Rapid growth in digital payments has led to increased fraud risks. Fraud typologies are continuously evolving — social engineering, mule accounts, and Authorized Push Payment (APP) frauds — while existing systems lag behind.
India's digital payment ecosystem has grown at an extraordinary pace. While this has democratised financial access, it has also created disproportionate increases in fraud risk. The scale and sophistication of attacks has overwhelmed fragmented, legacy defence mechanisms.
Fraudsters adapt constantly. Social engineering attacks exploit human psychology. Mule account networks launder fraudulent proceeds at scale. Authorized Push Payment (APP) frauds trick customers into willingly transferring money to criminals. Traditional systems cannot keep pace.
Existing Fraud Risk Management (FRM) systems are focused exclusively on the remitting side of transactions — the customer sending money. This leaves a critical blind spot: the beneficiary. Pre-transaction intelligence and beneficiary risk visibility are entirely absent from the current ecosystem.
Each bank, payment network, and payment service provider operates its own fraud detection in isolation. There is no central utility accessible to all participants, no shared intelligence layer, and inadequate dissemination of threat actor information across the ecosystem.
These five critical gaps in India's current payment fraud defence infrastructure are what IDPIC is mandated to directly address.
How IDPIC Fixes This →No unified platform exists for transaction risk monitoring across all ecosystem participants. Each entity operates with incomplete visibility.
Current monitoring focuses entirely on the sender. Beneficiary risk profiles are invisible to the system, allowing fraudulent destinations to operate undetected.
Mule accounts used to launder fraudulent proceeds are only identified after the damage is done. Proactive identification before transactions complete is impossible without shared intelligence.
Known fraud actors, suspicious account networks, and emerging threat patterns are not effectively shared across ecosystem participants, allowing the same fraudsters to strike repeatedly.