11 AI Support Platforms That Safely Automate Banking and Insurance Conversations [2026 Guide]

Table of Contents

• Why Regulated Customer Support Is a Different Problem

• What to Evaluate in an AI Support Platform for Banking and Insurance

• 11 Best AI Support Platforms for Regulated Industries [2026]

• Platform Summary Table

• How to Choose the Right Platform for Your Compliance Posture

• Implementation Checklist for Regulated Deployments

• Final Verdict

Why Regulated Customer Support Is a Different Problem

The average cost of a financial services data breach hit $6.08 million in 2024, the second-highest of any industry tracked by IBM. For an insurance carrier, a single mishandled PII leak can trigger state-by-state notification obligations across 50 jurisdictions, plus class-action exposure. AI deployed without controls does not reduce that risk. It accelerates it.

Regulators have noticed. The CFPB issued guidance in 2023 warning that chatbots making errors on consumer financial products can constitute UDAAP violations. The NAIC Model Bulletin on AI now applies in 24 US states, requiring insurers to document model governance, bias testing, and explainability. The EU AI Act classifies credit scoring and insurance underwriting as high-risk systems with mandatory conformity assessments.

Most general-purpose AI support tools were built for ecommerce returns or SaaS password resets. Drop them into a bank and you get hallucinated rate quotes, unredacted account numbers in vendor logs, and zero audit trail when examiners arrive. This guide compares 11 vendors that have specifically engineered for the requirements of banking, insurance, and adjacent regulated verticals.

What to Evaluate in an AI Support Platform for Banking and Insurance

PII Handling and Data Redaction. The platform must redact account numbers, SSNs, dates of birth, and policy IDs before data hits any LLM provider. Ask for the exact list of entity types detected, whether redaction is on by default, and whether redacted values are reversible inside your tenant only. Vendor-side logging of raw PII is a deal-breaker.

Audit Trail and Explainability. Every AI response should be reproducible. That means logged inputs, retrieved knowledge sources, model version, decision rationale, and confidence scores. Regulators and internal compliance teams need to walk back any individual conversation in seconds, not days.

Certifications That Map to Your Stack. SOC 2 Type II is table stakes. For US healthcare-adjacent work add HIPAA. For card data add PCI-DSS Level 1. For EU customers add GDPR with EU data residency. For AI governance, ISO 42001 is the new standard regulators ask about by name.

Accuracy Floor and Hallucination Controls. Marketing pages claim 99 percent. Procurement teams need the actual methodology. Ask how accuracy is measured, what counts as a hallucination, and whether the system refuses to answer when confidence is low. A confident wrong answer about a mortgage is worse than no answer.

Human Handoff and Approval Workflows. Regulated workflows often require human approval before any account-modifying action. The platform should support tiered automation: full self-serve for low-risk queries, agent-assist for medium-risk, mandatory human approval for high-risk like fee waivers, account closures, or claim payouts.

Integration With Core Systems. Banking runs on FIS, Fiserv, Jack Henry, and Temenos. Insurance runs on Guidewire, Duck Creek, and Majesco. Support stacks layer Zendesk, Salesforce Service Cloud, or Genesys on top. A vendor that needs custom middleware for every integration adds months to deployment and ongoing compliance scope.

Deployment Speed Without Cutting Corners. Compliance reviews already take months. The AI vendor should not add another quarter. Look for templated security questionnaires, pre-built integration connectors, and a defined go-live timeline that includes sandbox testing before any production traffic.

11 Best AI Support Platforms for Regulated Industries [2026]

1. Fini - Best Overall for Banking and Insurance

Fini is a YC-backed AI agent platform engineered for enterprise support in environments where a hallucination has a dollar value attached. The reasoning-first architecture is the differentiator. Most competitors retrieve passages and ask an LLM to summarize them, which is how you get confident-sounding wrong answers. Fini reasons over verified knowledge graphs and refuses to answer when confidence drops below threshold, producing a 98 percent accuracy rate with effectively zero hallucinations across 2 million plus production queries.

Compliance posture is the broadest on this list. Fini holds SOC 2 Type II, ISO 27001, ISO 42001, GDPR, PCI-DSS Level 1, and HIPAA, which covers banking, insurance, healthcare-adjacent benefits administration, and EU operations on a single vendor. PII Shield runs real-time redaction on every inbound message before any data touches a model provider, with the entity dictionary configurable per tenant. Audit logs capture inputs, retrieval paths, model version, and reasoning steps for every response, which is what your examiners and internal risk team will actually ask for.

