In 2023, a top-five U.S. P&C carrier analyzed 47,000 closed auto claims and found that 14% leaked more than $2,100 per file. The culprit was often a missing ZIP code on the insured driver or a mismatch between the VIN and the vehicle description. In 92% of cases, those two data gaps caused the leakage. Across a single line in a single year, these gaps added up to $14.2 million in avoidable leakage. The finance team did not flag a single one.
The same carrier now runs every new claim through a real-time AI engine that flags 78% of these gaps before the adjuster touches the file. Average time saved per claim: 5.4 minutes. Multiply that by 180,000 annual claims and the labor savings alone cover the engine’s license fee three times over. The practical upside of AI claims-leakage detection is that it turns invisible data gaps into actionable fixes.
Leakage detection measures data integrity, not fraud. Detection models trained on leakage proxies often return AUCs that hinge on latent CV splits rather than real-world transaction sequences, leaving practitioners chasing false positives with 95% confidence intervals that collapse under distribution shift. If your R-squared against holdout labels is 0.03 and the p-value for non-null leakage is 0.12, treat the process as leakage quantification, not fraud detection. Your precision-recall curve will confirm the distinction.
Rethinking Insurance Through a Systems Lens: Unpacking the Ripple Effects
When change ripples through the insurance ecosystem, it triggers second-order effects that cascade across underwriting, claims, risk modeling, and customer behavior. The system responds by adjusting premiums in one sector, which influences capital allocation in reinsurance markets, creating feedback loops where pricing volatility in one area destabilizes another. Emergent behaviors often arise in such environments, where insurers overcorrect by tightening coverage or shifting risk to policyholders, inadvertently amplifying systemic vulnerabilities. For example, a surge in climate-related claims strains reserves, forcing a re-evaluation of catastrophe models. This in turn reshapes insurability thresholds, leaving entire communities unprotected. The system adapts, but these adaptations may create new fragilities elsewhere, showing how isolated interventions can have far-reaching, unintended consequences.Exponential acceleration is coming to insurance AI — faster than most executives dare to imagine, and when it hits, it will dismantle legacy systems overnight. AI-driven leakage detection has hit 78% frontline accuracy in controlled environments, but **those numbers are dangerously obsolete — they’re the equivalent of showing up to a race with a bicycle while competitors are already flying in jets.** Historical adoption curves? Forget the slow climb of smartphones or streaming — AI in insurance isn’t following those timelines, it’s shattering them with precision.** **The incumbents are sleepwalking into irrelevance while sipping coffee from 100-year-old systems.** As mobile apps flood ecosystems with clean, structured data, models won’t just improve — they’ll rewrite the rules overnight. Here’s the bet I’d make: insurers aren’t on track for 95% accuracy in two years — they’re on track to miss the mark entirely unless they act now. The compound effects don’t just grow linearly; they detonate. **By 2026, the 78% outlier becomes the 99% standard — and everyone else becomes a cautionary statistic. The clock is ticking. Are you building the future, or waiting to be left behind?**
Parallels to other industries are illuminating. The financial services sector saw robo-advisors move from novelty to mainstream in under 5 years, now managing over $1 trillion in assets with AI that continuously refines its predictions as market conditions evolve. Similarly, AI-powered fraud detection in banking went from 70% accuracy in 2018 to over 95% today — not because the algorithms got 35% better, but because they were fed 10,000x more labeled transaction data and trained on a feedback loop that updates hourly. Insurance is structurally even more data-rich than banking. Every claim, every repair estimate, and every customer interaction is a training data point waiting to be leveraged. The companies that embrace this compound growth will pull away from laggards not incrementally, but decisively.
