How AI is Changing Auto Insurance (For Better and Worse)

After spending six years as an insurance technology consultant helping major carriers implement AI systems, and then experiencing these technologies firsthand as a customer, I’ve witnessed the dramatic transformation artificial intelligence is bringing to auto insurance. What started as simple telematics programs has evolved into comprehensive AI systems that analyze everything from your driving patterns to your social media activity to determine your rates and coverage eligibility.

The change isn’t subtle. In 2019, I paid $1,847 annually for auto insurance based primarily on traditional factors like age, location, and driving history. Today, that same coverage costs me $1,243—a 33% reduction—because AI systems recognize patterns in my driving behavior that traditional underwriting missed. Meanwhile, my neighbor’s rates increased 28% despite having a clean driving record, because AI identified risk factors that human underwriters would never have detected.

This dichotomy reveals the central tension in AI-powered insurance: the technology creates winners and losers with unprecedented precision, raising fundamental questions about fairness, privacy, and the social purpose of insurance. As someone who helped build these systems and now lives under their judgment, I’ve seen both the remarkable benefits and the deeply concerning implications.

The transformation goes far beyond pricing. AI is revolutionizing claims processing, fraud detection, customer service, and risk assessment in ways that fundamentally alter the relationship between insurers and policyholders. Some changes benefit consumers dramatically. Others create new categories of discrimination, privacy invasion, and algorithmic bias that regulators are struggling to address.

AI in Underwriting and Pricing: The New Risk Assessment

Traditional Underwriting vs. AI Models

Traditional auto insurance underwriting relied on a limited set of factors: age, gender, marital status, location, vehicle type, credit score, and driving history. Human underwriters applied these factors through established rating systems that treated broad demographic groups similarly.

AI underwriting analyzes hundreds or thousands of variables simultaneously, identifying complex patterns and correlations that humans can’t detect. Modern AI systems evaluate everything from smartphone accelerometer data to social media behavior to purchasing patterns, creating individualized risk profiles with unprecedented granularity.

The Transformation I’ve Witnessed:

Working with a major carrier’s AI implementation, I watched their underwriting model expand from 47 rating factors to over 2,300 data points. The AI identified risk correlations that shocked experienced underwriters: people who bought their cars in December had 8% lower accident rates than July purchasers. Drivers who charged their phones while driving (detected through telematics) had 23% higher claim rates.

The system could predict accident probability with 91% accuracy compared to 73% for traditional methods. This precision enabled the carrier to offer 15-25% lower rates to low-risk drivers while increasing rates significantly for high-risk drivers who previously blended into broad demographic categories.

Telematics and Behavioral Monitoring

Telematics programs monitor driving behavior through smartphone apps or plug-in devices, measuring acceleration, braking, cornering, speed, time of day, and mileage. First-generation programs offered simple good-driver discounts. Modern AI-powered telematics create real-time, dynamic risk assessments.

How Modern Telematics Actually Works:

The app on your phone continuously monitors driving behavior, feeding data to AI systems that analyze:

• Hard braking frequency and severity
• Acceleration patterns and aggressiveness
• Cornering speed and technique
• Phone usage while driving (screen touches, calls, texts)
• Time of day and day of week patterns
• Route selection and driving environments
• Weather and traffic condition responses

AI systems compare your driving against millions of other drivers, identifying patterns that correlate with accident risk. A driver who brakes frequently in moderate traffic might be attentive and cautious, while one who rarely brakes in heavy traffic might be dangerously inattentive.

My Personal Experience:

I enrolled in Progressive’s Snapshot program and received a 28% discount after six months. The AI determined that my driving patterns—consistent speeds, gradual acceleration and braking, minimal phone use, and avoiding late-night driving—indicated very low risk despite my younger age typically correlating with higher rates.

However, I carefully modified my driving behavior knowing I was being monitored. I avoided hard braking even when situationally appropriate, maintained conservative speeds, and didn’t drive late at night even when necessary. The discount rewarded behavioral changes rather than actual risk reduction—I became a more predictable driver, not necessarily a safer one.

Predictive Analytics and Non-Driving Data

The most controversial AI development involves using non-driving data to predict accident risk. Insurance companies purchase data from hundreds of sources, feeding AI systems that identify risk correlations that have nothing to do with actual driving.