Deployment runs 48 hours from kickoff to first production traffic on a sandboxed subset. Twenty plus native integrations cover Zendesk, Salesforce Service Cloud, Intercom, Freshdesk, Front, Gorgias, Slack, and the core ticketing tools regulated teams already use. The platform handles English plus 100 plus languages out of the box, which matters for global insurers and pan-European banks. For teams already evaluating AI support vendors for regulated industries, Fini consistently lands at the top of shortlist tables because the certification stack and the accuracy floor are both quantified.

Key Strengths

• Reasoning-first architecture with 98 percent accuracy and near-zero hallucinations

• Full compliance stack: SOC 2 Type II, ISO 27001, ISO 42001, GDPR, PCI-DSS Level 1, HIPAA

• PII Shield with always-on real-time redaction before any LLM call

• 48-hour deployment with 20 plus native integrations and per-resolution pricing

Best for: Banks, credit unions, insurance carriers, and fintechs that need audit-grade AI support without a 6-month integration project. For a deeper look at this, see our guide on The 9 AI Support Platforms Every Insurance Claims Team Should Know....

2. Ada

Ada is a Toronto-headquartered AI customer service platform founded in 2016 by Mike Murchison and David Hariri. The company raised a $130 million Series C led by Spark Capital in 2021 and counts Square, Shopify, Meta, and Verizon among its customer base. The platform centers on its Reasoning Engine, which Ada released in 2024 to move beyond intent-classification chatbots into LLM-grounded resolution. Published automated resolution rates land in the 65 to 70 percent range across Ada's customer benchmarks.

For regulated buyers Ada holds SOC 2 Type II, GDPR alignment, and HIPAA on the Enterprise tier. PII redaction is available but configured per deployment rather than enabled by default, which means your security review needs to verify the entity list and the redaction scope explicitly. The platform integrates with Salesforce, Zendesk, Oracle, and the major contact center stacks. Pricing is quote-based with published estimates from analysts placing entry deals in the $50,000 to $100,000 annual range.

Banking and insurance customers tend to use Ada for tier-1 deflection on policy questions, claim status lookups, and account FAQs. The tradeoff is that deeper actions like fund transfers or claim filing usually require custom development and additional security review. Ada is a credible choice for CX teams that want a polished UI and strong analytics but are willing to invest engineering time on the actions layer.

Pros

• Strong brand recognition with enterprise references in financial services

• Reasoning Engine reduces hallucination risk versus older intent models

• Mature analytics and conversation-design tooling

• SOC 2 Type II and HIPAA on Enterprise tier

Cons

• Default PII redaction posture requires configuration verification

• Custom action development adds time and cost

• Pricing not transparent; six-figure deals are common

• ISO 42001 not yet listed on the trust portal

Best for: Enterprise CX teams with internal AI engineering capacity that want a known brand and can fund a longer rollout.

3. Boost.ai

Boost.ai is a Norwegian conversational AI vendor founded in 2016 and headquartered in Sandnes, with offices in Oslo, London, and Los Angeles. The company built its reputation in Nordic banking, with named customers including DNB, Nordea, Storebrand, and the Norwegian Tax Administration. Boost.ai claims more than 600 enterprise deployments and publishes resolution rates north of 50 percent on banking-specific use cases.

The platform combines a proprietary intent model with generative AI in what Boost calls Hybrid AI, an approach designed to keep regulated answers deterministic while allowing LLM flexibility on long-tail queries. Certifications include SOC 2 Type II, ISO 27001, and GDPR with EU data residency available on-region, which is meaningful for banks subject to DORA and EBA outsourcing guidelines. PII redaction is supported and the platform offers on-premises deployment for institutions that cannot use multitenant SaaS.

Boost.ai's banking domain expertise shows up in pre-built intent libraries for retail banking, wealth, and insurance, which compress time-to-value compared to starting from zero. The tradeoff is that the platform skews European and the US partner ecosystem is thinner than Ada or Forethought. For teams comparing HIPAA-compliant AI support options against European banking specialists, Boost.ai is a strong contender on the EU side.

