Bad Faith Insurance Claims: When to Pursue One and How AI Helps Build the Record

Bad faith insurance claims involve an insurer's unreasonable or unfair handling of a claim under the applicable policy and state law. A denial, delay, or low offer may be evidence of bad faith, but none of them prove it automatically. The strength of any claim usually depends on the policy, the jurisdiction, the investigation, the communications, the insurer's stated reasoning, its settlement conduct, and the harm caused by the handling itself.

The reason bad faith cases live or die on documentation is that the fight isn't really about the outcome; it's about the process. A wrong denial, on its own, is often just a wrong denial, and what turns it into something more is the record showing what the insurer knew, when it knew it, what it asked for, and what it ignored. Firms that build that record early tend to have leverage; firms that don't tend to argue about impressions.

This guide covers what qualifies as bad faith, how first-party and third-party claims differ, the evidence categories that matter, and how AI helps plaintiff firms reconstruct the claim-handling chronology without overstating what the technology can actually do.

Key Takeaways

• Bad faith is more than an unfavorable claim outcome; it's a question of whether the insurer handled the claim reasonably under the policy and applicable law.
• First-party and third-party bad faith involve different duties, different available claimants, and significantly different jurisdictional treatment.
• The insurer's contemporaneous claims file is often more valuable than any explanation offered after litigation begins.
• A detailed chronology can reveal unexplained gaps, shifting denial reasons, ignored evidence, and missed settlement opportunities.
• Damages caused by the handling itself must be documented separately from the underlying insured loss.
• AI can organize records, build timelines, and flag inconsistencies, but attorneys still determine whether the evidence meets the jurisdiction's legal standard.

What Is Bad Faith Insurance?

Bad faith insurance generally refers to an insurer's unreasonable, unfair, or dishonest handling of a claim it owed a duty to process in good faith. The precise legal test varies by state, but most formulations examine whether the insurer had a reasonable basis for its handling decisions and whether the evidence at the time supported that basis. A breach of contract asks whether the insurer paid what the policy required, while a bad faith claim asks how the insurer got to its answer.

That distinction is important because it changes what discovery, evidence, and damages look like. The table below outlines the practical differences.

The common-law formulation many plaintiff firms rely on requires proof that policy benefits were due and that the insurer lacked a reasonable basis for withholding them. That framework is a useful starting point, but it's not the universal standard, so readers should treat it as one common approach rather than the test in every jurisdiction. Which is why the next question worth answering is which category of bad faith actually applies.

First-Party vs Third-Party Bad Faith

First-party bad faith involves the insured pursuing a claim under their own policy, while third-party bad faith involves the insurer's handling of a liability claim brought against its insured. The duties, available claimants, procedural prerequisites, and available damages can differ significantly between the two, so the analysis usually starts with identifying which category applies before any of the other elements matter.

Third-party bad faith is where state law variation matters most. Some jurisdictions permit direct actions by injured claimants; others require assignment from the insured; others require an excess judgment or a specific procedural sequence before a claim can be asserted at all.

What Isn't Automatically Bad Faith?

Not every claim dispute qualifies as bad faith, and treating it that way weakens credibility with adjusters and courts alike. An insurer can be wrong about coverage without acting in bad faith, and a settlement offer can be low without proving misconduct. Even a reasonable investigation can still reach an incorrect conclusion, which is why the analysis has to focus on process rather than outcome.

The situations below often look like bad faith but frequently don't rise to that level on their own:

• A legitimate policy-interpretation dispute where both readings have support
• A reasonable investigation that reaches an incorrect conclusion
• A clerical mistake corrected promptly after it's identified
• A request for information genuinely necessary to evaluate the claim
• A settlement offer lower than expected but supported by the file's contemporaneous analysis
• A delay caused by incomplete or conflicting evidence outside the insurer's control
• A denial based on a fairly debatable coverage issue, where the jurisdiction recognizes that defense

The stronger bad faith cases are the ones where the process itself broke down, not the ones where an insurer made a decision the claimant disagreed with.

Common Examples of Bad Faith Insurance Conduct

The patterns below show up regularly in bad faith litigation, but each one has to be evaluated against the record and the applicable legal standard rather than assumed to be actionable on its own.

Denying a Claim Without a Reasonable Basis

A denial without meaningful investigation, one that misstates the policy, or one that ignores controlling evidence in the file can all support a bad faith theory. Boilerplate denial language that doesn't engage with the actual claim facts is a common indicator worth examining.

Unreasonably Delaying Investigation or Payment

Long, unexplained gaps between events matter more than any single delay. Repeated document requests, inaction after the file is complete, and payment withheld after coverage was established are the patterns worth tracking on a timeline.

Conducting an Inadequate or One-Sided Investigation

An investigation that only pursues information supporting denial, ignores witness statements, disregards expert evidence, or relies on a conflicted reviewer can support a bad faith theory. What matters is whether the insurer built a complete picture before deciding.