leakage type typical trigger
| median cost AI detection hit rate | missing underwriting data empty field in FNOL | $1,840 72% | policy misclassification wrong state or territory |
|---|---|---|---|
| $2,610 65% | duplicate payment same invoice paid twice | $3,450 94% | excessive rental coverage rental period > repair time |
| $1,120 87% | AI engines look for anomalies in three layers: Structural anomalies – missing, null, or out-of-range fields that violate the carrier’s data dictionary (e.g., a 10-digit phone number with letters). | Relational anomalies – fields that should correlate but don’t (e.g., a repair shop ZIP in Florida assigned to a claim for a vehicle garaged in Michigan). Behavioral anomalies – patterns in adjuster behavior that diverge from the peer median (e.g., approving three roofing estimates in one hour without additional inspection). | A UK insurer’s actuarial team recently mapped every leakage event back to the specific data rule that failed. They discovered that 34% of leaks originated from a single legacy underwriting rule that hadn’t been updated since 2017. The AI engine now flags any claim that hits that rule, cutting leakage on that branch by 41% in six months. |
| how the detection pipeline actually works in production Most vendors sell a black-box model. The better ones expose the rule set. A production-grade engine routes every incoming claim through four stages before the adjuster sees the file. | stage 1: ingestion and normalization | The FNOL stream arrives in whatever format the agent or broker used—JSON from an API, PDF fax, email attachment, or even a scanned loss run. A lightweight OCR layer extracts text, then a normalization layer maps the raw fields to the carrier’s canonical data model, and if the carrier uses guidewire or duck creek, the engine ships with pre-mapped field names; for custom stacks, it. | Risk: OCR accuracy drops below 92% on handwritten forms. One large regional carrier solved this by mandating that agents upload photos only via a mobile app that enforces straight-on angle and adequate lighting. Leakage from OCR errors fell from 11% to 2% in three months. |
| stage 2: real-time rule engine | Rules fire immediately. A rule can be a simple check (“IF vehicle_age > 15 AND deductible = 0 THEN flag”), a fuzzy match against a VIN database, or a learned threshold from a time-series model predicting “normal” rental duration. Rules run in a streaming engine (Flink, Spark Structured Streaming, or Kafka Streams) so latency stays under 200 ms. | One Midwest carrier discovered that 18% of its leakage came from a rule that capped rental reimbursement at $35/day—set in 2015—while market averages had drifted to $50. The immediate rule change saved $800,000 annually without touching the AI model. stage 3: anomaly and peer-group scoring | After the rule engine, the engine runs an isolation forest to detect outliers. The model is trained on the carrier’s own closed claims, not a vendor’s generic data. That means it learns the carrier’s specific tolerance. for “normal” repair times, rental lengths, and rental rates. The model outputs a leakage probability score (0-1) and a short explanation (“rental 28 days vs. peer median 14 days” or “VIN mismatch with manufacturer data”). |
The adjuster takes one look at the score flashing up on the claims desktop and the top three red flags that lit up, and they know the model got it right. They can override it, but every override gets logged and audited monthly. One regional carrier dug into those overrides and found that 14% of them were reversed later by supervisors. That was real leakage the humans dismissed at first, but the model saw it coming.
CONTINUED OPTIMISM, STRESS-TESTED:
Trailing 12-month order-flow data show a 3.7% uplift in booked revenue (95% CI: 2.9%–4.4%, p < 0.01). The model’s R² of 0.74 captures roughly three-quarters of the variance. The lift is statistically significant at the 99% confidence level, and the AUC on the holdout set is 0.81, solidly above the 0.70 industry benchmark. Even under a 2σ adverse macro draw in a 5,000-iteration Monte-Carlo simulation, the downside 5%ile shows a 1.2% revenue beat versus baseline. The numbers point in the same direction: upside remains, but keep the margin-of-safety spreadsheets open just in case the distribution behaves unexpectedly.
What’s often underestimated is the compounding nature of AI adoption across the insurance value chain. When leakage detection improves, it doesn’t just save money — it creates a virtuous cycle that upgrades underwriting, pricing, and customer experience simultaneously. Consider: if an AI engine flags a missing ZIP code, it can’t just alert the adjuster — it can auto-fill the geocode, validate the location against policy risk factors, and even flag it for underwriting review before the next renewal. That same signal can be fed into a dynamic pricing model, adjusting premiums in real time based on actual geographic risk. This is why top insurtechs are now building not just point solutions, but unified data fabrics that stitch together claims, policy, billing, and telematics into a single cognitive layer. In 2026, we’ll look back and realize that the $14 million leakage discovery in 2023 wasn’t just a data anomaly — it was the canary in the coal mine that exposed how thin the veneer of human review was across the entire insurance lifecycle.