Data Sources AI Systems Use:

• Credit reports and payment histories
• Retail purchasing patterns (what you buy and where)
• Social media activity and connections
• Education levels and employment histories
• Property records and homeownership status
• Magazine subscriptions and media consumption
• Organizational memberships and affiliations

Real-World Example:

One AI system I evaluated discovered that drivers who purchased premium pet food had 12% fewer accidents than those buying economy brands. The correlation likely indicates income level, personality traits (conscientiousness), and lifestyle factors that correlate with careful driving—but the system didn’t care about causation, only prediction accuracy.

These correlations create pricing disparities that feel arbitrary or discriminatory. Two identical drivers with identical driving records might pay dramatically different rates because one shops at Whole Foods and the other at Walmart, or one posts frequently on Instagram and the other doesn’t use social media.

AI in Claims Processing: Speed and Scrutiny

Automated Damage Assessment

AI-powered photo analysis enables instant damage assessment through smartphone apps. Policyholders photograph accident damage, and AI systems analyze the photos to estimate repair costs, detect fraud indicators, and approve or deny claims within minutes rather than days.

The Technology Behind It:

Computer vision systems trained on millions of damage photos can identify vehicle makes and models, categorize damage types, estimate repair costs, and detect inconsistencies suggesting fraud or misrepresentation. The systems recognize damage patterns associated with different accident types, identifying suspicious claims that warrant detailed investigation.

I implemented a system that reduced average claim processing time from 11 days to 37 minutes for straightforward claims. The AI correctly estimated repair costs within 8% for 89% of claims, dramatically improving customer experience while reducing processing costs by 67%.

When AI Gets It Wrong:

The system struggled with unusual damage patterns, custom equipment, or vehicles outside its training data. A classic car owner submitted photos after a minor accident, and the AI estimated $2,300 in repairs. The actual cost was $14,000 because the AI didn’t recognize rare vehicle-specific parts and specialized repair requirements.

Another claim involved subtle frame damage that wasn’t visible in photos. The AI approved a $3,500 estimate, but proper inspection revealed $18,000 in structural damage. The system’s confidence in its analysis led to inadequate initial estimates that frustrated both the customer and repair shops.

Fraud Detection Systems

Insurance fraud costs the industry approximately $29 billion annually. AI fraud detection systems analyze claims for suspicious patterns, anomalies, and indicators that warrant investigation.

How AI Detects Fraud:

Machine learning models analyze millions of claims to identify fraud indicators:

• Accident timing and location patterns
• Damage inconsistent with reported accident types
• Medical treatment patterns suggesting exaggeration
• Claimant behavior and communication patterns
• Social media activity contradicting claimed injuries
• Historical patterns across multiple claims

Personal Observation:

I watched an AI system flag a claim where the reported accident location was a quiet residential street at 2:47 AM on a Tuesday. Cross-referencing the driver’s social media showed posts from a bar 4 miles away at 2:15 AM. The damage pattern was consistent with parking lot contact, not the reported collision with a signpost.

Investigation revealed the driver had hit another car while intoxicated in the bar parking lot, then drove to a different location and staged a single-vehicle accident to avoid DUI charges. The AI caught what human adjusters would likely have missed.

The Overreach Problem:

Aggressive fraud detection creates false positives that harm legitimate claimants. One system I evaluated flagged 23% of claims for investigation, but only 11% of flagged claims involved actual fraud. The remaining 12% were legitimate claims subjected to intensive investigation, delays, and suspicion because AI misinterpreted normal variations as fraud indicators.

Subrogation and Fault Determination

AI systems analyze accident data, police reports, witness statements, and vehicle damage patterns to determine fault and identify subrogation opportunities where insurers can recover costs from at-fault parties.

Natural language processing analyzes police reports and statements, extracting relevant details and assessing credibility. Computer vision analyzes damage patterns and accident scene photos to reconstruct events and determine fault.

I worked on a system that improved subrogation recovery by 34% by identifying cases with high recovery probability that human adjusters missed. The AI recognized subtle patterns in accident descriptions and damage photos that indicated clear liability despite initially ambiguous circumstances.

