment.
The Regulator’s Job Is Not To Price Insurance.
The regulator should not become the market’s actuary. Insurers should underwrite and price risk because portfolios, loss experience, expenses, claims management and capital differ across companies; a market-wide actuarial average cannot fully substitute for company-level risk information.
Jordan’s compulsory system adds another distortion through the Unified Office’s rotation mechanism, under which compulsory policies issued through licensing offices are distributed among insurers according to turn rather than ordinary competition for individual risks. That may solve administrative problems, but it weakens the connection between risk selection, underwriting, pricing and competition.
Compulsory third-party liability insurance can remain strongly regulated without having its price administratively set. Insurers can develop actuarially sound rates while the regulator reviews their adequacy and legality. If strict oversight is desired, Jordan can use prior approval, requiring each insurer to file its rates and receive approval before using them.
The central distinction is simple and important: regulating insurance prices is not the same as setting insurance prices.
My Data Don’t Lie, Just Like Shakira’s Hips.
The line is playful; the economics are not.
Without credible data, a regulator cannot know whether a price differential reflects risk or unfair discrimination, whether claim severity is accelerating, whether an AI model has deteriorated, whether catastrophe exposure is accumulating, or whether an apparently affordable tariff is producing hidden cross-subsidization. Modern insurance regulation therefore begins with an uncomfortable discipline: test the regulatory story against the loss data, the behavioral response and the spillover effects.
China’s agricultural-insurance rules demonstrate this clearly. Rates must satisfy non-life actuarial principles, incorporate pure-risk loss costs, reflect the underlying exposure and operating costs, undergo premium-adequacy testing and be corrected when assumptions diverge from actual experience.
The Economics Of Political Jokes.
Not every early-warning signal appears first in a supervisory dashboard. When a reform or public institution repeatedly becomes the subject of jokes and satire, I would treat that as a soft risk indicator: not evidence that the policy is wrong, but evidence that trust, understanding or perceived fairness may be deteriorating. Regulators should not regulate by joke count; they should ask what the jokes reveal about distributional effects, communication failures and institutional trust. I call this the economics of political jokes, a subject deserving a separate discussion.
Price Agricultural Risk Before Government Subsidizes It.
Government may legitimately subsidize farmers because food security, rural resilience and climate adaptation create benefits beyond the individual policyholder. But inclusion should be built around the risk price, not by destroying it. Measure the actuarially sound cost first; then decide transparently how much society wishes to subsidize and for whom.
That is also the foundation of insurance inclusion. Financial inclusion asks whether people can access financial services; insurance inclusion asks whether farmers, low-income households, small businesses and vulnerable groups can access meaningful risk protection. Agricultural insurance can therefore become a laboratory for broader inclusive-insurance programs, including appropriately designed index and parametric insurance where conventional indemnity coverage is costly or difficult to administer.
Climate Risk Cannot Be Regulated Using Yesterday’s Loss Distribution.
Climate change makes historical averages increasingly dangerous if treated as immutable probabilities. China’s insurance industry has been investing in catastrophe science, including the PICC China Earthquake Model, released in November 2024. The catastrophe-modeling architecture itself is instructive: hazard, exposure and vulnerability are analyzed before being recombined into estimates of loss.
Jordan does not need Chinese catastrophe parameters; it needs the Chinese discipline of investing in risk science. Jordanian catastrophe and agricultural models should reflect Jordanian water scarcity, climate, buildings, crops and geographic concentrations. AI can accelerate this work, but it cannot replace physics, engineering, meteorology or actuarial science. The next frontier is AI for science, not AI instead of science.
Longevity Risk Is When Demography Enters The Balance Sheet.
The same logic applies to Jordan’s Social Security Corporation. Living longer is good news for people; it can be expensive news for an institution promising lifetime benefits. As benefit duration increases and retirees grow relative to contributors, the familiar demographic pyramid narrows and long-duration liabilities place increasing pressure on contribution income and investment returns.
Longevity risk is therefore not a complaint about ageing. It is an asset-liability-management problem. Social insurance is an institution built on intergenerational trust. If retirement ages, contribution rates, benefit formulas or investment strategies eventually require adjustment, contributors need to understand why, how their money is managed and how burdens are distributed across generations. Without transparency and trust, even actuarially necessary reform can become politically toxic.
This is another area where China-Jordan cooperation could extend into pensions, health financing, actuarial modeling and long-duration risk.
Every Regulation Has A Shadow Price.
When the Central Bank of Jordan, its insurance supervisory function, or the Social Security Corporation considers a material reform, analysis should extend beyond legal authority. Policymakers should ask three questions: What are the economic implications? What are the public-policy implications? What are the spillover effects?
A price restriction may improve affordability while changing selection and cross-subsidization. A claims reform may protect claimants while changing settlement and litigation behavior. An AI restriction may reduce one form of consumer harm while removing technologies that improve fraud detection or underwriting. Regulation changes behavior, and behavioral responses change the ultimate cost of regulation.
This is why regulation should learn continuously, just as risk does. More observations generate better regulatory models; better models generate better interventions; interventions change behavior and create new data; the regulator recalibrates. Static regulation applied to dynamic risk eventually becomes misregulation.
Jordan Should Import Knowledge, Not Rules.
China and Jordan differ in scale, legal systems, institutions and culture. The opportunity is not to transplant Chinese rules, but to exchange and adapt knowledge: risk-sensitive supervision, AI governance, behavioral pricing, agricultural actuarial discipline, catastrophe science, data infrastructure and continuous regulatory evaluation.
His Majesty’s visit should therefore lead to a China-Jordan Insurance and Regulatory Risk Dialogue between the Central Bank of Jordan and China’s NFRA, supported by the Insurance Association of China, insurers, reinsurers, universities, technology companies and Jordan’s Social Security Corporation. Three initial workstreams could make it operational: risk-based motor pricing, telematics and modern underwriting; AI, agriculture, catastrophe and longevity-risk governance; and a Risk and Insurance Literacy Initiative combined with insurance inclusion.
Risk literacy is especially important because policymakers cannot regulate a risk they do not conceptually understand. Senior officials should understand the difference between reducing and transferring risk, affordability and actuarial soundness, deliberate redistribution and accidental cross-subsidization, and a regulation’s intended effect and its equilibrium effect.
Jordan has the talent to build this capability, while China has accumulated extensive experience regulating one of the world’s largest and fastest-changing insurance markets. The opportunity is reciprocal: not simply Chinese capital entering Jordan, but Chinese and Jordanian institutions learning how to govern uncertainty together.
In the New Era of Risk, the strongest regulator will not be the regulator that writes the most rules. It will be the regulator that understands risk faster than risk changes.
That is the regulatory advantage Jordan should now build with China.
