Real-time fraud detection,
built around your
business.

Real-time detection of fraudulent UPI transactions, mule-linked collect requests and social-engineering scams.

UPI moves funds in real time, so fraud detection must be faster than the payment itself. BANKiQ scores collect requests, payer-beneficiary relationships, device binding, VPA velocity, and transaction narration to identify mule-linked, scam-driven, or high-velocity UPI fraud in milliseconds.

Real-time controls for instant payment rails, where funds move irreversibly in seconds.

Instant payment rails are irreversible once executed, making detection timing critical. BANKiQ evaluates beneficiary risk, account-to-account velocity, transaction timing, device and network metadata, and historical counterparties to step up or block suspicious instant transfers before execution.

Flag suspicious new beneficiaries, rapid beneficiary addition and high-risk beneficiary networks before funds are transferred out.

Fraudsters often coerce victims into adding new beneficiaries or use accounts that have been recently added as payees. BANKiQ monitors beneficiary addition velocity, account-age patterns, mobile-number linkages, network centrality, and rapid post-addition transactions to detect risky payee behaviour.

Detect QR code replacement, malicious QR codes, merchant QR manipulation and collect-request abuse.

QR codes collapse payment intent into a single image, making them attractive to fraudsters. BANKiQ detects QR replacement, tampered merchant details, off-platform static QR abuse, and collect-request phishing by scanning transaction metadata, merchant reputation, and payer context at the point of scan.

Protect debit and credit card transactions across every acceptance channel in real time.

Card fraud spans physical and digital channels with different attack patterns. BANKiQ evaluates merchant category risk, transaction amount, location, device, 3DS behaviour, recurring-payment patterns, and cross-channel velocity to detect stolen-card use, card-not-present abuse, and counterfeit transactions.

Identify cash-withdrawal anomalies, card skimming, ATM compromise and cash-out behaviour.

ATMs remain a major cash-out channel for fraudsters. BANKiQ models withdrawal patterns, card-present device signatures, ATM terminal risk, time-of-day anomalies, and geographic clustering to detect skimming, card trapping, and rapid cash-out behaviour.

Detect point-of-sale fraud patterns at the moment of swipe, tap or dip.

POS terminals are a direct interface between stolen card data and merchant settlement. BANKiQ analyzes merchant risk, terminal behaviour, transaction amount, card-present vs. manual-entry indicators, and velocity patterns to detect counterfeit cards, fallback fraud, and collusive merchant activity.

Score online and CNP transactions for stolen-card use, friendly fraud and account-linked card abuse.

E-commerce and CNP transactions lack physical card verification, so risk is concentrated in digital signals. BANKiQ evaluates device identity, shipping-billing mismatch, account history, merchant risk, digital goods behaviour, and chargeback patterns to separate genuine shoppers from fraudulent actors.

Detect wallet top-up abuse, prepaid instrument (PPI) misuse and wallet-to-bank fraud patterns.

Prepaid wallets and PPIs can obscure the origin of funds and accelerate mule cash-outs. BANKiQ monitors wallet top-up velocity, load-to-cash-out ratios, wallet-to-wallet transfers, KYC tier limits, and linked bank accounts to identify abuse of closed-loop or semi-closed instruments.

Identify rapid transaction bursts, unusual frequency and velocity anomalies that signal an account is being drained.

Velocity is one of the strongest signals of an active compromise. BANKiQ builds customer-specific baselines for transaction count, amount, counterparties, and timing, then flags deviations that exceed adaptive thresholds — whether from a single burst or distributed low-value activity.

Monitor international remittances and cross-border payment flows for anomalies across corridors.

Cross-border transactions involve more complex routing, higher amounts, and less immediate recovery. BANKiQ scores corridors, correspondent Banks, beneficiary history, currency pairs, purpose codes, and sender-receiver patterns to detect trade-based money laundering, remittance scams, and fraud-driven outbound transfers.

Identify individual accounts being used to receive and move stolen or laundered funds.