The Privacy Implications: What You’re Really Agreeing To

Data Collection Scope

Modern insurance apps request permissions that grant access to far more data than necessary for basic insurance functions. The typical telematics app requests:

• Location services (precise GPS tracking 24/7)
• Motion sensors (accelerometer, gyroscope data)
• Camera access (photo upload capabilities)
• Contact list access (for emergency features)
• Phone state and identity (unique device identification)
• Storage access (reading and writing files)
• Network access (constant data transmission)

What Really Happens With Your Data:

Insurance companies aggregate telematics data with purchased data from hundreds of third-party sources. They track not just your driving, but your complete lifestyle patterns. Where you go, how long you stay, who you visit, what you purchase, where you work, and your daily routines.

I reviewed one carrier’s data architecture that integrated:

• Telematics driving data
• Credit bureau records
• Retail purchase histories from data brokers
• Social media profile analysis
• Property and asset records
• Employment and income verification data
• Health and wellness app data (through partnerships)

This comprehensive profile enables precise risk assessment but creates unprecedented privacy invasions. Your insurance company may know more about your daily life than your closest friends.

Consent and Control Issues

Most drivers don’t understand what they consent to when enrolling in telematics programs. Terms of service are deliberately vague about data usage, retention, and sharing. The few who read privacy policies discover broad permissions with minimal practical limits.

Real Terms of Service Analysis:

One major carrier’s privacy policy I analyzed granted the company rights to:

• Retain data indefinitely even after policy cancellation
• Share data with unnamed “partners and affiliates”
• Use data for unspecified “research and development”
• Analyze data for purposes beyond insurance underwriting
• Combine data with other sources without disclosure

Customers cannot opt out of specific data uses while maintaining telematics discounts. It’s all-or-nothing consent: grant comprehensive data access or pay higher rates.

The Coercion Problem:

When AI-driven pricing creates 25-40% rate disparities between traditional and telematics-based policies, enrollment becomes effectively mandatory for cost-conscious drivers. The “choice” to maintain privacy costs hundreds or thousands annually—a choice many can’t afford.

I’ve watched this transformation create a two-tier system: wealthy drivers who can afford privacy pay traditional rates, while lower-income drivers must accept comprehensive surveillance to afford coverage.

Data Security and Breach Risks

Concentrating detailed lifestyle data in insurance company databases creates massive security risks. A single breach could expose intimate details about millions of people’s daily lives, routines, and behaviors.

The Security Reality:

Insurance companies are not technology firms with security-first cultures. Many run outdated systems with inadequate security measures. I’ve audited insurance data systems that would horrify security professionals:

• Unencrypted databases containing years of location data
• Minimal access controls allowing broad internal data access
• Inadequate breach detection and response capabilities
• Third-party data sharing with minimal security requirements
• Long retention periods creating unnecessarily large attack surfaces

When breaches occur, they expose not just financial information but comprehensive lifestyle profiles including location histories, behavioral patterns, and relationship networks that enable identity theft, stalking, and targeted crimes.

The Discrimination and Bias Problem

Algorithmic Bias in Risk Assessment

AI systems learn patterns from historical data, perpetuating and often amplifying existing biases. If historical data reflects discriminatory practices, AI systems learn to replicate those discriminations while obscuring their basis.

Documented Bias Examples:

Research and investigations have revealed multiple cases where AI insurance systems created discriminatory outcomes:

• Predominantly minority neighborhoods receiving systematically higher rates despite controlling for relevant risk factors
• Lower-income zip codes facing higher premiums independent of actual loss history
• Gender-based rate disparities that exceeded actuarial justifications
• Education level correlations creating class-based discrimination

My Professional Experience:

I evaluated an AI underwriting system that consistently rated drivers in predominantly Hispanic neighborhoods 18% higher than comparable drivers in predominantly white neighborhoods. The AI had identified legitimate risk factors (higher uninsured motorist rates, different accident patterns), but the correlation created pricing that effectively discriminated based on ethnicity.

The company faced a dilemma: the AI was actuarially accurate (those neighborhoods did have higher claim rates), but the outcome violated fairness principles and potentially anti-discrimination laws. Should they use accurate predictions that create discriminatory outcomes, or sacrifice accuracy to avoid bias?

Proxy Discrimination

Even when AI systems avoid directly considering protected characteristics like race, gender, or religion, they often use proxy variables that correlate with those characteristics, creating indirect discrimination.

