Fraud Report & Transaction Dispute Intake Chatbot

Overview

Fraud Report & Transaction Dispute Intake Chatbot is a no-code, white-label conversational agent built on NIVA's persona, flow, and smart-form engines. Customers reporting suspected fraud or disputing a transaction face phone queues at a stressful moment, and intake details arrive inconsistently, slowing investigation. With NIVA, 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. The result is a measurable shift from manual, after-hours-limited handling to instant, structured, around-the-clock engagement that feeds directly into your systems.

The problem

Customers reporting suspected fraud or disputing a transaction face phone queues at a stressful moment, and intake details arrive inconsistently, slowing investigation. For finance & banking teams specifically, every hour spent on this manually is an hour not spent on higher-value work, and every unanswered query outside business hours is a lost opportunity that a competitor with instant response will capture.

How NIVA solves it

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. Because the persona is pre-trained for finance & banking and the logic is assembled in a no-code flow, the team owning this does not need engineering support to launch or iterate. Every conversation is logged and attributed, so the same deployment doubles as an insight layer revealing the questions and friction points worth acting on.

Automation 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

Before vs after

How it works under the hood

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 this is 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.