Skip to content
Zentis AI
Request a demo
Zentis AI
Industries
Banking & creditSOUL co-pilot, retail credit, onboarding.InsuranceFNOL, claims triage, underwriting.Finance, risk & voiceForecasting, reserving, voice agents.Insurance BrokerageEvery quote and every renewal, ready before the client starts wondering.
Products
Zentis AnalyticsForecasting, reserving, and regulatory reportingAuditOSBank, e-commerce, Shariah, and claims audit.FAMIend-to-end motor insurance automationZentis BinderDefence file assembly, live as the case runs.
Resources
NewsProduct news and conference write-ups.ArticlesLonger pieces on AI in regulated industry.Events & webinarsWhere to find us in person.UsecasesA library of reusable, editable templates.
Company
AboutWho we are and why the harness is the product.PartnersTechnology, consulting, and reseller partners.ContactTalk to the team, or book a working session.CareersJoin us
Platform
ZaraZara is where a workflow starts, Zara builds the team.Zen StudioZen Studiois where engineering opens it upZen PilotZen Pilot is where it actually runs
Request a demo

Stay Ahead with Zentis AI Insights

Subscribe to receive the latest updates straight to your inbox

Zentis AI

An enterprise-grade agentic platform for banking, insurance, and audit. Incubated by Techvantage.ai.

Platform

ZaraSPARZen StudioZen Pilot

Solutions

Audit & complianceInsuranceBanking & creditFinance, risk & voice

Trust

The defence fileSecurityAssuranceRegulatory packs

Resources

NewsArticlesEvents & webinarsClassic

Company

AboutPartnersContactCareers
© 2026 Zentis AI. All rights reserved.info@zentis.aiLondon, England
Anthropic Partner NetworkNVIDIA InceptionTechvantage.ai, Deloitte Technology Fast 50
← All use cases18 / 20
Banking · Global

Fraud investigation for a retail bank

A ten day case could close in one, without needing a senior investigator every time.

Built with a Head of Fraud & Financial Crime in mindFraud investigation for a retail bank — workflow screenshot

The kind of team this fits: a retail bank fraud and financial crime function, roughly 180 cases a month, mix of transaction and application fraud.

The morning before

180 cases a month, a ten day median to a closure recommendation, and most of them end up escalated to a senior investigator who re-reads the same evidence a junior handler already pulled together the week before.

The morning before

How this could run

Not a feature list. The actual sequence, from the moment the case lands to the moment someone signs it.

  1. 01

    Evidence gathers itself across systems

    Transaction history, account data, and external watchlists are pulled together automatically.

  2. 02

    Pattern analysis runs in parallel

    Behavioral patterns and fraud indicators are assessed across the case at once, not sequentially.

  3. 03

    A closure recommendation arrives documented

    The case file arrives with the evidence, the analysis, and the recommendation, ready for review.

Image, inside Zen Studio

Screenshot of a Zen Studio canvas configured for a workflow like this one.

What doesn't change

A senior investigator still reviews and signs every closure recommendation. What changes is how much of the file they have to read before they can.

What this could mean

Illustrative, based on the shape of a workflow like this one. The real number depends on the case, the team, and how it's configured.

10 days → 1 dayCase flag to closure recommendation
72%Cases closed without senior escalation
Image, inside Zen Pilot

Screenshot of the Zen Pilot dashboard or explainability panel for a run of this workflow.

Where this lives in the platform

For a head of fraud & financial crime, a workflow like this starts as a conversation with Zara, describing the process in plain language and getting a first draft of the team back in the same session. Zen Studio is where engineering opens that draft, sets the guardrails node by node, and versions it before it goes live. Zen Pilot is where it actually runs, and where anyone can watch a case move through it in real time, node by node, with the reasoning attached.

Scale this is modelled on

Modelled at 180 cases a month

See a workflow like this built and running, live.Request a demo
← Previous use caseAll use casesNext use case →