How Proxy Discrimination Works:

An AI system might not consider race directly, but uses factors that correlate strongly with race:

• Neighborhood location and zip code
• Education level and occupation
• Credit scores and financial histories
• Social media usage patterns
• Retail purchasing behaviors

The AI discovers that people who shop at certain stores, live in certain neighborhoods, or have certain purchasing patterns have higher accident rates. These patterns correlate with race, income, or other protected characteristics, creating discrimination without explicitly considering prohibited factors.

Regulatory Challenges:

Current anti-discrimination laws focus on explicit consideration of protected characteristics. They’re poorly equipped to address algorithmic discrimination that operates through complex, non-obvious correlations across hundreds of variables.

Regulators struggle to evaluate AI systems that even developers don’t fully understand. The “black box” nature of neural networks makes it virtually impossible to determine whether specific outcomes result from legitimate risk factors or discriminatory patterns.

The Fairness vs. Accuracy Tradeoff

This tension reveals a fundamental question: should insurance rates reflect individual risk as accurately as possible (potentially creating discriminatory outcomes), or should they promote social fairness (potentially subsidizing high-risk drivers with low-risk drivers’ premiums)?

The Traditional Insurance Model:

Insurance traditionally spread risk across broad pools, with high-risk and low-risk members subsidizing each other. This approach promoted social solidarity and ensured insurance availability for everyone, but created cross-subsidies that some viewed as unfair.

The AI-Driven Future:

Hyper-precise risk assessment enables perfect price discrimination where everyone pays exactly for their individual risk. This eliminates cross-subsidies and rewards low-risk behavior, but potentially makes insurance unaffordable for high-risk individuals who still need coverage.

I’ve watched carriers struggle with this balance. Maximizing AI accuracy increases profitability by attracting low-risk customers with low rates while shedding high-risk customers with high rates. But this “adverse selection” death spiral can make insurance unavailable for people who most need it.

READ ALSO: 7 Hidden Exclusions in Your Homeowners Policy That Could Bankrupt You

The Customer Experience Transformation

Instant Quote and Binding

AI systems enable instant quotes and immediate coverage binding based on minimal information. Drivers can obtain coverage through smartphone apps in minutes rather than days, with no phone calls or human interaction required.

The Technology:

Natural language processing interprets customer inputs, extracting relevant details. AI systems access dozens of databases simultaneously, pulling driving records, credit reports, property records, and other data sources. Machine learning models assess risk and generate pricing in seconds.

I helped implement a system that reduced quote generation time from 22 minutes (human process) to 47 seconds (AI process) while improving accuracy by 31%. Customer satisfaction increased dramatically, and conversion rates (quotes becoming policies) improved by 19%.

The Downside:

Instant automated quotes provide no opportunity for human judgment or context consideration. Drivers with unusual circumstances or temporary issues have no mechanism to explain situations or request consideration.

One system I audited automatically declined a driver because AI found an accident report from three years prior. The accident wasn’t the driver’s fault and wasn’t on his driving record because charges were never filed. But the AI had no mechanism to consider context—it simply applied its risk model and declined coverage.

Chatbots and Virtual Assistants

AI-powered chatbots handle most customer service interactions, providing instant responses 24/7 without human involvement. Natural language processing enables these systems to understand questions, provide information, and complete transactions.

Performance Reality:

Modern insurance chatbots successfully resolve 60-75% of inquiries without human intervention. They handle routine questions, policy changes, payment processing, and document retrieval efficiently and instantly.

However, the remaining 25-40% of inquiries require human judgment, empathy, or complex problem-solving that AI can’t provide. Customers with these needs face frustrating experiences bouncing between automated systems trying to reach human representatives.

Personal Frustration:

I experienced this personally when filing a claim with unusual circumstances. The chatbot couldn’t understand my situation and kept providing irrelevant responses. I spent 37 minutes trying to reach a human representative, repeatedly cycled back to the chatbot, and eventually gave up and filed the claim through a different channel.

The cost savings from AI automation are real, but they come at the expense of customer experience for anyone with non-routine needs.

Personalized Recommendations

AI systems analyze policyholder profiles and behaviors to generate personalized coverage recommendations, identify potential gaps, and suggest cost-saving opportunities.

These systems consider factors like:

• Life stage and family situation changes
• Asset accumulation and protection needs
• Behavioral patterns indicating changed risk profiles
• Market conditions and competitive product availability

I implemented a recommendation system that increased average premium per policy by 11% while improving customer satisfaction because recommendations actually addressed real coverage needs that customers didn’t recognize.