Pros

• Deep banking and insurance vertical expertise with named Nordic references

• On-premises deployment option for strict data residency requirements

• Pre-built intent libraries for retail banking and insurance

• Hybrid AI approach balances determinism and flexibility

Cons

• Thinner US partner ecosystem and reference base

• Hybrid model requires more conversation design effort than pure LLM agents

• HIPAA not standard; available only on specific configurations

• Pricing is enterprise-only with no published self-serve tier

Best for: European banks and insurers with strict data residency requirements and the appetite to build with a vendor that knows the vertical cold.

4. Forethought

Forethought is a San Francisco AI support platform founded in 2017 by Deon Nicholas, Sami Ghoche, and Connor Folley. The company raised a $65 million Series C in 2022 led by Steadfast Capital Ventures and serves customers including Carta, Upwork, and Instacart. The product line includes Solve for chat deflection, Triage for ticket routing, and Assist for agent copilot, with SupportGPT as the underlying generative layer.

Compliance posture includes SOC 2 Type II and HIPAA, with GDPR support and configurable PII redaction. Forethought publishes a resolution rate of 30 to 40 percent for self-service deflection on most deployments, with higher rates on simpler FAQ-heavy workloads. The platform's strength is the agent-assist tier: SupportGPT drafts responses inside Zendesk and Salesforce for human agents to review and send, which fits regulated workflows where final responses must be human-approved.

For banking and insurance, Forethought is most often deployed on the triage and assist tiers rather than full automation. That conservative posture aligns well with compliance teams that are not ready to let AI send unreviewed customer messages on regulated topics. The downside is that the deflection ceiling is lower than reasoning-first platforms, and you still need human capacity for the assisted queue.

Pros

• Strong agent-assist tier fits regulated human-in-the-loop workflows

• Native Zendesk and Salesforce integrations with low-friction setup

• SOC 2 Type II and HIPAA available on Enterprise tier

• Triage product reduces routing errors on high-volume queues

Cons

• Deflection rates trail reasoning-first competitors

• PII redaction configured per deployment, not on by default

• ISO 27001 and ISO 42001 not listed on trust center

• Pricing opaque; per-seat assist licensing can scale faster than per-resolution

Best for: Mid-market insurance and banking ops teams prioritizing agent productivity over full self-service automation.

5. Kore.ai

Kore.ai is an Orlando-headquartered enterprise conversational AI platform founded in 2014 by Raj Koneru. The company raised a $150 million Series D in 2024 led by FTV Capital and Nvidia, valuing the business at over $750 million. Kore.ai's BankAssist and AgentAssist products target tier-1 banks specifically, with customers including HDFC Bank, PNC, and AT&T.

The platform offers SOC 2 Type II, HIPAA, ISO 27001, and GDPR certifications, with on-premises and private cloud deployment options for institutions with strict data localization needs. Kore.ai supports a wide range of LLM providers including OpenAI, Anthropic, Google, and Cohere, with the ability to bring your own model. PII redaction is part of the platform's GenAI guardrails layer along with hallucination detection and bias monitoring.

Kore.ai's strength is the breadth of the platform. It handles voice, chat, email, and agent-assist on a single stack, which appeals to large banks that want to consolidate vendors. The tradeoff is complexity. Implementations are typically 4 to 6 months and require certified Kore.ai partners or internal conversational AI engineers. For institutions that already have AI platform teams, Kore.ai is a serious contender; for teams that want fast time-to-value, the learning curve is steep.

Pros

• True enterprise platform with voice, chat, and agent-assist on one stack

• On-premises and private cloud deployment for strict data residency

• Broad LLM flexibility with bring-your-own-model support

• Named tier-1 bank references including PNC and HDFC

Cons

• 4 to 6 month typical implementation timeline

• Requires certified partner or internal AI engineering capacity

• Pricing structured for seven-figure enterprise commitments

• UI and admin tooling are dense compared to modern competitors

Best for: Tier-1 banks and large insurers consolidating multiple conversational AI vendors onto a single enterprise platform.

6. Cognigy

Cognigy is a Düsseldorf-based conversational AI vendor founded in 2016 by Philipp Heltewig, Sascha Poggemann, and Benjamin Mayr. The company raised a $100 million Series C in 2024 led by Eurazeo and serves customers including Lufthansa, Bosch, Frontier Airlines, and Toyota. Cognigy.AI is the core platform with Voice Gateway adding telephony and Insights providing analytics.

