Blogs
Blogs
Blogs
UPI forensics: tracing fraudulent activity throughout Google Pay, PhonePe & Paytm in India
UPI forensics: tracing fraudulent activity throughout Google Pay, PhonePe & Paytm in India
UPI forensics: tracing fraudulent activity throughout Google Pay, PhonePe & Paytm in India
Over 20 billion UPI transactions in a single month. That was India in late 2025. UPI isn't just a payment method anymore. It's the financial infrastructure. Chai, rent, car purchases, salaries. Everything runs on it.
Which also means UPI fraud runs on it.
Investment scams, loan app extortion, romance fraud, money mule networks. The money almost always moves through UPI. When investigators seize a suspect's phone, the transaction history inside GPay, PhonePe, and Paytm is often the single most important piece of evidence. But making sense of that data is harder than most people expect.
Over 20 billion UPI transactions in a single month. That was India in late 2025. UPI isn't just a payment method anymore. It's the financial infrastructure. Chai, rent, car purchases, salaries. Everything runs on it.
Which also means UPI fraud runs on it.
Investment scams, loan app extortion, romance fraud, money mule networks. The money almost always moves through UPI. When investigators seize a suspect's phone, the transaction history inside GPay, PhonePe, and Paytm is often the single most important piece of evidence. But making sense of that data is harder than most people expect.
Over 20 billion UPI transactions in a single month. That was India in late 2025. UPI isn't just a payment method anymore. It's the financial infrastructure. Chai, rent, car purchases, salaries. Everything runs on it.
Which also means UPI fraud runs on it.
Investment scams, loan app extortion, romance fraud, money mule networks. The money almost always moves through UPI. When investigators seize a suspect's phone, the transaction history inside GPay, PhonePe, and Paytm is often the single most important piece of evidence. But making sense of that data is harder than most people expect.
Share Article
Share Article
Published
Published
Published
April 4, 2026
April 4, 2026
April 4, 2026
Category
Category
Category
Technical
Technical
Technical
7 Min Read
7 Min Read
7 Min Read
Where UPI transaction data is stored on Android phones
Where UPI transaction data is stored on Android phones
Where UPI transaction data is stored on Android phones
Each UPI app keeps transaction records in SQLite databases inside its application sandbox on Android:
Google Pay: `com.google.android.apps.nbu.paisa.user`
PhonePe: `com.phonepe.app`
Paytm: `net.one97.paytm`
Two fields matter more than anything else for UPI forensic investigation.
The UTR (Unique Transaction Reference) is a 12-character alphanumeric code that NPCI assigns to every UPI transaction. It's the one identifier that ties what's on the phone to what the banking system recorded. If you have the UTR, you can match a device-level record to a bank-level record. That's your anchor for tracing digital payment fraud.
The VPA (Virtual Payment Address) is the `user@bankhandle` format, like `john@okicici`, that maps to a specific bank account. Cross-referencing VPAs across multiple apps on the same device can expose accounts the suspect never voluntarily disclosed. In money mule investigations, VPA analysis often uncovers the full network.
Each UPI app keeps transaction records in SQLite databases inside its application sandbox on Android:
Google Pay: `com.google.android.apps.nbu.paisa.user`
PhonePe: `com.phonepe.app`
Paytm: `net.one97.paytm`
Two fields matter more than anything else for UPI forensic investigation.
The UTR (Unique Transaction Reference) is a 12-character alphanumeric code that NPCI assigns to every UPI transaction. It's the one identifier that ties what's on the phone to what the banking system recorded. If you have the UTR, you can match a device-level record to a bank-level record. That's your anchor for tracing digital payment fraud.
The VPA (Virtual Payment Address) is the `user@bankhandle` format, like `john@okicici`, that maps to a specific bank account. Cross-referencing VPAs across multiple apps on the same device can expose accounts the suspect never voluntarily disclosed. In money mule investigations, VPA analysis often uncovers the full network.
Each UPI app keeps transaction records in SQLite databases inside its application sandbox on Android:
Google Pay: `com.google.android.apps.nbu.paisa.user`
PhonePe: `com.phonepe.app`
Paytm: `net.one97.paytm`
Two fields matter more than anything else for UPI forensic investigation.
The UTR (Unique Transaction Reference) is a 12-character alphanumeric code that NPCI assigns to every UPI transaction. It's the one identifier that ties what's on the phone to what the banking system recorded. If you have the UTR, you can match a device-level record to a bank-level record. That's your anchor for tracing digital payment fraud.
The VPA (Virtual Payment Address) is the `user@bankhandle` format, like `john@okicici`, that maps to a specific bank account. Cross-referencing VPAs across multiple apps on the same device can expose accounts the suspect never voluntarily disclosed. In money mule investigations, VPA analysis often uncovers the full network.
