AI agents are the next step in business automation, going beyond basic chatbots and voice tools. To build and scale AI-driven workflows, you need a reliable platform and a clear plan. That’s where Tovie Platform and our team can help. Here are some of the most effective Tovie Platform use cases for banks and insurers, focused on delivering a strong return on investment.
By agentic AI, we mean systems built with AI agents that can manage tasks autonomously with minimal human input.
Imagine an AI agent that summarises client conversations, identifies next steps, and updates CRM or case management systems automatically. Or one that analyses customer data and follows up with clients who may be interested in a new insurance package, sending personalised offers. These are some of the most common Tovie Platform AI use cases in financial services.
Tovie Platform offers a unique no-code/low-code solution. You can create simple agents for routine tasks within departments or more complex multi-agent systems that span the entire organisation.
It is an enterprise-grade platform for building AI agents and multi-agent systems. It combines chat, voice, and action automation, provides enterprise-level security, and pre-built integrations with all systems and channels essential for enterprise clients. Tovie Platform is designed for large organisations with strict infrastructure and data requirements that need to launch and test AI initiatives quickly.
Let’s see some examples of where AI agents can add the most value in financial organisations.
Banks and insurers handle a huge volume of incoming emails every day: claim notifications, policy change requests, KYC documents, broker submissions, customer enquiries, and more. Each message needs to be opened, understood, classified, and processed. This creates a heavy workload, leads to errors and slows down response times.
Processing unstructured data is one of the biggest operational bottlenecks for financial organisations. AI agents can remove this bottleneck. They read, classify, and extract data from inbound emails and route them to the right system in seconds. This reduces manual work and speeds up processing dramatically.
AI can turn unstructured text and attachments into structured data such as policy numbers, claimant details, dates, amounts and broker names. It checks for missing information and routes the case accordingly. The structured data is then added to your CRM, case management system, or core back office. Follow-up actions can happen automatically, such as requesting missing documents, sending an acknowledgement, or escalating complex cases.
65% of large enterprises are accelerating Intelligent Document Processing projects in 2025. The top reported benefit was a 50% reduction in processing time.
Impact and ROI:
- Up to 40% faster processing of inbound emails
- No manual re-entry errors
- Better SLA compliance and accuracy
- Higher customer satisfaction
- Up to 30–50% cost savings and up to 50% faster processes
Other AI agent use cases:
1. A claim arrives by email. An AI agent extracts key details, updates your system, and sends a status update to the customer.
2. A broker requests a policy amendment. The agent identifies the document type, updates policy details, and alerts the underwriter.
3. A customer submits identity documents for KYC. The agent validates and extracts the data, flags missing files, and sends the case to compliance.
4. A client asks for an update on a claim or payout. The agent retrieves the latest information and replies instantly.
5. A regulator or partner sends a compliance notice. The agent logs it securely, updates the audit trail, and notifies the compliance team so nothing is missed.
Daily calls create another steady flow of follow-up work. Teams spend time writing summaries, updating CRM records or providing follow-ups. Research by McKinsey confirms that AI in contact centres can cut low-value workloads and improve accuracy.
AI agents can handle post-call tasks from start to finish. They summarise conversations, identify next steps, and update CRM or case management systems automatically. The agent processes call transcripts and extracts structured data, including key topics, sentiment, promised actions, and details such as policy numbers or claim references. It can then update systems, create tasks, send follow-up emails or SMS, alert teams, or log audit entries.
Impact and ROI:
- Up to 50% less manual after-call work
- More accurate summaries and workflow updates
- Faster response and follow-up times
- Stronger compliance and audit readiness
- Lower agent burnout and better morale
- Rich analytics for QA, coaching and CX improvement
More examples of agentic AI use cases:
1. A policyholder calls about a claim. The agent extracts claim details, updates the claims system, and assigns a follow-up to the adjuster.
2. A customer asks about renewing their policy. The agent identifies renewal intent, updates CRM data, and sends a personalised offer.
3. A bank client disputes a transaction. The agent captures sentiment, logs the issue, flags escalation, and alerts a supervisor.
4. A mortgage applicant provides updated documents. The agent records the details, updates the loan system, and triggers a reminder for the next step.
5. A compliance-related call comes in. The agent logs actions in the audit system and notifies the compliance team.
Finance teams spend nearly half of their time on routine work. Banks and insurers’ teams manage numerous repetitive processes, including renewals, KYC updates, payment reminders, and reporting. AI agents can automate these recurring workflows and keep operations consistent and on schedule.
AI can plan and run recurring tasks, send notifications, update systems, escalate cases, and complete follow-ups. It can pull data from Excel files, SQL databases, BI tools, or core systems to prepare reports and documents. It can also classify customer cases, detect risks, and select the next best action.
Impact and ROI:
- 35–50% reduction in manual work across recurring processes
- Lower lapse rates through proactive retention
- Better compliance through automated KYC refresh and reporting
- Faster and more accurate reporting with scheduled data collection
- Significant cost savings by freeing teams for higher-value work
Examples of recurring tasks automated by AI:
1. Sends personalised renewal reminders and follows up with non-responders.
2. Identifies at-risk customers and triggers targeted retention offers.
3. Performs annual KYC refresh, requests missing documents, and flags anomalies.
4. Runs payment reminder workflows, calculates due amounts, sends alerts, and escalates failed transactions.
Want to see how other teams automate at scale? Visit our page dedicated to financial services and AI business use cases:
- AI-powered policy renewals
→ Increase revenue by up to 20% with personalised cross-sell offers.
→ Automate up to 1 million interactions a day and reduce costs by 50%.
- Enterprise voice AI and support automation
→ Reduce routine enquiries by up to 90% and stay fully compliant.
- Enterprise-ready integrations with channels and apps. From RAG and databases to CRM systems, the platform supports multichannel workflows and flexible communication across a wide range of enterprise AI use cases.
- Seamless integration with leading LLMs. Connect to OpenAI, Anthropic, Gemini, and popular messaging platforms like Teams and Slack.
- Built-in FinOps tools. Track agent performance, control costs, analyse user behaviour, and get insights that help you improve processes continuously.
- Pro-code environment. Get full control when creating custom AI agents with deep integration into internal corporate systems.
Security and data protection
Tovie AI agents and solutions are designed for financial services, where privacy and security are essential. Every stage of development and deployment follows strict information security standards to protect your organisation and its customers.
- IBM Cloud for Financial Services validated
- Personal data masking in dialogue logs and storage
- Regular code and infrastructure security audits
- User and group-level access control for full visibility and governance
- Compliance with GDPR, Cyber Essentials Plus, SOC 2 Type 1, PII requirements, and internal corporate policies
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