# 9 Key Use Cases for Agentic AI: How to Unleash Automation in Healthcare, Financial Services, Banking, and Insurance .png?fit=crop&width=48&height=48&name=Favicon%20Primary%202%20(2).png) Skan AI Contributors 09 Jun, 2025 --- Agentic AI automation - led by autonomous AI agents that learn, adapt, and optimize to make independent decisions - is revolutionizing industries by leveraging process intelligence and autonomous agents to end-to-end workflows. Unlike traditional task automation, agentic automation offers dynamic, context-aware solutions that can significantly enhance efficiency and reduce costs. In sectors like healthcare, banking, and insurance, where complex processes and regulatory requirements abound, the potential benefits are substantial. According to the 2019 CAQH Index, the U.S. healthcare system incurs approximately [**$350 billion annually in administrative costs**](https://www.caqh.org/news/caqh-2019-index-133-billion-33-percent-healthcare-administrative-spend-can-be-saved-annually?utm_source=chatgpt.com). Of this, [**$40.6 billion is spent on eight specific administrative transactions**](/content/blogs/9-key-use-cases-for-agentic-ai-in-healthcare-banking-insurance#/index.html). The report estimates that by transitioning these transactions to fully electronic processes, the industry could save $13.3 billion annually, representing 33% of the current spend on these transactions. Below, we explore ten high-impact use cases grouped by industry to illustrate how agentic automation is driving transformation. ## Healthcare Agentic AI Use Cases (Providers & Payers) ### **1. Pre-Authorization and Eligibility Checks** Healthcare providers often face delays due to manual pre-authorization processes, leading to postponed treatments and patient dissatisfaction. These inefficiencies compromise patient care and inflate [**administrative costs**](/content/blogs/the-hidden-costs-in-your-contact-center-that-no-one-talks-about/index.html). _Challenge:_ Long approval delays for procedures frustrate patients and providers. _Agentic AI Advantage:_ Autonomous agents can validate eligibility, initiate pre-authorizations, and respond to provider queries in real time. _Potential Win:_ Payers can potentially reduce turnaround time for high-volume pre-authorization requests from multiple days to under 24 hours. ### **2. Prior Authorization Appeals Processing** The appeals process for denied claims is labor-intensive and time-consuming, often leading to delayed patient care and increased administrative obstacles. [**Manual handling of appeals**](/content/case-studies/leading-us-health-insurance-provider-partners-with-skan-to-identify-appeals-process-deviations/index.html) can result in errors and inconsistent outcomes. _Challenge:_ High volume of manual appeals leads to delays and denials. _Agentic AI Advantage:_ Agents extract clinical data, evaluate denial reasons, and draft templated responses for appeal resubmission. _Potential Win:_ Payers could process three times more appeals with the same number of full-time employees using autonomous agents. ### **3. Revenue Cycle Management (RCM)** Inefficient billing and collections processes can lead to significant revenue losses for healthcare organizations. Manual errors and delays in coding and claim submissions exacerbate financial challenges. _Challenge:_ Fragmented billing, coding, and collections. _Agentic AI Advantage:_ Agents track claims, validate coding, flag anomalies, and initiate collections workflows. _Potential Win:_ Hospital networks could reclaim millions in lost revenue within one quarter using agentic automation. ## Banking & Financial Services Agentic AI Use Cases ### **4. Loan Origination & Underwriting** Traditional loan processing involves multiple manual steps, leading to prolonged approval times and potential customer attrition. Inefficiencies in underwriting can also increase the risk of defaults. _Challenge:_ Legacy processes slow down approvals and risk assessments. _Agentic AI Advantage:_ Agents gather financial documents, verify identity, assess creditworthiness, and prepare files for human review. _Potential Win:_ Digital-first banks can reduce manual touchpoints by upwards of 60% in small business loan workflows. ### **5. Know Your Customer (KYC) & Risk Scoring** KYC compliance is critical but often hampered by manual data collection and verification processes, leading to delays and potential regulatory penalties. Inadequate risk assessment can expose institutions to fraud and financial losses. _Challenge:_ Constant regulatory pressure and manual KYC processes. _Agentic AI Advantage:_ Autonomous agents continuously monitor, refresh, and flag KYC profiles with new risk indicators. _Potential Win:_ Multinational banks can slash onboarding time by over 40% while boosting compliance accuracy. ### **6. Dispute & Chargeback Resolution** Handling disputes and chargebacks manually is resource-intensive and prone to errors, affecting customer satisfaction and increasing operational costs. Delayed resolutions can also impact cash flow. _Challenge:_ Time-consuming, inconsistent resolutions frustrate customers. _Agentic AI Advantage:_ Agents auto-investigate disputes, gather evidence, and submit standardized responses across channels. _Potential Win:_ Card issuers can improve [**SLA adherence**](/content/case-studies/f200-enterprise-arrive-at-a-baseline-sla-for-turnaround-time/index.html) by 50% and reduce false refunds. ## Insurance Agentic AI Use Cases (P&C and Healthcare Payers) ### **7. Claims Adjudication** [**Manual claims processing**](/content/blogs/infographic-supercharging-insurance-claims/index.html) is slow and susceptible to errors, leading to increased operational costs and customer dissatisfaction. Inefficient adjudication processes can also result in delayed settlements. _Challenge:_ Manual, rules-based claims review is slow, error-prone, and expensive. _Agentic AI Advantage:_ Agents can ingest claims, verify documentation, cross-check against policy rules, and adjudicate or escalate with minimal human input. _Potential win:_ U.S. health insurers could cut claims cycle time by 45% and improve straight-through processing by 30%. ### **8. FNOL (First Notice of Loss) Automation** Delays in initiating claims after an incident can erode customer trust and complicate the claims process. Manual FNOL processes are often inefficient and inconsistent. _Challenge:_ Delays in claim initiation cause customer churn. _Agentic AI Advantage:_ Agentic systems capture claim details via voice, email, or chat, verify policy coverage, and triage immediately. _Potential Win:_ Insurers can reduce average FNOL intake from several hours to under 15 minutes. ### **9. Underwriting Automation and Risk Assessment** Insurance underwriting is often slowed by fragmented data sources and manual risk assessments, creating bottlenecks and inconsistencies in pricing and policy issuance. The process is particularly costly when underwriters must manually sift through third-party data, historical claims, and risk models. _Challenge:_ Inefficient underwriting workflows reduce speed-to-quote and increase error risk. _Agentic AI Advantage:_ Agentic systems can aggregate internal and external data, flag risk indicators, generate initial quotes, and prep decisions for underwriter review. _Potential Win:_ Global insurers could see as much as 40% improvement in quote turnaround time and increase underwriting throughput by 35% using agentic risk engines. ## **Next Steps: Changing How Work Gets Done** Agentic automation is not a futuristic concept-it's a present-day advantage. Across healthcare, banking, and insurance, enterprises are leaking billions annually due to slow, manual, and error-prone processes. According to McKinsey, inefficient claims and underwriting processes in Property & Casualty (P&C) insurance can contribute to [**combined ratio losses exceeding 100% in challenging years**](https://www.caqh.org/hubfs/43908627/drupal/explorations/index/2021-caqh-index.pdf), indicating that companies are paying out more in claims and expenses than they are receiving from premiums. The same report reveals that poor automation in financial services can lead to operational inefficiencies, potentially costing firms up to 30% of their operating expenses. The organizations winning today are the ones actively mapping their processes and deploying agentic automation to improve accuracy, speed, and experience.