Why cross-app correlation is the hardest part of UPI forensics
Why cross-app correlation is the hardest part of UPI forensics
Why cross-app correlation is the hardest part of UPI forensics
Extraction is the easy part. Correlation is where cases get made or lost.
A typical UPI fraud suspect doesn't stick to one app. They use three or four, sometimes with multiple accounts on each. Money hops: victim sends to Account A on GPay, Account A forwards to Account B on PhonePe, Account B cashes out through Paytm. Every hop is a separate transaction, in a separate app, with a separate database schema.
Try doing that correlation manually. Open each database in SQLite Browser. Match timestamps. Cross-reference UTRs. Build a flowchart. For one suspect with ten transactions, it's tedious. For a money mule network with dozens of accounts and hundreds of transactions? Forget it.
There's a subtlety that makes this worse: the apps don't store data the same way. Column names differ. Timestamp formats differ. Some record amounts in rupees, others in paise. Some use integer timestamps, others ISO strings. A forensic tool needs to understand each app's specific data model, and these models change every time the app updates.
This is exactly why Secfore built parsers for GPay, PhonePe, and Paytm that track schema changes as they happen. The Visualizer puts all UPI transactions on a unified timeline alongside messages, calls, and location data. No spreadsheet gymnastics needed.
Extraction is the easy part. Correlation is where cases get made or lost.
A typical UPI fraud suspect doesn't stick to one app. They use three or four, sometimes with multiple accounts on each. Money hops: victim sends to Account A on GPay, Account A forwards to Account B on PhonePe, Account B cashes out through Paytm. Every hop is a separate transaction, in a separate app, with a separate database schema.
Try doing that correlation manually. Open each database in SQLite Browser. Match timestamps. Cross-reference UTRs. Build a flowchart. For one suspect with ten transactions, it's tedious. For a money mule network with dozens of accounts and hundreds of transactions? Forget it.
There's a subtlety that makes this worse: the apps don't store data the same way. Column names differ. Timestamp formats differ. Some record amounts in rupees, others in paise. Some use integer timestamps, others ISO strings. A forensic tool needs to understand each app's specific data model, and these models change every time the app updates.
This is exactly why Secfore built parsers for GPay, PhonePe, and Paytm that track schema changes as they happen. The Visualizer puts all UPI transactions on a unified timeline alongside messages, calls, and location data. No spreadsheet gymnastics needed.
Extraction is the easy part. Correlation is where cases get made or lost.
A typical UPI fraud suspect doesn't stick to one app. They use three or four, sometimes with multiple accounts on each. Money hops: victim sends to Account A on GPay, Account A forwards to Account B on PhonePe, Account B cashes out through Paytm. Every hop is a separate transaction, in a separate app, with a separate database schema.
Try doing that correlation manually. Open each database in SQLite Browser. Match timestamps. Cross-reference UTRs. Build a flowchart. For one suspect with ten transactions, it's tedious. For a money mule network with dozens of accounts and hundreds of transactions? Forget it.
There's a subtlety that makes this worse: the apps don't store data the same way. Column names differ. Timestamp formats differ. Some record amounts in rupees, others in paise. Some use integer timestamps, others ISO strings. A forensic tool needs to understand each app's specific data model, and these models change every time the app updates.
This is exactly why Secfore built parsers for GPay, PhonePe, and Paytm that track schema changes as they happen. The Visualizer puts all UPI transactions on a unified timeline alongside messages, calls, and location data. No spreadsheet gymnastics needed.
UPI fraud investigation workflow: from extraction to money trail
UPI fraud investigation workflow: from extraction to money trail
UPI fraud investigation workflow: from extraction to money trail
You start by extracting every payment app on the device. Not just the one you think was used. All of them. You don't know which ones were involved until you look.
Parse the transaction records from each app. Pull UTR, amount, timestamp, counterparty VPA, and transaction status (success, pending, failed). Then match on UTR across apps and across seized devices in the same case. The same UTR showing up on two phones in your evidence room means those phones were on opposite ends of the same transaction.
Build the money flow chronologically: Victim → Mule 1 → Mule 2 → Cash-out. Each node is a VPA, each link is a UTR-stamped transaction. This is your money trail.
Then, and this is the part that actually cracks cases, cross-reference with communications data. Were there WhatsApp messages instructing money transfers around the same timestamps? Call logs showing contact between the suspect and the victim right before the transaction? Location data putting the suspect at an ATM during the cash-out window?
The transaction log tells you money moved. Correlating it with communications and location data tells you who directed it and why. That combination is what makes the difference between a BSA 2023 Section 63-compliant case file and a pile of disconnected records.