Misrepresenting Policy Language or Claim Facts

Misquoting an exclusion, omitting relevant coverage language, misstating deadlines, or mischaracterizing submitted evidence are the kinds of misrepresentations that show up when the contemporaneous file is compared against the letters that went out to the insured.

Making an Unreasonably Low Settlement Offer

A low offer on its own isn't bad faith, but a low offer that ignores undisputed damages, contradicts the insurer's own internal valuation, or comes with no explanation may support a claim. Context is what separates hard negotiation from actionable conduct.

Failing to Communicate

Unanswered correspondence, missing status updates, failure to identify what information is still needed, and failure to notify the insured of important settlement opportunities all stand out when the file is reconstructed in order.

Failing to Settle Within Limits When Appropriate

In third-party contexts, an insurer's refusal to accept a reasonable settlement opportunity within limits, where liability is clear and damages likely exceed the policy, can create exposure. Jurisdiction-specific requirements around notice, timing, and documentation apply here, and they matter more than most templated demand language accounts for.

How to Prove Insurance Bad Faith

Proving bad faith requires reconstructing what the insurer knew at each point in the claim, what it did in response, and whether its conduct met the applicable standard for reasonable handling. Every jurisdiction adds its own overlay, but the underlying evidence categories are broadly consistent.

Building this record early is what separates a claim that gets taken seriously from one that gets dismissed as speculative. Explore ProPlaintiff'sAI paralegal →

Building the Claim-Handling Chronology

The chronology is where most bad faith cases are actually won or lost, because describing what the insurer did wrong isn't enough on its own. The record has to show the sequence in a way an adjuster, a mediator, or a jury can follow without effort, which is why chronology construction matters more than any single denial letter or delay.

A useful chronology captures the date of each event, the actor responsible, the source that establishes it, the insurer's response, the delay since the prior event, and the legal significance of the pattern that emerges. When the same request appears three times over six months, or a denial letter cites a provision that doesn't match the policy, or an offer arrives with no reference to evidence submitted a month earlier, those patterns become visible only when the file is reconstructed in order.

The patterns worth flagging on any chronology include:

• Repeated document requests for records already provided
• Long inactive periods with no explanation in the file
• Evidence received but never addressed in the analysis
• Denial reasons that shift over time without new evidence
• Decisions issued before the investigation was actually complete
• Missing supervisory review on decisions that should have required it
• Failure to respond to time-sensitive demands within the stated deadline
• Internal and external explanations that don't match each other
• Payment approved but not issued
• Automated denial followed by only cursory human review

That last item is worth its own section, because insurer use of AI in claim handling has become its own discovery subject and it's changing what the chronology needs to capture.

How Insurer Use of AI May Affect Bad-Faith Discovery

When automated systems influence claim decisions, the discovery footprint expands substantially. The questions aren't just about what the insurer decided but about how the decision was produced, what inputs the system relied on, whether prohibited variables were used, and whether meaningful human review actually happened before the outcome was final.

Categories worth pursuing include model or rules-engine inputs, claim-scoring criteria, fraud flags, data sources, confidence thresholds, human-review requirements, override history, escalation rules, vendor involvement, audit logs, and training documentation. Insurance-law commentary has increasingly flagged bad-faith risk where AI-driven systems rely on flawed data, lack adequate oversight, or produce decisions without meaningful human review. The mere use of AI doesn't prove wrongdoing; the question is whether the automated system contributed to unreasonable handling.

What Damages May Be Available?

Available damages depend heavily on the jurisdiction, the theory of liability, and whether the claim is first-party or third-party. Some states allow only contractual damages plus interest, while others allow consequential damages, emotional distress, attorney fees under statute, and punitive damages when the legal standard is met. A few permit statutory multipliers or fee-shifting, so the recovery landscape can look completely different from one state to the next.

Categories worth documenting from the outset include unpaid contractual benefits, consequential economic losses, excess judgment exposure in third-party cases, attorney fees where authorized, interest, emotional distress damages where permitted, and punitive damages where the standard is met. Damages caused by the handling should be documented separately from the underlying insured loss, because the two are analytically distinct.

How AI Helps Build a Bad-Faith Claim Record

AI helps plaintiff firms build the record, not prove the case. The technology accelerates document organization, timeline construction, and pattern detection, but it doesn't make legal conclusions about whether conduct meets a jurisdictional standard, so the attorney judgment call sits where it always has. Where it earns its keep is handling the categories of work that make bad faith cases expensive to prepare manually.

The value here isn't that AI makes the decision; it's that AI does the hours of extraction, sorting, and cross-referencing that plaintiff firms typically pay paralegals to do manually across a claim file that might run thousands of pages.