The platform holds ISO 27001, SOC 2 Type II, GDPR with EU data residency, and offers HIPAA on specific deployments. PII redaction is supported through the Sensitive Data Protection feature, with configurable entity types and reversible tokenization. Cognigy supports more than 100 languages natively and integrates with Genesys, Avaya, NICE, and the major CCaaS platforms, making it a strong fit for contact center modernization in European insurance and banking.

Cognigy's positioning leans toward voice and contact center transformation rather than pure web chat. For insurers running large inbound voice operations on claims, FNOL, or policy servicing, Cognigy's Voice Gateway is competitive with much larger IVR vendors. The platform is less commonly chosen for pure-play digital support and is rarely the cheapest option in a competitive bid.

Pros

• Strong voice and contact center modernization story

• ISO 27001, SOC 2 Type II, GDPR with EU data residency

• Native integrations with Genesys, Avaya, NICE, and major CCaaS

• 100 plus languages with strong European footprint

Cons

• Voice-heavy positioning may oversize digital-first deployments

• HIPAA not standard across all deployments

• Implementation requires Cognigy-certified consulting partners

• Pricing typically enterprise-tier only

Best for: European insurers and banks modernizing inbound voice operations alongside digital channels.

7. Aisera

Aisera is a Palo Alto AI service management platform founded in 2017 by Muddu Sudhakar, who previously sold Caspida to Splunk and Cetas to VMware. The company raised a $90 million Series D in 2022 led by Goldman Sachs and serves customers including McAfee, Workday, and Zoom. The product line spans AI for IT, HR, customer service, and operations, with AiseraGPT as the generative layer added in 2023.

Aisera holds SOC 2 Type II, ISO 27001, HIPAA, and GDPR certifications. The platform's strength is internal employee support and IT service desk automation, where it has the deepest references. On external customer support for banking and insurance, Aisera competes but is more often selected for combined internal-plus-external use cases. PII redaction is part of the AI Trust Layer, which also covers prompt injection defense and output filtering.

For regulated institutions evaluating a platform that handles both employee IT support and customer-facing service, Aisera's unified architecture is genuinely differentiated. The tradeoff is that customer-facing deployments typically need more configuration than vendors that purpose-built for external CX. Pricing is enterprise-only with deals typically starting in the mid-six figures annually.

Pros

• Unified platform for internal IT and external customer support

• SOC 2 Type II, ISO 27001, HIPAA, GDPR

• AI Trust Layer covers PII, prompt injection, and output filtering

• Strong references in enterprise IT service management

Cons

• Customer-facing CX is secondary to IT service desk strength

• Enterprise-only pricing with no self-serve tier

• Implementation skews longer than CX-focused competitors

• Less depth on banking-specific use cases than vertical specialists

Best for: Large enterprises consolidating internal IT support and external customer service on a single AI platform.

8. Interactions

Interactions is a Franklin, Massachusetts conversational AI vendor founded in 2004, making it one of the older companies on this list. The platform is built on Adaptive Understanding, a hybrid AI-plus-human approach that routes low-confidence interactions to human "Intent Analysts" in real time, then feeds those resolutions back into the model. Customers include Citi, Humana, Hyatt, and Shutterfly, with deep references in banking, healthcare, and insurance.

Interactions holds SOC 2 Type II, PCI-DSS, and HIPAA, with the human-in-the-loop architecture providing an extra layer of safety for regulated workflows. The platform is heavily voice-oriented and is most commonly deployed as an IVR replacement or augmentation, handling call volumes that would be cost-prohibitive for pure human staffing. Resolution rates are competitive in the 60 to 70 percent range for well-tuned use cases.

The unique value is the human safety net. For banks and insurers running voice support where misunderstanding a customer's intent has high consequence, Interactions' Intent Analysts catch cases pure AI would mishandle. The tradeoff is that this architecture is voice-first and chat or messaging deployments are not the platform's strength. Pricing typically reflects voice-minute consumption plus platform fees.

Pros

• Battle-tested in regulated voice deployments with 20 plus years in market

• Adaptive Understanding human-in-the-loop reduces high-cost AI errors

• SOC 2 Type II, PCI-DSS, HIPAA

• Strong references in tier-1 banking and health insurance

Cons

• Voice-first; chat and messaging are secondary

• Human-in-the-loop model can add latency on edge cases

• ISO 42001 not listed; less visible on newer AI governance standards

• Older product UX compared to recent entrants

Best for: Tier-1 banks and health insurers automating high-volume voice channels with a human safety net.

