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Metrics Guide

🧩 Rule Filter fields

1. Overview

âś… Figma Design: Design / Rule Filter

Field Name

Type / Format

Options

Required

Notes

Time Interval

Select

5 Mins | 1H | 12H | 1D

Required

Time window to evaluate metrics

Merchant ID

Multi Select

API (Org’s resources)

Optional

Filter by merchant(s).

PSP

Multi Select

API (Org’s resources)

Optional

Filter by payment service provider(s).

Country

Multi Select

API (Org’s resources)

Optional

Filter by end-user country (ISO alpha-3).

BIN

Multi Select

API (Org’s resources)

Optional

Filter by card BIN(s).

Issuer

Multi Select

API (Org’s resources)

Optional

Filter by issuing bank(s)

Tx Type

Multi Select

API (Org’s resources)

Optional

Filter by transaction type.

PSP Service

Multi Select

API (Org’s resources)

Optional

Filter by specific PSP service(s).

2. Field Details

Time Interval

  • Reference: interval

  • Type: string

  • Sample: 5Mins / 1H / 12H / 1D

  • Notes: Merchant identifier; repeated across transactions

Merchant ID

  • Reference: merchantId

  • Type: string

  • Sample: 100727003

  • Notes: Merchant identifier; repeated across transactions.

PSP

  • Reference: psp

  • Type: string

  • Sample: Payneteasy, Kluwp, Pradexx, SaltarPay, Interkassa, CardEye

  • Notes: PSP that produced the final/current transaction status.

Country

  • Reference: country

  • Type: string

  • Sample: SWE, NLD, JPN, IRL, AUS

  • Notes: ISO 3166-1 alpha-3 country code of the end user.

BIN

  • Reference: bin

  • Type: string

  • Sample: 537049, 535583

  • Notes: Bank Identification Number of the payment method (e.g., for card transactions).

Issuer

  • Reference: issuer

  • Type: string

  • Sample: TERRA BANK

  • Notes: Name of the issuing bank or institution.

Tx Type

  • Reference: txType

  • Type: string

  • Sample: BankDeposit

  • Notes: Transaction category (e.g., Banking, Card, E-wallet, etc.).

PSP Service

  • Reference: pspService

  • Type: string

  • Sample: CONTIANT_RAB_EZIFL

  • Notes: Indicates specific service used via PSP (e.g., open banking via Sofort).

⚙️ Rule Metric Conditions

1. Metric Overview

âś… Figma Design: Design / Rule Condition

âś… Sample CSV Data: Sample Data

Time Window

Definition:

The time range used to retrieve transactions for metric calculations.
If the time window is defined as 60, it means retrieving all transactions created within the last 60 minutes from the current time.

Example:

If the current time is 2025-10-01 12:00:00 and Time Window = 60, → transactions from 2025-10-01 11:00:00 → 2025-10-01 12:00:00 will be included.

Example Dataset (Time Window = 60 minutes)

transactionID

customerEmail

created (UTC)

Included?

TX001

smith@gmail.com

2025-10-01 11:05:00

âś… Yes

TX002

emma.johnson@mail.com

2025-10-01 11:45:30

âś… Yes

TX003

liam.brown@yahoo.com

2025-10-01 10:58:10

❌ No (older than 60 mins)

TX004

ava.taylor@hotmail.com

2025-10-01 12:00:00

âś… Yes

Note:

Only transactions created between 2025-10-01 11:00:00 and 2025-10-01 12:00:00 are included in metric calculations because the Time Window is set to 60 minutes.

This ensures only recent transactions (e.g., the last 60 minutes) are counted in your metric, not older ones.

Acceptance Rate (AR)

Definition:

The percentage of Successful transactions out of the total number of transactions within the selected time window or group.

Formula:

AR = (Successful Transactions / Total Transactions) Ă— 100

Example dataset:

transactionID

customerEmail

merchantId

state

TX001

smith@gmail.com

100727022

Successful

TX002

emma.johnson@mail.com

100727022

Successful

TX003

liam.brown@yahoo.com

100727022

Failed

TX004

ava.taylor@hotmail.com

100727022

Successful

TX005

noah.wilson@gmail.com

100727022

Failed

âś… Successful Transactions = 3

âś… Total Transactions = 5

➡️ AR = (3 / 5) × 100 = 60%

Note:

There are 5 total transactions.