The Manipulation Risk:

Personalized recommendations optimize for company profitability, not necessarily customer benefit. AI systems identify opportunities to upsell profitable coverage while avoiding conversations about cheaper alternatives or coverage reductions that might benefit customers.

One system I reviewed consistently recommended specific endorsements that carried 64% profit margins while rarely suggesting deductible increases that would save customers more money than the endorsement costs.

The Autonomous Vehicle Future

Coverage Model Disruption

Autonomous vehicles will fundamentally transform auto insurance as liability shifts from drivers to manufacturers, software developers, and vehicle owners in complex new ways.

Emerging Coverage Questions:

• Who’s liable when autonomous systems fail: driver, manufacturer, or software developer?
• How does insurance cover vehicles operating in partial autonomy?
• What role does driver monitoring play in mixed-autonomy operation?
• How do insurers assess risk for constantly-updating software systems?

AI systems will need to evaluate entirely new risk factors: software version histories, update compliance, system maintenance records, and autonomous system engagement patterns.

The Transition Period:

The next 10-20 years will involve mixed autonomy where some vehicles are fully autonomous, others partially autonomous, and many remain fully manual. This creates unprecedented complexity for risk assessment and liability determination.

I’ve participated in working groups developing autonomous vehicle insurance frameworks. The complexity is staggering—current insurance models simply don’t translate to autonomous vehicles, requiring complete reinvention of coverage structures.

Manufacturer Liability Integration

As liability shifts toward manufacturers, insurance may transition from individual driver policies to manufacturer fleet policies or vehicle-based coverage included with purchase prices.

Potential Models:

• Manufacturers self-insure autonomous vehicle fleets
• Insurance becomes vehicle-based rather than driver-based
• Hybrid models where driver behavior in partial-autonomy determines rates
• Subscription models where insurance is included with vehicle access

This transition could dramatically reduce individual insurance costs (fewer accidents) while shifting premium dollars from individual consumers to manufacturers who distribute costs through vehicle prices.

AI Monitoring Autonomous Systems

Insurers will use AI to continuously monitor autonomous vehicle system performance, identifying patterns suggesting increased failure risk or suboptimal operation.

These systems might analyze:

• Sensor degradation and maintenance patterns
• Software version currency and update compliance
• Disengagement frequency and circumstances
• System intervention and override patterns
• Environmental condition performance variations

This creates new privacy invasions where insurers monitor not just driving behavior but vehicle system performance, maintenance compliance, and software management.

Regulatory Responses and Consumer Protection

State-Level Regulation Efforts

State insurance regulators are struggling to address AI-driven changes with varying approaches and effectiveness:

California’s Approach: Requires insurers to disclose AI factors and demonstrate actuarial justification. Prohibits certain non-driving factors and requires human review of AI decisions. However, enforcement is challenging given AI system complexity.

New York’s Framework: Mandates algorithm transparency and third-party audits of AI systems. Requires insurers to explain rate factors in understandable terms. Creates appeals processes for algorithmically-determined denials.

Texas’s Hands-Off Stance: Minimal AI-specific regulation, allowing market competition to drive innovation. Relies on general anti-discrimination laws without AI-specific requirements.

The Coordination Problem:

Fifty different state approaches create compliance complexity and regulatory arbitrage where insurers operate differently in different states. National regulation could address this but faces political and jurisdictional challenges.

Federal Legislation Proposals

Multiple federal bills address AI in insurance, though none have passed:

The Algorithmic Accountability Act: Would require impact assessments for automated decision systems, including insurance AI. Mandates bias testing, accuracy validation, and consumer explanation rights.

The Data Protection Act: Would limit data collection, require explicit consent for non-essential uses, and create data minimization requirements. Could significantly restrict insurance AI data usage.

The AI Transparency Bill: Would require companies to disclose when AI makes consequential decisions about individuals, explain decision factors, and provide appeal mechanisms.

Consumer Rights and Protections

Consumers need specific protections in AI-driven insurance markets:

Essential Protections:

• Right to human review of AI decisions
• Explanation rights for rate determinations
• Data access and correction capabilities
• Opt-out options without penalty
• Bias testing and regular audits
• Data retention limits and deletion rights
• Breach notification requirements

The Enforcement Challenge:

Even with strong regulations, enforcement requires technical expertise that most regulatory agencies lack. Insurers can claim technical complexity prevents transparency, making oversight difficult.

