United States Legal Articles & Guides — 87 Articles

When artificial intelligence influences an insurance claim decision, policyholders may reasonably ask why the claim was denied. But does U.S. law provide a general right to an explanation of an AI-generated decision? The answer is more complicated than simply saying “yes” or “no.” This guide examines explainable AI, algorithmic transparency, claim-denial explanations, black-box models, human review, auditability, insurance regulation and consumer protection.

Artificial intelligence is increasingly being used to process insurance claims, detect fraud, estimate losses and prioritise investigations. But what happens when an algorithm makes a mistake? Can an insurer deny a legitimate claim because its AI system produced the wrong result? This guide examines AI claims processing, automated claim denials, human review, unfair claims practices, bad-faith risks, third-party vendors, explainability, audit trails and insurer responsibility.

Artificial intelligence is changing insurance underwriting by allowing insurers to analyse larger datasets, automate risk assessment and make faster eligibility decisions. But algorithmic underwriting also raises questions about discrimination, proxy variables, explainability, data privacy, actuarial fairness and regulatory oversight. This guide examines how AI underwriting works and the legal issues insurers must consider when algorithms influence insurance risk classification.

Artificial intelligence is increasingly being used to analyse healthcare utilisation, predict costs and support medical-necessity reviews. But medical necessity is an individualised clinical and coverage determination, not simply a statistical prediction. This guide examines the legal risks of using AI to determine whether healthcare treatment is medically necessary, including algorithmic bias, physician review, prior authorisation, appeals, Medicare Advantage, Medicaid and ERISA.

Artificial intelligence is increasingly being used to streamline prior authorisation and utilisation-management processes. But when an algorithm influences whether a patient receives an expensive test, procedure, medication or treatment, efficiency becomes a legal question. This guide examines AI prior authorisation, medical necessity, automated denials, human review, appeals, Medicare Advantage, Medicaid managed care, ERISA and insurer liability.

Artificial intelligence is increasingly used by health insurers to predict risk, process claims, detect fraud, manage utilisation and support coverage decisions. But healthcare AI creates unique legal questions involving patient data, medical necessity, prior authorisation, algorithmic discrimination, HIPAA, automated decision-making and consumer protection.

Artificial intelligence is becoming embedded in insurance underwriting, pricing, claims, fraud detection and customer service. In the United States, however, insurers do not operate under a single comprehensive AI insurance statute. Instead, existing state insurance laws, NAIC guidance, model frameworks, market-conduct examinations and emerging rules concerning data and predictive models are shaping the regulatory landscape.

Artificial intelligence is transforming insurance fraud detection by analysing claims, identifying unusual patterns and connecting information across large datasets. But a fraud score is not the same as proof of fraud. False positives, biased historical data, opaque models and automated investigations can harm legitimate policyholders. This guide examines AI fraud detection, fraud scoring, human review, third-party vendors and the evolving regulatory framework.

Artificial intelligence can make insurance decisions faster and more data-driven, but algorithms can also reproduce historical bias or create discriminatory outcomes through seemingly neutral variables. This guide explains AI insurance discrimination, proxy discrimination, actuarial justification, underwriting and pricing bias, claims discrimination, external data, regulatory testing and the growing role of state insurance regulators.

Artificial intelligence is increasingly being used to process insurance claims, analyse documents and images, detect fraud and prioritise claims for investigation. But when an algorithm contributes to a claim denial, insurers cannot simply point to the technology and avoid legal responsibility. This guide examines AI claims automation, human review, automated denials, fraud detection, unfair claims practices, third-party vendors and regulatory oversight.

Artificial intelligence is changing how insurers evaluate risk and calculate premiums. Predictive models can analyse enormous datasets, accelerate underwriting and identify patterns that traditional methods may miss. But when algorithms determine who is considered risky and how much they pay, questions arise about data quality, transparency, unfair discrimination, third-party models and regulatory oversight.

Banks and fintech companies increasingly depend on AI for lending, fraud detection, investment services and customer decisions. But when an AI system makes a serious mistake, responsibility can become difficult to determine. Is the bank liable? The fintech? The AI developer? A third-party vendor? This guide examines contractual, negligence, consumer-protection, regulatory and model-risk issues surrounding liability for AI-powered financial services.