Out of them, 3 are marked as “Successful.”

That means 60% of all transactions were accepted successfully.

Error Rate (ER)

Definition:

The percentage of Failed transactions out of the total number of transactions within the selected time window or group.

Formula:

ER = (Failed Transactions / Total Transactions) Ă— 100

Example dataset:

transactionID

customerEmail

merchantId

state

TX001

olivia.miller@mail.com

100727022

Failed

TX002

mason.davis@gmail.com

100727022

Failed

TX003

mia.clark@yahoo.com

100727022

Successful

TX004

ethan.james@mail.com

100727022

Failed

TX005

lucas.thomas@gmail.com

100727022

Successful

âś… Failed Transactions = 3

âś… Total Transactions = 5

➡️ ER = (3 / 5) × 100 = 60%

Note:

We have 3 out of 5 transactions ended with an error (state = “Failed”).

That means 60% of all attempts failed, meaning the system or PSP rejected those payments.

Error Code

Definition:

Represents the error response code returned by the Payment Service Provider (PSP) or internal system when a transaction fails.

Used to categorize or analyze transaction failures.

Operators:

  • ANYOF: True if at least one transaction’s error code matches any in the selected list.
    Simplified: One matching error code is enough to satisfy the condition.

  • ALLOF: True only if all selected error codes are present among transactions.
    Simplified: Every listed error code must occur at least once.

Example:

transactionID

errorCode

TX001

1101

TX002

1202

TX003

1303

TX004

1404

TX005

1505

Selected Error Codes: [1101, 1999]

Dataset contains error codes: 1101, 1202, 1303, 1404, 1505.

  • ANYOF → âś… satisfied(1101 found)

  • ALLOF → ❌ not satisfied (1999 missing)

BIN

Definition:

Bank Identification Number (BIN) of the payment method used (e.g., for card transactions).

BIN identifies the issuing bank or financial institution of the card.

Operators:

  • ANYOF: True if at least one BIN in the selected list appears in the dataset.
    Simplified: If any transaction has a BIN in the selected list, the condition is satisfied.

  • ALLOF: True only if all BINs in the selected list appear in the dataset.
    Simplified: Every BIN in the list must exist among the transactions.

Example:

transactionID

bin

TX001

1234

TX002

1111

TX003

2222

TX004

3333

TX005

4444

Selected BINs: [1234, 5678]

  • ANYOF → âś… satisfied (because 1234 exists)

  • ALLOF → ❌ not satisfied (5678 missing)

ISSUER

Definition:

Name of the issuing bank or financial institution that provided the payment method.

Operators:

  • ANYOF: True if any transaction’s issuer matches one of the selected issuers.
    Simplified: At least one transaction comes from a selected issuer.

  • ALLOF: True only if all selected issuers appear among the transactions.
    Simplified: The dataset must include every issuer in the list.

Example:

transactionID

issuer

TX001

HSBC

TX002

Citibank

TX003

JPMorgan

TX004

Vietcombank

TX005

ANZ

Selected Issuers: [HSBC, DBS]

Dataset Issuers: HSBC, Citibank, JPMorgan, Vietcombank, ANZ

  • ANYOF → âś… satisfied (HSBC found)

  • ALLOF → ❌ not satisfied (DBS missing)

Unique User

Definition:

The number of unique users, identified by distinct customer email addresses (merchantUserEmail) within the dataset.

Formula:

Unique User = Count(Distinct merchantUserEmail)

Example dataset:

transactionID

customerEmail

merchantId

state

TX001

alice@mail.com

100727022

Successful

TX002

bob@mail.com

100727022

Failed

TX003

alice@mail.com

100727022

Failed

TX004

carol@mail.com

100727022

Successful

TX005

bob@mail.com

100727022

Successful

âś… Distinct Emails = alice, bob, carol

➡️ Unique Users = 3

Note:

Even though there are 5 transactions, some users made multiple transactions.

We only count distinct customer emails, so there are 3 unique users in this dataset.

Tx State Rate

Definition:

The proportion of transactions that belong to a specific transaction state (e.g., SUCCESSFUL, FAILED, PENDING, WAITING_INPUT) compared to the total number of transactions in the dataset or time window.