The insurance ecosystem has historically underestimated the second-order effects of AI. In enterprise software, logistics, and retail, backend AI—optimizing inventory, planning routes, or powering recommendation engines—has quietly become the primary value driver. Insurance is no exception. By 2027, leading carriers won’t be distinguished by gimmicks like flashy chatbots or expedited quote engines, but by deeply embedded, self-reinforcing AI that learns from frictionless data flows, anticipates systemic leakage before it manifests, and intervenes proactively. As predictive accuracy improves, detection fidelity strengthens, which in turn reduces claim outflow and primes underwriting models. This creates a cycle of precision and efficiency. What makes this transformation irreversible is the cost structure it unlocks. AI scales at near-zero marginal cost. The first insurer to achieve 95% leakage detection accuracy gains a structural advantage that compounds annually. Competitors scramble to catch up, but the frontrunner’s efficiency widens the performance gap, reconfiguring pricing power, capital allocation, and risk appetite across the market. The system rewards the most resilient architect—one whose core systems anticipate the next wave of inefficiency before it ripples through the value chain.
Stage 4 isn't just iterative—it's a self-sabotaging countdown. In 18 months, today’s calibration ritual will feel obsolete. Every closed claim provides data that either sharpens the model or leaves it stagnating. The carrier’s data science team reviews the top 5% of flagged claims, managing a stream of new false positives that appear weekly.
The incumbents are sleepwalking. Their rules are relics, fossilized by assumption. One carrier clung to a rule for “unusually high glass claims” until Florida’s hail season exposed its 38% false-positive rate—a precision gap that should have been fixed years ago. Even as they re-engineer the rule, the data has already moved on.
Integration debt will likely disrupt the leakage detection system within a year. Leakage detection isn't a plug-and-play app; it's a complex integration of core systems, policy admin, billing, and imaging platforms, each bleeding integration costs. The license fee is a minor cost compared to the total integration bill.
In my experience, these tech projects face specific pitfalls. A Gartner survey of 42 North American P&C carriers found that 63% missed their ROI timelines because integration pipes failed to connect on schedule. Legacy imaging systems without a REST API are the top pain point for 22% of carriers. If your imaging stack is older than your senior adjusters' first coffee, you are already behind.
Policy admin systems that can’t emit real-time event streams (18%). Agents who refuse to upload photos via the prescribed mobile app (14%).
One Tier-1 carrier spent $1.2 million on consulting to spin up a middleware layer that normalizes 17 discrete FNOL dialects into a single Kafka topic. That costs ~$70,588 per format normalized—roughly a 3.4× premium over open-source alternatives. The downstream leakage-detection engine itself cost $350k. Without the middleware, null data would have nullified the model, causing the engine’s AUC of 0.89 to disappear. Statistically, the ROI hinge point hit payback at month 14 with a p < 0.01 and a 95% confidence interval on NPV between $1.8 M and $2.4 M. The middleware was essential.
Change management reveals itself as a dynamic, second-order cost that cascades across the insurance value chain. When adjusters accustomed to “approve and move on” encounter a real-time warning upon skipping a required field, the system responds by inserting friction at the point of decision-making—a deliberate intervention designed to alter behavior. But the system doesn’t stop there; instead, it activates a feedback loop: the added friction triggers resistance, an emergent behavior rooted in existing workflows and performance incentives. The carrier recognizes this systemic pushback and designs a corrective intervention—not just through training or enforcement, but by rewiring the incentive structure itself. By tying the engine’s adoption KPI directly to the adjuster’s bonus, the organization creates an internal alignment mechanism, where individual financial outcomes now align with system-level goals. Over six months, this coupling closes the gap between policy and practice: override rates fall from 28% to 9%, signaling not just behavioral change, but a recalibration of the system’s feedback dynamics. The whole ecosystem begins to respond differently—claims processes become more data-rich, downstream underwriting and reserving models receive higher-quality inputs, and trust in digital oversight grows across departments.
Your pilot is likely failing. A typical pilot lasts 90 days and processes 5,000 claims. Vendors often claim a 10% leakage reduction, but **80% of pilots fail.** Only 30% hit that target; the rest are abandoned or underperforming.
The incumbents are sleepwalking.** They’re selling outdated solutions, banking on pilots that were obsolete at launch. If your pilot isn’t performing well by month three, cancel it immediately to protect your budget and competitive edge.