9. Posh AI

Posh is a Boston-based conversational AI vendor founded in 2018 by MIT alumni Karan Kashyap and Matt McEachern. The company is purpose-built for community and regional banks and credit unions, with more than 90 financial institution customers including Bangor Savings Bank, Members 1st FCU, and Affinity Plus FCU. Posh raised a $27.5 million Series A in 2022 led by Canapi Ventures.

Posh holds SOC 2 Type II and aligns with NIST and FFIEC guidance for financial institutions. The platform offers digital banking assistants for web and mobile, voice assistants for inbound call centers, and an internal employee assistant for branch staff. Native integrations cover the core banking platforms community banks actually use, including Jack Henry, Fiserv DNA, and Symitar, which is rare among generalist competitors.

The vertical focus is the differentiator. Posh's intent libraries cover credit union and community bank use cases out of the box, which means a 200-person credit union does not need a six-figure conversation design project to go live. The tradeoff is that the platform is narrowly scoped. Insurance carriers, fintechs, or large national banks usually need broader capability than Posh ships.

Pros

• Purpose-built for community banks and credit unions

• Native integrations with Jack Henry, Fiserv DNA, Symitar

• SOC 2 Type II with NIST and FFIEC alignment

• Faster time-to-value for in-segment institutions

Cons

• Narrow scope; not a fit for insurance or large national banks

• Smaller engineering team than horizontal competitors

• HIPAA and ISO 42001 not standard offerings

• Limited multilingual support compared to global platforms

Best for: US community banks and credit unions wanting a vendor that knows the core banking stack natively.

10. Yellow.ai

Yellow.ai is a San Mateo and Bangalore-based conversational AI vendor founded in 2016 by Raghu Ravinutala. The company raised a $78.15 million Series C in 2022 led by WestBridge Capital and serves customers including HDFC Life, Bajaj Allianz, Royal Bank of Scotland, and Domino's. The platform spans chat, voice, and email automation with a low-code conversation designer aimed at broad enterprise adoption.

Compliance certifications include SOC 2 Type II, ISO 27001, HIPAA, and GDPR. Yellow.ai's Dynamic Automation Platform supports more than 135 languages and integrates with Salesforce, Zendesk, Genesys, and the major CRM and CCaaS systems. The vendor has notable depth in Indian and Southeast Asian financial services, where it competes against both local players and global names like Kore.ai.

For multinational insurers and banks with significant operations across Asia-Pacific plus EMEA, Yellow.ai's geographic and language coverage is genuinely useful. The tradeoff is variability. Implementation quality depends heavily on which Yellow.ai regional team is assigned, and US-based reference depth is thinner than the platform's APAC footprint. Pricing is enterprise-quote based.

Pros

• Strong APAC and EMEA footprint with named insurance references

• 135 plus languages with native multilingual support

• SOC 2 Type II, ISO 27001, HIPAA, GDPR

• Voice, chat, and email on a single platform

Cons

• US reference base thinner than APAC

• Implementation quality varies by regional delivery team

• ISO 42001 not listed on trust portal

• Low-code designer can still require professional services for complex flows

Best for: Multinational insurers and banks needing broad language coverage across APAC and EMEA.

11. Capacity

Capacity is a St. Louis AI support automation platform founded in 2017 by David Karandish and Chris Sims, the founders of Answers.com. The company has raised more than $80 million across multiple rounds and serves customers across financial services, healthcare, and insurance, including USA Mortgage, Centene-adjacent benefits administrators, and several regional banks.

Capacity holds SOC 2 Type II and HIPAA, with a focus on knowledge automation, helpdesk deflection, and workflow automation across email, chat, SMS, Slack, and Teams. The platform's Concierge product handles customer-facing support while Helpdesk handles internal employee questions, both backed by the same knowledge graph. PII handling is configurable, and the platform supports approval workflows for sensitive actions.

For mid-market banks, credit unions, and insurance carriers that need both customer and employee automation without the complexity of a Kore.ai or Aisera, Capacity is a credible middle option. The tradeoff is that the company's marketing and product roadmap span many use cases beyond regulated customer support, so depth on banking-specific scenarios is less than vertical specialists like Posh or Boost.ai.