You start by extracting every payment app on the device. Not just the one you think was used. All of them. You don't know which ones were involved until you look.
Parse the transaction records from each app. Pull UTR, amount, timestamp, counterparty VPA, and transaction status (success, pending, failed). Then match on UTR across apps and across seized devices in the same case. The same UTR showing up on two phones in your evidence room means those phones were on opposite ends of the same transaction.
Build the money flow chronologically: Victim → Mule 1 → Mule 2 → Cash-out. Each node is a VPA, each link is a UTR-stamped transaction. This is your money trail.
Then, and this is the part that actually cracks cases, cross-reference with communications data. Were there WhatsApp messages instructing money transfers around the same timestamps? Call logs showing contact between the suspect and the victim right before the transaction? Location data putting the suspect at an ATM during the cash-out window?
The transaction log tells you money moved. Correlating it with communications and location data tells you who directed it and why. That combination is what makes the difference between a BSA 2023 Section 63-compliant case file and a pile of disconnected records.
You start by extracting every payment app on the device. Not just the one you think was used. All of them. You don't know which ones were involved until you look.
Parse the transaction records from each app. Pull UTR, amount, timestamp, counterparty VPA, and transaction status (success, pending, failed). Then match on UTR across apps and across seized devices in the same case. The same UTR showing up on two phones in your evidence room means those phones were on opposite ends of the same transaction.
Build the money flow chronologically: Victim → Mule 1 → Mule 2 → Cash-out. Each node is a VPA, each link is a UTR-stamped transaction. This is your money trail.
Then, and this is the part that actually cracks cases, cross-reference with communications data. Were there WhatsApp messages instructing money transfers around the same timestamps? Call logs showing contact between the suspect and the victim right before the transaction? Location data putting the suspect at an ATM during the cash-out window?
The transaction log tells you money moved. Correlating it with communications and location data tells you who directed it and why. That combination is what makes the difference between a BSA 2023 Section 63-compliant case file and a pile of disconnected records.
Common mistakes in UPI forensic investigation
Common mistakes in UPI forensic investigation
Common mistakes in UPI forensic investigation
Ignoring failed transactions. Everyone focuses on successful transfers, but failed UPI attempts reveal intent. Ten failed attempts to one VPA before a successful one to a different VPA? That's the suspect testing accounts or hitting transaction limits. That pattern matters in court.
Overlooking refund and reversal records. Some fraud operations weaponize UPI's refund mechanism. These records are stored differently from regular transactions in most apps, and they get skipped in standard analysis. Always check for them.
Missing merchant vs P2P metadata. UPI handles both person-to-person and person-to-merchant payments, and the metadata is different. Merchant transactions carry merchant IDs and MCC codes that can identify businesses being used for laundering. P2P analysis alone won't catch that.
Not correlating with other app data. UPI transactions in isolation tell you money moved. Without cross-referencing against messaging apps, call logs, and location history, you're missing the story. This is where tools with unified forensic timelines pay for themselves.
Ignoring failed transactions. Everyone focuses on successful transfers, but failed UPI attempts reveal intent. Ten failed attempts to one VPA before a successful one to a different VPA? That's the suspect testing accounts or hitting transaction limits. That pattern matters in court.
Overlooking refund and reversal records. Some fraud operations weaponize UPI's refund mechanism. These records are stored differently from regular transactions in most apps, and they get skipped in standard analysis. Always check for them.
Missing merchant vs P2P metadata. UPI handles both person-to-person and person-to-merchant payments, and the metadata is different. Merchant transactions carry merchant IDs and MCC codes that can identify businesses being used for laundering. P2P analysis alone won't catch that.
Not correlating with other app data. UPI transactions in isolation tell you money moved. Without cross-referencing against messaging apps, call logs, and location history, you're missing the story. This is where tools with unified forensic timelines pay for themselves.
Ignoring failed transactions. Everyone focuses on successful transfers, but failed UPI attempts reveal intent. Ten failed attempts to one VPA before a successful one to a different VPA? That's the suspect testing accounts or hitting transaction limits. That pattern matters in court.
Overlooking refund and reversal records. Some fraud operations weaponize UPI's refund mechanism. These records are stored differently from regular transactions in most apps, and they get skipped in standard analysis. Always check for them.
Missing merchant vs P2P metadata. UPI handles both person-to-person and person-to-merchant payments, and the metadata is different. Merchant transactions carry merchant IDs and MCC codes that can identify businesses being used for laundering. P2P analysis alone won't catch that.
