> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dojah.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Banking Transaction Monitoring

> Monitor transfers and withdrawals in real time. Rule-based and ML risk scoring so you can allow, block, or review in milliseconds.

Monitor banking transactions in real-time with EasyDetect’s transaction
intelligence engine. Designed for financial platforms, neobanks, and digital wallets,
this system helps you detect unauthorized activity, risky behavior, or fraud rings before money moves.

It uses a layered approach: rule-based logic, behavioral profiling, and
contextual analysis. Together they assess risk across sender, receiver, device,
and transaction metadata.

***

#### How It Works

When you send a banking event to EasyDetect, the system immediately performs a multi-signal risk analysis. It evaluates:

* **Transaction details** like amount, currency, channel, and purpose
* **User profile signals**, including account age, balance, registration time, and device consistency
* **Sender/Receiver reputations** and known fraudulent relationships
* **Device context** such as IP address, operating system, and geolocation

In milliseconds, EasyDetect returns one of three decisions:

* ✅ **Allowed** – The transaction is safe to proceed
* ⚠️ **Pending** – The transaction is flagged for manual review in the Case Management tab
* ❌ **Blocked** – The transaction should be stopped immediately

These verdicts give you operational flexibility,
automate decisions for low-risk events and review edge cases with full context.

***

#### Step-by-Step Integration

<Steps>
  <Step title="Create a Flow">
    Log in to the Dojah Dashboard. Under EasyDetect, create a new fraud flow and select "Banking" as your use case. You can name it something like “Customer Transfers” or “Wallet Payouts.”
  </Step>

  <Step title="Define Rules">
    Use the no-code rule builder to configure conditions like:\
    `IF amount > ₦1M AND device mismatch → Mark as High-Risk`\
    You can include logic for transaction velocity, foreign transfers, balance anomalies, and more
  </Step>

  <Step title="Send Events">
    Push your transaction data via POST to the
    unique ingestion [URL](/fraud/monitoring/live-transactions#ingest-url) provided in your flow’s settings.
    Ensure the `type` is set to `banking` and include relevant details like
    transaction metadata, user profile, device, and recipient.
  </Step>

  <Step title="Receive Decisions in Real-Time">
    Your system receives an instant verdict. Based on the result, you can:

    * Approve and proceed
    * Hold for review in the dashboard
    * Block entirely
  </Step>

  <Step title="Review Cases (if needed)">
    Suspicious events will automatically generate a case with all contextual data and rule triggers. Analysts can manually review and take action.
  </Step>

  <Step title="Report and Optimize">
    Use the reporting tab to analyze fraud trends, triggered rules, and verdict history. Iterate on your rule sets for better outcomes.
  </Step>
</Steps>

***

#### Example Risk Scenarios

EasyDetect flags more than static values. Here are a few fraud patterns it detects:

* Transfers initiated within minutes of a new account creation
* Unusual IP locations for high-value withdrawals
* Sender and receiver linked to prior high-risk activity
* Transactions from rooted or jailbroken devices
* SIM change before a transfer

<Note>You can configure who receives email alerts for case review.</Note>

***

#### Use Cases

* **Account Takeover Detection**\
  Catch sessions from new devices with unusual transaction requests

* **Mule Account Identification**\
  Flag transactions to beneficiaries with suspicious behavioral history

* **AML/Compliance Monitoring**\
  Combine transaction signals with EasyDetect’s fraud and identity stack for full-spectrum screening

* **Payment Controls**\
  Apply different rules by transaction channel: POS, mobile, ATM, or web

***

#### Why It Works

EasyDetect combines high-speed fraud signals with long-term risk memory.
It doesn’t just inspect a transaction, it profiles the account, evaluates the network, and adapts based on feedback from every decision.

Your team stays ahead of emerging fraud patterns while preserving trust with good customers.