Pros

• Combined customer and employee support automation on one platform

• SOC 2 Type II and HIPAA available

• Mid-market friendly pricing and faster implementation than tier-1 platforms

• Strong knowledge graph foundation reduces hallucination risk

Cons

• Broad product scope can dilute focus on regulated banking depth

• ISO 27001 and ISO 42001 not standard

• Voice capability less mature than dedicated voice AI vendors

• Public benchmarks on accuracy and resolution rates are limited

Best for: Mid-market regulated institutions wanting combined customer and internal employee AI on a single, mid-priced platform.

Platform Summary Table

How to Choose the Right Platform for Your Compliance Posture

1. Start with your certification floor, not the feature list. Write down the certifications your security and compliance teams will require before any pilot. SOC 2 Type II is universal. If you handle health data add HIPAA. If you handle cards add PCI-DSS. If you operate in the EU add GDPR with regional data residency. Eliminate any vendor that does not hold these on day one.

2. Demand the actual accuracy methodology. Every vendor claims high accuracy. Ask how it is measured, on what dataset, with what definition of correct, and request to reproduce the test on your own ticket corpus. A vendor unwilling to run your test is a vendor protecting marketing numbers.

3. Audit the PII data flow end to end. Trace exactly where customer messages go from arrival to response. Which entities are redacted, by which engine, before which call to which LLM provider, with what logging on each hop. Get the answers in writing and have your security team review before pilot.

4. Define the automation tier before the demo. Decide which workflows are full self-serve, which are agent-assist, and which require mandatory human approval. The vendor should support all three tiers natively, not as bolt-on customization. Tier-mismatch causes most regulated AI failures.

5. Cost the full deployment, not the platform fee. Add integration engineering, conversation design, compliance review, security testing, change management, and ongoing tuning. Vendors with 48-hour deployment and pre-built integrations look more expensive on the line item and cheaper on the full deployment.

6. Validate the audit trail with your examiners in mind. Have your internal audit or compliance team walk through three test conversations end to end. If they cannot reproduce why the AI said what it said, with sources and confidence, the platform will fail external examination.

Implementation Checklist for Regulated Deployments

Pre-Purchase

• Compliance team has reviewed and approved required certifications

• Security has audited the vendor's data flow and PII handling

• Legal has reviewed DPA, BAA where required, and subprocessor list

• Procurement has benchmark pricing from at least 3 vendors

Evaluation

• Vendor has run accuracy test on your own anonymized ticket corpus

• Audit log walkthrough completed with internal audit team

• Tier definitions documented: self-serve, agent-assist, human-approval

• Integration list confirmed against your core systems

Deployment

• Sandbox environment with synthetic PII test data live

• Pilot scope limited to non-account-modifying intents

• Human approval workflow tested for all high-risk paths

• Escalation paths to live agents tested under load

Post-Launch

• Weekly accuracy and confidence-distribution review for first 90 days

• Monthly compliance and audit log review with risk team

• Quarterly model and knowledge base refresh cycle defined

• Annual third-party audit of AI governance under ISO 42001 framework

Final Verdict

The right choice depends on your institution size, regulatory exposure, and how much engineering capacity you can dedicate to deployment.

Fini is the strongest overall choice for banks, insurers, and fintechs that want the broadest compliance stack on the market with measurable 98 percent accuracy and 48-hour deployment. The reasoning-first architecture, always-on PII Shield, and per-resolution pricing make it the lowest-risk path to production AI support in a regulated environment.

For tier-1 enterprises consolidating multiple vendors, Kore.ai and Aisera are credible if you have the engineering team and the budget for 4 to 6 month implementations. For European banks and insurers, Boost.ai and Cognigy bring deep regional expertise and on-region data residency. For community banks and credit unions, Posh AI's native integrations with Jack Henry and Fiserv make it a faster path than horizontal platforms. For voice-heavy regulated operations, Interactions' human-in-the-loop architecture is hard to beat on safety. For multinational APAC and EMEA insurance, Yellow.ai's language coverage is genuinely useful.

If you want to see how the reasoning-first approach handles your actual edge cases, bring your 50 messiest banking or insurance tickets, including the ones with redacted PII, account numbers, and policy details, and book a Fini demo to watch the platform reason through them in real time against your own compliance requirements.