Not correlating with other app data. UPI transactions in isolation tell you money moved. Without cross-referencing against messaging apps, call logs, and location history, you're missing the story. This is where tools with unified forensic timelines pay for themselves.
UPI forensics is now essential for every Indian forensic lab
UPI forensics is now essential for every Indian forensic lab
UPI forensics is now essential for every Indian forensic lab
Every cyber cell, every economic offences wing, every forensic lab in India is going to need UPI forensics as a core competency. The fraud volume guarantees it. India reported over ₹11,000 crore in digital payment fraud in recent years, and UPI's share is growing.
The tools matter. Trying to do cross-app correlation manually across GPay, PhonePe, and Paytm with schema differences and timestamp format mismatches isn't realistic at scale. The evidence is sitting in databases on seized phones right now. The gap is whether your toolchain can actually read it, decode it, and correlate it fast enough to build a case.
Secfore supports GPay, PhonePe, Paytm, and 25+ other Android apps with a unified forensic timeline. See a demo of UPI transaction analysis in action.
Every cyber cell, every economic offences wing, every forensic lab in India is going to need UPI forensics as a core competency. The fraud volume guarantees it. India reported over ₹11,000 crore in digital payment fraud in recent years, and UPI's share is growing.
The tools matter. Trying to do cross-app correlation manually across GPay, PhonePe, and Paytm with schema differences and timestamp format mismatches isn't realistic at scale. The evidence is sitting in databases on seized phones right now. The gap is whether your toolchain can actually read it, decode it, and correlate it fast enough to build a case.
Secfore supports GPay, PhonePe, Paytm, and 25+ other Android apps with a unified forensic timeline. See a demo of UPI transaction analysis in action.
Every cyber cell, every economic offences wing, every forensic lab in India is going to need UPI forensics as a core competency. The fraud volume guarantees it. India reported over ₹11,000 crore in digital payment fraud in recent years, and UPI's share is growing.
The tools matter. Trying to do cross-app correlation manually across GPay, PhonePe, and Paytm with schema differences and timestamp format mismatches isn't realistic at scale. The evidence is sitting in databases on seized phones right now. The gap is whether your toolchain can actually read it, decode it, and correlate it fast enough to build a case.
Secfore supports GPay, PhonePe, Paytm, and 25+ other Android apps with a unified forensic timeline. See a demo of UPI transaction analysis in action.
Related Articles
Technical
March 31, 2026
Industry
April 2, 2026
Industry
April 6, 2026
Related Articles
Technical
March 31, 2026
Industry
April 2, 2026
Industry
April 6, 2026
Related Articles
Technical
March 31, 2026
Industry
April 2, 2026
Industry
April 6, 2026
FAQ
FAQ
FAQ
Frequently Asked Questions
Frequently Asked Questions
Frequently Asked Questions
Find quick, straightforward answers to the most frequently asked questions.
Find quick, straightforward answers to the most frequently asked questions.
Find quick, straightforward answers to the most frequently asked questions.
Can UPI transaction history be recovered from a seized phone?
What is a UTR number and why does it matter in forensics?
How do investigators trace money flows across multiple UPI apps?
What is the biggest challenge in UPI forensic investigation?
Can UPI transaction history be recovered from a seized phone?
What is a UTR number and why does it matter in forensics?
How do investigators trace money flows across multiple UPI apps?
What is the biggest challenge in UPI forensic investigation?
Can UPI transaction history be recovered from a seized phone?
What is a UTR number and why does it matter in forensics?
How do investigators trace money flows across multiple UPI apps?
What is the biggest challenge in UPI forensic investigation?
Blogs & Insights
Blogs & Insights
Blogs & Insights
Insights, Updates, and Tips for Mobile Forensics
Insights, Updates, and Tips for Mobile Forensics
Insights, Updates, and Tips for Mobile Forensics
Stay ahead of the curve with expert insights, product updates, and practical tips tailored for mobile forensics professionals.
Stay ahead of the curve with expert insights, product updates, and practical tips tailored for mobile forensics professionals.
Stay ahead of the curve with expert insights, product updates, and practical tips tailored for mobile forensics professionals.
Secure & Scalable Infrastructure
Secure & Scalable Infrastructure
Secure & Scalable Infrastructure
Ready to Transform Your
Forensic Capabilities?
Ready to Transform Your
Forensic Capabilities?
Ready to Transform Your
Forensic Capabilities?
Experience the power of a proven digital forensics platform built for real-world
investigations. Get hands-on with advanced extraction.
Experience the power of a proven digital forensics platform built for real-world
investigations. Get hands-on with advanced extraction.
Experience the power of a proven digital forensics platform built for real-world
investigations. Get hands-on with advanced extraction.
Request a Demo
Request a Demo
Request a Demo