A mule account is the critical link between a fraud victim and a cash-out point. BANKiQ detects mule accounts by combining transaction topology, account-opening behaviour, device and mobile linkages, KYC quality, and rapid pass-through patterns that indicate an account is being used as a conduit rather than for genuine banking.

Identify the entire mule network behind a fraud — not just one account — from victim to cash-out.

Mule accounts are rarely isolated. BANKiQ uses graph analytics to discover clusters of accounts connected by common devices, mobile numbers, beneficiaries, IPs, KYC agents, and transaction patterns. Network-level detection exposes the full cash-out chain and enables preventive action before the network scales.

Trace fraud proceeds across multiple layers of mule accounts as funds are moved to obscure their origin.

Sophisticated operations split funds across multiple layers to hide the trail. BANKiQ traces the propagation path from initial receipt through intermediate distribution accounts to final cash-out, identifying layered structures that evade single-account detection.

Catch fraud-to-mule-to-cash-out chains before funds are withdrawn or moved beyond recovery.

The cash-out window is the last opportunity to recover stolen funds. BANKiQ monitors for rapid movement from deposit to ATM withdrawal, UPI collect, wallet transfer, or foreign exchange, and triggers alerts or blocks when the velocity indicates an active cash-out.

Flag customers with a history of mule-linked activity who re-enter the banking ecosystem via a new account, mobile number or device.

Closed mule accounts often reappear under slightly different identities. BANKiQ maintains persistent risk markers for devices, mobile numbers, addresses, and biometric fragments to flag re-entry attempts even when the account name and KYC documents are new.

Map relationships across customer, mobile, device, account, beneficiary, IP and merchant to expose hidden fraud networks.

Fraud networks hide in plain sight across disconnected data silos. BANKiQ links entities across channels to build a unified network view, revealing dense clusters, unusual bridges, and high-risk intermediaries that would be invisible with account-level checks alone.

Flag suspicious activity immediately before or after loan disbursement.

Fraud around disbursement can involve redirecting funds, ghost borrowers, or kickback schemes. BANKiQ monitors disbursement account changes, beneficiary risk, sudden account dormancy breaks, and velocity immediately following disbursement.

Monitor for fraud across origination, disbursement, repayment, restructuring and closure.

Fraud can enter at any stage of a loan's life. BANKiQ provides continuous monitoring from origination through closure, detecting anomalies in repayment patterns, restructuring requests, foreclosure behaviour, and write-off attempts.

Distinguish genuine financial stress from deliberate fraudulent borrower behaviour.

First-party fraud involves borrowers who intentionally misrepresent their situation. BANKiQ differentiates willful default from genuine hardship using behavioural patterns, asset movement, income consistency, and repayment intent signals.

Extend real-time risk controls to in-branch and assisted-channel transactions.

Branch channels can be exploited by both insiders and external fraudsters. BANKiQ brings real-time risk scoring to assisted channels, including branch transactions, teller activity, and relationship-manager-initiated changes.

Detect employee-enabled fraud and policy circumvention across banking operations.

Insiders have privileged access and knowledge that can bypass controls. BANKiQ monitors employee access patterns, transaction approvals, data exports, and maker-checker overrides to detect internal misuse and collusion.

Detect collusion that circumvents maker-checker and segregation-of-duties controls.

Segregation of duties exists specifically to prevent single points of abuse. BANKiQ detects when two or more employees collude to approve suspicious transactions, alter customer data, or override risk flags in coordinated ways.

Flag misuse of privileged system access by employees and administrators.

Privileged users can disable controls, view sensitive data, and approve exceptions. BANKiQ monitors administrator actions, access to sensitive systems, bulk data operations, and configuration changes to detect abuse of high-privilege accounts.

Flag suspicious reactivation and movement on accounts that suddenly wake up after a long period of inactivity.

Dormant accounts are attractive to fraudsters because they bypass typical monitoring baselines. BANKiQ detects sudden reactivation, balance liquidation, beneficiary changes, and channel changes on long-inactive accounts.