Industries · Banking · Malaysia + Singapore
Every day, ChatGPT, Gemini, and Google AI Overview answer questions about your bank. Some answers are incomplete, outdated, or wrong. Lawnise tells you what they're saying — and helps you correct it.
100 visibility checks per month. No credit card.
For procurement and Enterprise scope.
The Public AI problem for banking
Public AI answer engines — ChatGPT, Gemini, Google AI Overview, Copilot, Perplexity — answer questions about your bank every day. Some of those answers are incomplete, outdated, or wrong: outdated rates, incomplete product disclosures, missing PIDM coverage detail, branch and product confusion. Wrong answers travel as if they were yours.
Common factual gaps for banking-related answers include deposit insurance scope (PIDM coverage limits stated incorrectly or omitted), Islamic vs conventional product confusion, current rate accuracy (especially fixed deposit and home loan headline rates), and branch / channel availability. Different engines fail differently.
For a regulated bank, what an answer engine says about your products is functionally what a customer hears about you. Misstatements travel through customer-service inquiries, complaints, social media, and — increasingly — into regulator visibility. The accuracy gap is a reputation, distribution, and compliance issue at the same time. Most banks today have no systematic way to see what these engines are saying.
What Lawnise does for banking
Lawnise is AI visibility, accuracy, reputation, and risk infrastructure — built for regulated enterprises that need to see and correct what public AI says about them. Four capability dimensions, mapped here to the questions that matter most for banks.
Lawnise runs visibility checks against the supported public AI engines on a continuous schedule. You see, prompt by prompt, which engines are talking about your bank, what share of voice you have versus competitors, and where your brand is being missed. For banking specifically: rate-comparison prompts, product-recommendation prompts, deposit-insurance prompts, and branch-locator prompts are tracked as a default sector pack.
Every claim a public AI engine makes about your bank is checked against your stored brand reference documents — product disclosure sheets, current rate cards, regulatory filings, public statements. Where the engine answer disagrees with your reference, the discrepancy is flagged with the exact engine response, the supporting source, and a hash-linked evidence trail. Fact verifications are part of the Lawnise Monitor and Enterprise plans.
Reputation analysis tracks how public AI answers describe your bank's posture on the issues that customers ask about: digital experience, regulatory standing, fee competitiveness, complaint handling, sustainability framing. Sentiment shifts are tracked over time and against your competitor set. Reputation analysis is included in Monitor's combined visibility + reputation scan quota.
Where engine answers create compliance exposure (incorrect product disclosures, misstatements about regulated terms), Lawnise Enterprise customers can generate compliance coverage tailored to their sector and regulators, export the full evidence-to-claim ledger for audit, and use the right-to-reply workflow plus correction-notice publishing to push corrected answers back into the public record.
Capability detail by dimension lives at /platform; sector-relevant evidence lives in the next section.
Lawnise Trust Index
The Lawnise Trust Index is an independent benchmark of how accurately public AI engines describe regulated institutions. Trust Index coverage and methodology are in preparation for the P4 Authority Launch. Banks interested in inclusion in the launch sample frame can Book Briefing.
Who this is for
Lawnise is built for the cross-functional team that owns external-AI exposure inside a bank.
ACISO / Head of Information Security
What your bank's customers hear from public AI engines is part of your external attack surface. Lawnise gives you continuous visibility plus the evidence trail your security team needs to scope and respond.
BCRO / Head of Risk
Inaccurate AI answers about regulated products are a live operational and compliance risk. Lawnise quantifies that exposure across engines, correlates it to regulatory framework, and tracks correction over time.
CHead of Compliance
When public AI engines misstate your product disclosures, you need a defensible record of what was wrong, when, on which engine, and what corrective action was taken. The Lawnise evidence-to-claim ledger is built for that record.
DHead of Communications / Brand
Reputation now lives partly in what AI engines say about you. Lawnise tracks how engines characterise your bank versus your competitors and gives your team the right-to-reply workflow when corrections are needed.
Product proof
Three illustrative scenarios showing how Lawnise surfaces and corrects engine answers about a bank. Fixture institution; numbers and prompts are illustrative, not customer data.
Scenario 01 · Visibility check pattern
“What is the current 12-month fixed deposit rate at BankCo MY?”
| Engine | Response pattern | Verdict |
|---|---|---|
| AEngine A | States a rate that does not match the institution’s current published headline rate. | Factual gap |
| BEngine B | States the matching rate (correct against stored reference). | Match |
| CEngine C | Does not surface the institution; returns a competitor’s rate instead. | Visibility gap |
Illustrative prompt: "What is the current 12-month fixed deposit rate at BankCo MY?"
Engine A states a rate that does not match the institution's current published headline rate (factual gap). Engine B states the matching rate (correct against stored reference). Engine C does not surface the institution; returns a competitor's rate instead (visibility gap).
A Lawnise visibility check captures the same prompt across all engines on the same scan; fact verification flags the discrepant engine answer against the stored reference document; reputation analysis logs the competitor-displacement signal.
Scenario 02 · Evidence-to-claim ledger pattern
“BankCo MY offers deposit insurance protection up to a published per-depositor cap.”
Illustrative engine response: "BankCo MY offers deposit insurance protection up to a published per-depositor cap."
Stored reference: current published deposit-insurance cap from the institution's own disclosure documents.
Hash-linked evidence trail: scan ID + engine response capture + reference document version + multi-agent review path + timestamp.
Enterprise customers export this ledger for audit. Each row carries the full chain — engine response, reference matched, scan metadata, verification path.
Scenario 03 · Correction workflow pattern
“A discrepancy on Engine X for BankCo MY about branch availability is flagged.”
Discrepancy on Engine X surfaces in scan; Lawnise opens a finding with full evidence chain.
Branch availabilityRight-to-reply workflow drafts a correction notice anchored to the institution’s own reference.
Right-to-replyNotice published via the engine provider’s correction pathway; receipt logged in the ledger.
Correction pathwayLawnise re-scans the same prompt set; verification status updates from gap to match (or escalates).
Closed-loop verifyIllustrative cycle: a discrepancy on Engine X for BankCo MY about branch availability is flagged. The right-to-reply workflow drafts a correction notice; the notice is sent to the engine provider via the published correction pathway; Lawnise tracks the cycle from finding → correction notice → engine update → re-scan verification.
Start with what fits
See what public AI says about your bank in 60 seconds. Or scope a briefing for procurement and Enterprise.
100 visibility checks per month. No credit card.
For procurement, Enterprise scope, and Lawnise-operated deployment.
Lawnise builds independent AI trust infrastructure for regulated sectors, starting with banking and insurance. When independence and methodology transparency matter, the answer engine answers should match the source of truth.
Banking-sector teams use Lawnise to operationalise the Enterprise Public AI TRiSM for banking framework, with platform evidence grounded in our ongoing public AI audit methodology.
See the underlying Public-facing AI governance and evidence infrastructure that powers visibility, accuracy, and reputation checks for regulated brands.
Adjacent-sector procurement teams may also review Enterprise Public AI TRiSM for insurance for cross-sector coverage patterns.