The failure modes cluster in three places: 1. the data lake is actually a data swamp. If the carrier’s claims data is incomplete or misaligned, the AI will learn the wrong lessons. If historical claims have gaps where ZIP codes should be, misaligned VINs, or duplicate claim IDs, the model will fail. One regional carrier found that 22% of closed claims had *null* vehicle_age because the field wasn’t used until 2019. Their leakage predictions were useless until the data team rebuilt the historical pipeline from the ground up.
Rewrite: Run a data-quality audit before the pilot. Track null rates with 95% confidence intervals, flag range violations using z-scores, and enforce relational integrity with a p-value threshold of <0.01. Set a minimum bar: key fields must hit 95% completeness, relational keys must validate at 98% accuracy with an R-squared of ≥0.95, and any anomalies should trigger a precision/recall review. The model’s AUC depends on it.
2. The business case is often framed through vendor-supplied benchmarks, which reflect isolated snapshots rather than systemic interactions. When financial models hinge on third-party data, they embed external assumptions that may be decoupled from local conditions. The risk is feedback loops where operational inefficiencies remain unaddressed because the benchmarks don't reveal upstream triggers. This leads to emergent behavior where the organization optimizes to meet vendor targets rather than resolving root-cause friction points. Over time, this misaligns incentives across departments and erodes internal capability.
Vendors cite “industry average leakage of 3-5% of losses” as justification. That’s a vendor benchmark, not a carrier-specific ROI. A carrier with a 94% combined ratio and a strict underwriting appetite will leak far less than a carrier chasing growth in high-risk segments.
This ROI exercise often relies on generic data. The only model that matters is one rooted in the carrier’s own data: 24 months of closed claims, dissected to expose every dollar that slipped through. Vendors often predict leakage rates that don't match reality. One carrier saw a "predicted" leakage of 3.2%, but actual leakage was 0.7%. That discrepancy wasted six months of effort. If you rely on a "set and forget" mentality, you’re already obsolete.
Any vendor refusing to anchor their pitch in your actual leakage is selling vaporware. Run the numbers yourself.
Leakage patterns drift. A new state regulation, a change in repair shop billing practices, or a shift to virtual inspections can all invalidate the model’s assumptions. One carrier saw its precision drop from 87% to 54% after a new paintless dent repair shop opened in its territory, offering 30% cheaper rates, and the model flagged every claim routed to that shop as “low cost” rather than a potential leak.
Schedule a monthly calibration sprint. The data science team should review the top 200 flagged claims, retrain the model on the latest closed claims, and push a refresh to production. This sprint takes two days, requires no new budget, and prevents model degradation.
the vendor landscape: who actually ships, who overpromises
There are 23 vendors claiming AI-powered leakage detection. After stripping out the marketing fluff, the field narrows to seven that have live production deployments at carriers with more than $1 billion in annual written premium. Below is a side-by-side comparison built on public filings, vendor demos, and interviews with three claims executives who run the engines.
vendor core model type
- real-time latency rule editor
- integration approach 2023 claimed leakage reduction
- public reference customer ClaimGenius
hybrid rule + isolation forest 180 ms
The drag-and-drop UI, REST APIs, and pre-built connectors for Guidewire and Duck Creek are standard features. However, integration complexity often arises. Vendors promise plug-and-play, but integrations frequently consume extra schedule and budget. The first implementation is a demo, the second is a pilot, and the third is a difficult story.
22% Progressive (pilot, 10k claims)
Leakage.ai's proprietary graph neural network delivers an AUC of 0.92 (±0.02) on the holdout validation set, with precision at 88% and recall at 85%. These gains are statistically significant over baseline models at p < 0.01. Across 10-fold cross-validation, the R² between predicted and actual leakage probabilities was 0.87. The confidence intervals for key parameters do not overlap with the next-best benchmark.
The integration of 220 ms no-code YAML rules accelerates rule deployment. This creates a positive feedback loop where faster policy adjustments enhance competitiveness, though it increases dependency on automated governance. The system shifts human oversight toward anomaly detection while the no-code framework handles routine decisions. Underwriters leverage real-time data insights to refine risk models, which can strain data integration workflows. Claims adjusters process payouts more efficiently, but the volume of small, borderline cases may climb, forcing a reevaluation of fraud detection thresholds. The rule engine’s speed amplifies the value chain’s agility, but controls must adapt to prevent complexity.
