Use Cases

Built for platforms where fraud is a real problem.

If you move money, manage users, or deploy AI that makes decisions — you are a fraud target. Here's how Zarelva's fraud intelligence work maps to five platform types.

🏦

Digital Lenders & NBFCs

Disbursing ₹5Cr–₹500Cr/month with 0–2 fraud FTEs in-house, and under RBI audit pressure.

The Problem

→Synthetic identities passing KYC and cultivating credit history before bust-out
→Application fraud at underwriting — fabricated income, employment, and bank statements
→No documented fraud posture ahead of a fundraise or RBI audit

What Zarelva Does

→Fraud Risk Assessment — attack surface map + signal gap analysis at underwriting
→FPaaS decision-layer ownership across the loan application funnel
→Board-ready Fraud Posture Report for audit and fundraise readiness

Proof: how synthetic identities pass KYC undetected — and the signals that surface them. Read the insight →

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💳

BNPL & Neobanks

UPI, wallets, and instant-checkout flows — where fraud patterns move fastest.

The Problem

→UPI Collect abuse — fraudsters send collect requests disguised as refunds
→SIM swap + account takeover during onboarding or checkout
→Mule account networks moving funds through in hours

What Zarelva Does

→ZSIG Signal Engine — real-time behavioural & velocity signals across 7 fraud rule packs
→Weekly Fraud Summary + pattern review under FPaaS
→Ring detection via the ZIC Investigator Canvas for mule networks

Proof: full forensic dissection of an OLX UPI Collect fraud case, from 67KB of evidence. Read the case study →

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🏧

Payment Platforms

Card testing, refund abuse, and chargeback rings that scale faster than manual review can catch.

The Problem

→Card testing runs disguised as normal low-value transactions
→Refund and chargeback abuse rings sharing devices or payout accounts
→Merchant MCC mismatch — high-risk transactions through legitimate-looking merchants

What Zarelva Does

→ZIC Engine — automated fraud ring detection via entity relationship graphs
→Device Risk SDK at checkout — emulator, rooted device, and remote-control detection
→Rule & scorecard tuning against measurable loss-rate targets

Proof: how the ZIC Investigator Canvas resolves entities and visualises fraud rings. See the ZIC Engine →

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🤖

AI-Native & Agentic Products

Autonomous agents making decisions bypass fraud controls built for human-speed behaviour.

The Problem

→Prompt injection attack surfaces in agent-facing interfaces
→Delegation chain abuse — agent-to-agent handoffs with no accountability trail
→Non-human velocity patterns that human-era fraud rules never anticipated

What Zarelva Does

→AI Agent & Autonomy Risk Review — prompt injection, delegation, and tool-use abuse mapping
→Agent Risk Engine (open source) — 47 signals across five fraud layers
→Layered detection architecture across identity, access, behaviour, transaction, network

Proof: the full AI Fraud Detection Framework — layered signals, scoring, and example scenarios. Read the framework →

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🛒

Marketplaces & SaaS

Buyer/seller trust and API surfaces that scale faster than the fraud controls protecting them.

The Problem

→Seller-side fraud — fake listings, non-delivery, collusive returns
→Account takeovers via credential stuffing and session hijacking
→API abuse — scraping, inventory hoarding, automated account creation

What Zarelva Does

→Fraud Intelligence Architecture Support — signal library & detection design for your product
→Device Risk SDK for account creation and login surfaces
→Fraud Signal Library (open source) — identity, device, behavioural, network, transaction signals

Proof: 7 malicious loan-app APKs identified — full Android forensics and incident timeline. Read the case study →

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Not sure which use case fits your platform?

Take the 90-second Fraud Exposure Assessment, or ask Zara directly — instant answers on pricing, services, and products.

Take the Assessment →