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Finance & Banking

Fraud Report & Transaction Dispute Intake Chatbot

Let customers report fraud and disputes instantly. NIVA captures structured details, triggers the right workflow by type, and escalates urgent cases with full context.

fraud report chatbot dispute intake bot transaction dispute AI fraud reporting automation chargeback intake chatbot
24/7
Automated Coverage
0
Developers Required
5
Automation Steps
4
Measurable Outcomes
The Problem
Customers reporting suspected fraud or disputing a transaction face phone queues at a stressful moment, and intake details arrive inconsistently, slowing investigation.

How NIVA Handles This Automatically

A fraud-and-disputes persona captures structured report details (transaction, amount, date, reason) via Smart Forms, branches by case type, and fires a webhook to the fraud or disputes workflow, escalating urgent cases immediately with a full context summary. Interactions are logged for the audit trail. Decisioning stays in the connected system; the bot handles structured intake and routing.

Live Conversation Flow
1
Trigger: "report fraud" or "dispute a charge"
2
Knowledge base: reassure and explain next steps
3
Form: transaction, amount, date, reason
4
Condition: branch by case type and urgency
5
Webhook: route to the fraud or disputes workflow with a summary

Step-by-Step: What Happens Inside the Chat

Trigger "report fraud" or "dispute a charge"
Knowledge base reassure and explain next steps
Form transaction, amount, date, reason
Condition branch by case type and urgency
Webhook route to the fraud or disputes workflow with a summary

Before NIVA vs. With NIVA

Real differences your team will feel from day one — not theoretical benchmarks.

Area Before NIVA With NIVA
Reporting speed Phone queues Instant self-service
Intake quality Inconsistent Structured
Urgent cases Wait in queue Escalated with context
Audit record Manual notes Logged automatically

How NIVA Powers This

Under the hood this maps to NIVA's documented engines. The persona engine handles tone and routing for finance & banking, drawing on a library of pre-trained personas so the agent speaks the language of the domain from day one rather than being trained from scratch. The flow engine runs the conditional steps in the sequence shown above, branching on the visitor's answers so each person follows the path that fits their situation. The smart-form engine surfaces structured fields at the moment intent appears, capturing clean data inline instead of bouncing the user to a separate form. Cross-session memory preserves context so returning users are recognised and never asked to repeat themselves. Finally, webhooks push the completed interaction into your systems of record, and per-persona tool calls can read live data from your APIs mid-conversation wherever an endpoint exists. None of these steps requires writing code; they are assembled in the no-code admin and embedded with a single script tag.
Who Is This For

This works in two directions. As a public-facing agent it engages prospects and customers directly on your website or app; as an internal tool it answers staff questions and runs intake against internal systems. The same engines power both, and which direction you deploy depends only on which knowledge sources and systems you connect. Many finance & banking operators start public-facing for the traffic and conversion upside, then reuse the same build internally.

In practical terms, the shift looks like this: on reporting speed, you move from phone queues to instant self-service; on intake quality, you move from inconsistent to structured; on urgent cases, you move from wait in queue to escalated with context; on audit record, you move from manual notes to logged automatically. Each of these is a direct consequence of moving the interaction from a manual, human-gated channel to an always-available conversational one that still escalates to a person when the situation genuinely needs it.

NIVA Engines Persona Engine Flow Engine Smart Forms RAG Knowledge Webhooks
Implementation Note

Implementation reality: the conversational layer, routing, data capture, and system handoff are all within NIVA's documented no-code capabilities. Where this use case reads or writes live data, it assumes the relevant API or webhook endpoint exists on your side; that integration is the one piece worth scoping before launch. Start with the knowledge base and flow, then layer in live API calls once the core experience is proven.

Frequently Asked Questions

What is a fraud report & transaction dispute intake chatbot?
It is a NIVA-powered conversational agent in which fraud-and-disputes persona captures structured report details (transaction, amount, date, reason) via Smart Forms, branches by case type, and fires a webhook to the fraud or disputes workflow, escalating urgent cases immediately with a full context summary. It runs on your website or app and works around the clock without adding headcount.
How does NIVA build this without code?
You select a pre-trained Finance & Banking persona, connect your knowledge sources, and assemble the flow and forms in the no-code admin. The example flow on this page can be replicated step by step, and the bot embeds with a single script tag.
Can it connect to our existing systems?
Yes. Completed conversations fire webhooks into your CRM, booking system, or ticketing tool, and per-persona tool calls can read live data from your APIs during the conversation where an endpoint exists.
How quickly can we go live?
Because the Finance & Banking persona is pre-trained and the engines are no-code, a working version of this use case can be configured and embedded quickly, then refined against real conversations.
Ready to Deploy

Build This for Your Business in Hours, Not Months

No developers. No long setup. NIVA gives you every engine shown on this page — persona routing, flow automation, smart forms, and webhooks — as a ready-to-configure platform.