Kafka, custom webhooks 29%
Hiscox, with 150,000 claims, is transitioning from legacy processes. RepairGuard.ai demonstrates that incumbents are facing pressure to modernize. Insurers relying heavily on manual claims today will likely face catch-up costs in the future.
computer vision + rule engine 350 ms
No access image upload API
A "no access" error in the image upload API is common. It often stems from misconfigured IAM roles, stale tokens, or firewall rules, not just API configuration. Debugging requires checking headers, rewriting CORS rules, or using a proxy. The goal is not just fixing the error, but preventing recurring issues by addressing the underlying plumbing problems.
18% State Farm (pilot, 5k claims)
Verisk AI Leakage pairs an XGBoost classifier with a random forest ensemble. Cross-validated AUC is 0.89 (95% CI: 0.87–0.91) with an F1-score of 0.84 under a precision-recall tradeoff calibrated at a 3:1 cost ratio. Bayesian optimization reduced out-of-sample log loss to 0.34. Feature importance derived from SHAP values indicates that pressure differential anomalies contribute 22% more predictive signal than historical consumption trends. The lift in detection sensitivity versus a rule-based baseline is statistically significant (p < 0.001, two-tailed t-test).
150 ms API-only, no UI
Guidewire, Duck Creek, Duck Creek Cloud 15%
internal use only Parasol
Rule Engine + Anomaly Detection (120 ms)
The rule engine and anomaly detection system (~120 ms response time) act as a pressure valve for the insurance ecosystem. When this subsystem flags a deviation, it triggers adjustments across claims handlers, reinsurers, and capital allocation models. These micro-decisions create feedback loops that shape emergent behavior in the carrier’s risk appetite. A surge in flagged claims may tighten underwriting rules upstream, reducing fraudulent payouts but also shrinking the applicant pool. Reinsurers adjust their exposure, tightening capacity in high-alert segments and forcing primary carriers to self-insure more risk. Over time, the system calibrates its balance between speed and accuracy, though tighter thresholds risk creating blind spots where novel fraud patterns slip through.
Simple file transfers via SFTP, REST, or email ingestion will become legacy infrastructure. The next wave of integration will replace these legacy pipelines entirely.
Latency matters. Allstate’s production run showed a 31% improvement, reaching 420 ms end-to-end. Anything north of 300 ms risks being ignored by adjusters. Vendors that don't nail sub-200 ms latency consistently often end up as shelfware. Parasol and Verisk have managed sub-200 ms consistency. Rule editing is non-negotiable; adjusters need the ability to tweak thresholds without waiting for a vendor sprint. ClaimGenius and Parasol provide this capability. Computer vision requires caution; RepairGuard.ai offers 350 ms latency and image-only ingestion, but carriers forcing photo uploads face resistance. State Farm’s pilot is under review for scaling back. Reinforcement learning, such as Axiom Insights’ 34% reduction claim, requires six months of closed-claim feedback to stabilize. No carrier has run it in production for more than a year. The CFO should demand proof of precision before signing.
Demand a side-by-side of historical leakage versus predicted reduction. Get a latency SLA in the contract. One Tier-1 carrier negotiated 150 ms end-to-end. Anything slower is ineffective. Rule editor and audit trail are essential; adjusters will override, so you need to know why, when, and who did it. ClaimGenius and Parasol offer queryable audit tables. Integration costs can blow budgets; demand a fixed-fee integration or a capped-cost partner. Monthly calibration is necessary because models decay. Axiom Insights and Leakage.ai support this. Include an exit clause: if the engine underperforms by more than 20% in the first 12 months, terminate with a 20% refund. Only Parasol agreed to this. One Midwest mutual carrier negotiated all six terms. After 14 months, the engine paid for itself and saved $420K in audit labor. The CFO renewed for three years without a bid.
The next frontier is pushing leakage detection upstream. Carrier A runs every new application through the leakage engine before binding. Missed underwriting data leads to higher premiums or rejection. They saw a 7% bump in loss ratio accuracy and cut new-business leakage by 11%. Carrier B embeds the leakage score in their telematics app. High-risk drivers get a dynamic surcharge. Six months in, bodily injury claims dropped 4%. Regulators are watching; a California DOI filing argued that dynamic pricing based on internal leakage risk could constitute unfair discrimination. The DOI hasn't ruled yet.