Practical Strategies for Consumers

Understanding Your AI-Driven Rates

Request detailed explanations of rating factors from your insurer. Many states require disclosure, though explanations are often incomplete or confusing.

Key Questions to Ask:

• What data sources inform my rates?
• Can I review and correct data affecting my premiums?
• How do telematics programs affect my specific rates?
• What non-driving factors influence my pricing?
• How can I improve my risk profile?

Optimizing Your AI Risk Profile

If participating in telematics programs, understanding AI assessment criteria enables optimization:

Behavioral Adjustments:

• Avoid hard braking and aggressive acceleration
• Minimize phone use while driving (even hands-free calling affects some systems)
• Avoid late-night and early-morning driving when possible
• Choose routes with less complex traffic patterns
• Demonstrate consistent, predictable driving patterns

Be aware: Optimizing for AI assessment may not actually make you a safer driver—just a more predictable one that fits AI models of low-risk behavior.

Privacy Protection Strategies

Minimizing Data Exposure:

• Decline telematics participation if you can afford higher rates
• Review and limit app permissions to essential functions only
• Use separate devices for insurance apps versus personal data
• Regularly review and delete unnecessary data
• Exercise data access and deletion rights where available

The Cost-Benefit Analysis:

Calculate the actual savings from telematics programs versus the privacy costs. If you save $300 annually but grant comprehensive location tracking and behavioral monitoring, is the tradeoff worthwhile?

Shopping for Less AI-Dependent Options

Some insurers rely more heavily on traditional underwriting than AI-driven models. These companies may offer better rates for drivers who don’t fit AI risk profiles well.

Alternatives to Consider:

• Regional and local insurers with traditional underwriting
• Usage-based insurance that doesn’t require constant monitoring
• Pay-per-mile insurance for low-mileage drivers
• Group insurance through employers or associations

The Path Forward: Balancing Innovation and Protection

The Promise of AI Insurance

AI has genuine potential to improve auto insurance:

Legitimate Benefits:

• More accurate risk assessment reduces cross-subsidies
• Faster claims processing improves customer experience
• Better fraud detection reduces costs for honest policyholders
• Personalized pricing rewards safe driving behavior
• Improved customer service through 24/7 availability

The Perils That Must Be Addressed

Critical Concerns:

• Privacy invasions from comprehensive data collection
• Algorithmic bias and discrimination
• Lack of transparency and explainability
• Coercive data sharing through pricing pressure
• Security risks from data concentration

A Balanced Framework

Effective AI insurance regulation should:

Enable Innovation While Protecting Consumers:

• Require transparency and explainability for consequential decisions
• Mandate regular bias testing and public reporting
• Limit data collection to actuarially necessary factors
• Provide opt-out options without punitive pricing
• Ensure human review availability for disputed decisions
• Enforce data security standards and breach accountability
• Prohibit discrimination through proxy variables

Conclusion: Navigating the AI Insurance Landscape

The AI transformation of auto insurance is inevitable and accelerating. The technology provides genuine benefits—more accurate pricing, faster service, better fraud detection—but creates serious risks around privacy, discrimination, and algorithmic accountability.

As someone who helped build these systems and now lives under their judgment, I believe the key is informed participation. Understand what data you’re providing, what AI systems do with that data, and what you’re really consenting to when enrolling in telematics programs or using AI-powered services.

The 33% savings I’ve achieved through AI-optimized insurance is real and substantial. But it comes at the cost of comprehensive surveillance of my driving patterns, locations, and behaviors. That’s a tradeoff I’ve made consciously and deliberately—not one I accepted without understanding the implications.

Your situation may differ. Some drivers benefit dramatically from AI systems, others face higher rates and privacy invasions. The optimal approach depends on your risk profile, privacy values, and financial situation.

What’s non-negotiable is the need for informed choice, transparency, and meaningful protections against discrimination and abuse. AI insurance is here to stay, but the terms under which it operates should reflect not just what’s technically possible, but what’s socially acceptable and ethically justified.

Stay informed, ask questions, understand your rights, and make deliberate choices about when AI surveillance is worth the financial benefits. The future of auto insurance is algorithmic—make sure you understand the code that’s judging you.

In another related article, What Auto Insurance Actually Covers (And the Expensive Surprises It Doesn’t)