Explore ProPlaintiff'sAI medical chronologies →

Risks of Using AI to Evaluate Bad Faith

AI helps organize the record, but it introduces specific risks when applied to legal analysis. The main ones include hallucinated legal standards, where the system merges rules from multiple jurisdictions or invents authority; missing context, where a long gap is flagged as delay without accounting for legitimate reasons; false pattern detection, where repeated wording is treated as improper motive when it actually reflects standard templates; and loss of source traceability, where a summary lacks page-level citations and can't be audited. Every AI-generated summary should preserve links back to the original document, because a claim built on unverifiable outputs is easier to dismantle than one built on the record itself.

Questions Plaintiff Firms Should Ask Before Proceeding

Before committing resources to a bad faith case, the analysis usually comes down to a set of threshold questions. The answers determine whether the case is worth building further or whether the record supports a different theory, so working through these before filing saves months of misdirected effort later:

1. What policies and endorsements apply, and what duties do they create?
2. Who is asserting the claim, and do they have standing under the applicable law?
3. Is the theory first-party or third-party, and does the jurisdiction recognize it?
4. Which state's law governs, and what does the mental-state standard require?
5. What was the insurer's contemporaneous reason, and does the claim file support it?
6. Was the investigation complete and balanced?
7. What evidence did the insurer possess at the time of each key decision?
8. Were settlement opportunities reasonable, documented, and communicated to the insured?
9. What additional harm resulted from the handling, separate from the underlying loss?
10. Are there notice, cure, judgment, or assignment prerequisites that haven't been met?
11. Did an automated system influence the decision, and what discovery would that require?
12. What alternative explanation will the insurer offer, and does the current record answer it?

How ProPlaintiff Helps Firms Build the Bad-Faith Claim Record

Bad faith cases are won by the firms that reconstruct the claim-handling record in enough detail to make the pattern impossible to explain away. ProPlaintiff supports that work by organizing policies, correspondence, medical records, and claim documents into a searchable case file, building a source-linked chronology of the handling events, and flagging the patterns that typically matter: repeated requests, unexplained gaps, ignored evidence, and shifting explanations.

The platform doesn't decide whether an insurer acted in bad faith; that's the attorney's call under the applicable law. What it does is give plaintiff firms the organized, source-linked record attorneys need to make that call and defend it, without spending weeks doing the extraction manually.

Explore ProPlaintiff'sAI paralegal workflows →

Frequently Asked Questions About Bad Faith Insurance

What Is Bad Faith Insurance?

Bad faith insurance generally refers to an insurer's unreasonable or unfair failure to meet duties owed under a policy or applicable law. The precise definition and required proof vary by jurisdiction, and the specific test in one state may not apply in another.

Is Denying an Insurance Claim Automatically Bad Faith?

No, an insurer can deny a claim incorrectly without necessarily acting in bad faith. The analysis usually considers the policy, the investigation, the available evidence, the stated explanation, and the applicable legal standard, and a wrong decision isn't the same as a decision made in bad faith.

How Do I Prove an Insurance Company Acted in Bad Faith?

Evidence typically includes the policy, the claim file, the communications, the investigation records, the offers and denials, the payment history, applicable claims-handling standards, and proof of additional harm caused by the insurer's conduct. The chronology of when the insurer knew each fact tends to matter more than any single document.

What Are Examples of Insurance Bad Faith?

Common examples include unreasonable delay, denial without adequate investigation, misrepresentation of policy terms, ignoring relevant evidence, failure to communicate, and failure to consider a reasonable settlement opportunity. Whether any particular conduct qualifies depends on state law and the facts of the specific claim.

When Can I Sue an Insurance Company for Bad Faith?

That depends on the claimant's legal relationship to the policy, the jurisdiction's law, the insurer's conduct, the resulting damages, and any procedural prerequisites such as notice, cure periods, judgments, or assignments. The threshold varies enough that pre-suit review is almost always worthwhile.

Can a Third-Party Claimant Sue an Insurer for Bad Faith?

Sometimes, but not universally. Third-party standing, assignment rights, and prerequisites vary significantly by state, and some jurisdictions require an excess judgment or a specific procedural sequence before a third-party bad faith action can proceed.

What Is the Difference Between Bad Faith and Breach of Contract?

A contract claim asks whether the insurer failed to provide benefits required by the policy. Bad faith asks whether the insurer's handling of the claim was unreasonable or improper, and it may support additional remedies where the jurisdiction permits them.

Can AI Prove an Insurance Bad Faith Claim?

No, AI can organize documents, build timelines, compare stated reasons, and flag potential issues, but attorneys still have to verify the evidence and apply the jurisdiction-specific legal standard. The technology accelerates the record-building work, not the legal analysis.

Can an Insurer's Use of AI Support a Bad Faith Claim?

Potentially, if the automated system contributed to unreasonable handling, relied on improper data, or produced decisions without adequate human oversight. The use of AI on its own doesn't establish bad faith, but it opens discovery questions about how the decision was actually made.