The leakage dashboard flags annual exposure. Policy misalignments (p = 0.003, 95% CI: ±$Y.YYM) account for 62% of the variance, while data gaps explain the remaining 38%. The AUC on the detection model is 0.87. Focusing on the top three drivers—duplicate payments and contractor classification errors—yields a 12% precision uplift by fixing data gaps, whereas tightening policy controls improves recall by 8% but increases false-positive spend by 4%.
Look at the FNOL data model as a node in the broader insurance claims ecosystem. If the completeness and accuracy of the FNOL data model haven't been audited recently, you are monitoring only the entry point. Missing ZIP codes today can cascade into delayed claim routing tomorrow. When adjusters override the system more than 15% of the time, it signals a breakdown in alignment between the digital workflow and real-world conditions. The system responds by rerouting decisions manually. Second-order effects include inconsistent data feeding into actuarial models, eroding predictive accuracy and inflating reserves. Repeated overrides can create shadow processes that bypass formal controls. If the top reasons for overrides are process issues, the feedback loop degrades morale and increases turnover. Until these gaps are closed, the system will keep compensating with human intervention, masking deeper inefficiencies.
This will be obsolete in 18 months. That’s the shelf life of today’s AI leakage engines. If you’re duct-taping AI onto clunky infrastructure, you’ve already lost. The real play is rewriting the plumbing from the ground up before the market leaves you behind.
Jiangpeng Xu — Lead Author & *Principal Analyst*
Jiangpeng Xu has ten years of experience in AI in insurance, focusing on claims auto-processing, underwriting bots, and fraud detection. He holds a Computer Science master’s with a focus on machine learning in financial services.
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Key Takeaways
- A top-five U.S. P&C carrier identified that missing ZIP codes and VIN mismatches caused 14% of closed auto claims to leak over $2,100 per file.
- Deploying a real-time AI engine to flag 78% of data gaps before adjuster review saved an average of 5.4 minutes per claim across 180,000 annual cases.
- A UK insurer found that 34% of leakage events originated from one legacy underwriting rule, which the AI engine flagged to cut leakage by 41% in six months.
- AI detection hit rates vary by leakage type, ranging from 65% for policy misclassification to 94% for duplicate payments, with median costs spanning $1,120 to $3,450.
Community perspectives
Selected real discussions from insurance practitioners, adjusters and policyholders on public forums. Curated for relevance and quoted with attribution; each link opens the original thread.
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You weren't denied because you didn't notice the leak for a few days. You were denied because there was no covered cause of loss. Homeowners insurance is not for maintenance. Just because something is old and fails doesn't mean homeowners insurance would cover it. Sounds like this is a good time to sit down with an agent and review your coverages.
— FindTheOthers623 on Reddit · 2026-03-03 source -
Liberty mutual has denied my claim of water damage. "We do not cover loss caused directly or indirectly by any of the following excluded perils. Such loss is excluded regardless of any other cause or event contributing concurrently or in any sequence to the loss. These exclusions apply whether, or not, the loss event results in widespread damage or affects a substantial area. … 5. Continuous or repeated seepage or leakage of water or steam, or the presence or condensation of humidity, moisture, or vapor which occur
— tryptall on Reddit · 2026-03-03 source -
As you can see by the list, water is a big no-go with most standard home policies. You are ticking the box multiple times for the claim to be excluded from coverage . If it was just the timing of discovery that's one thing, but the rust and wear and tear throw any edge you had to debate out the window. Such is homeownership, there will always be something to fix.
— druzyyy on Reddit · 2026-03-03 source -
You may need to narrow your question. I think it's hard to deny at this point that there have been many proven outputs from the field of AI/deep learning - language translation, defect detection, OCR, weather forecasting, spam filtering, protein folding, tumor segmentation, etc. Can say that there are also plenty of exaggerated claims about AI, but there is at least something real and useful at its core.Progress in the field appears largely based around empirical experimentation; researchers come up with
— Ukv on Hacker News · 2025-01-27 source -
A bit like @newscracker, I'm on the lookout for a messaging app that works for me. @motohagiography addresses the privacy claims by asking good privacy for what.I've discussed nothing on a conf/video call or exchanged messages that are so sensitive as to absolutely require encryption. There are no absolutes in security anyway. Sorry, but I'm sublimely unparanoid at my national government reading my emails. While I could probably be accused of being a member of the metropolitan elite (c.f. suburb
— pr956738850 on Hacker News · 2021-01-24 source
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