Formula:

Tx State Rate = (Transactions in Selected State / Total Transactions) Ă— 100

Example dataset:

transactionID

customerEmail

merchantId

state

TX001

smith@gmail.com

100727022

WAITING_INPUT

TX002

emma.johnson@mail.com

100727022

WAITING_INPUT

TX003

liam.brown@yahoo.com

100727022

WAITING_INPUT

TX004

ava.taylor@hotmail.com

100727022

WAITING_INPUT

TX005

noah.wilson@gmail.com

100727022

FAILED

âś… Selected State = WAITING_INPUT

âś… Transactions in selected state = 4

âś… Total Transactions = 5

➡️ Tx State Rate = (4 / 5) × 100 = 80%

Note:

Most transactions are still waiting for input (e.g., pending user action or PSP response).

That means 80% of all transactions are currently in WAITING_INPUT state.

2. Condition Fields Detail

Acceptance Rate (AR)

Reference: AR

Type: number (float)

Sample: 60.5

Formula: (Successful Transactions / Total Transactions) Ă— 100

Description: Percentage of successful transactions out of total transactions within the selected time window.
Indicates the overall success rate of transaction processing for a merchant or system.

Notes:

  • Only transactions with state = SUCCESSFUL are counted.

  • Commonly used to evaluate payment performance or success trends over time.

Acceptance Rate Threshold

Reference: condition_AR_THRESHOLD_[number]

Type: number (float)

Sample: 60.5

Description: Threshold value used for comparison against the Acceptance Rate (AR) metric.

Notes: Defines the limit at which the Acceptance Rate is considered acceptable or triggers a rule condition.

Error Rate (ER)

Reference: ER

Type: number (float)

Sample: 60.5

Formula: (Failed Transactions / Total Transactions) Ă— 100

Description: Percentage of failed transactions out of total transactions within the selected time window. Helps identify the proportion of transactions that ended with an error or were rejected by the PSP.

Notes:

  • Only transactions with state = FAILED are counted.

  • Useful for monitoring system reliability and identifying processing issues.

Error Rate Threshold

Reference: condition_ERROR_THRESHOLD_[number]

Type: number (float)

Sample: 40.0

Description: Threshold value used for comparison against the Error Rate (ER).

Notes: Defines the acceptable error rate limit for a merchant or monitoring rule.

Unique User (UNIQ_USER)

Reference: UNIQ_USER

Type: integer

Sample: 3

Formula: Count(Distinct customerEmail)

Description: Number of unique users identified by distinct customer email addresses (e.g., customerEmail or merchantUserEmail).

Notes:

  • A single customer performing multiple transactions is counted only once.

  • Measures reach or distinct customer count.

Unique User Threshold

Reference: condition_UNIQ_USER_THRESHOLD_[number]

Type: integer

Sample: 5

Description: Threshold for comparison against Unique User (UU).

Notes: Defines expected or limit values for rule evaluation.

Transaction State Rate (TX_STATES)

Reference: TX_STATES

Type: number (float)

Sample: 80.0

Formula: (Transactions in Selected State / Total Transactions) Ă— 100

Description: Percentage of transactions belonging to a specific state (e.g., SUCCESSFUL, FAILED, PENDING, etc.).
Helps visualize transaction state distribution and monitor real-time processing status.

Notes:

  • Selected state is dynamically configurable based on rule or condition.

Transaction State Rate Threshold

Reference: condition_TX_STATE_RATE_THRESHOLD_[number]

Type: number (float)

Sample: 80.0

Description: Threshold for comparison against Transaction State Rate (TSR).

Notes: Defines acceptable or trigger limits for the selected transaction state.

Transaction State Code

Type: string

Sample: Successful

Notes: Enumerated transaction states representing specific processing outcomes.

Possible Values:
Successful, Failed, Cancelled, Pending, Processing

Transaction State Code Condition

Reference: condition_TX_STATE_[number]

Type: array (string[])

Sample: ["SUCCESSFUL", "WAITING_INPUT"]

Description: Array of transaction state codes used to filter which states are included in the TSR or compared with thresholds.

Notes:

  • Each value matches a valid TX STATE CODE entry.

  • Enables flexible multi-state comparison.

  • Example: monitor if ≥80% of transactions are in WAITING_INPUT or PROCESSING.

BIN

Reference: BIN

Type: string

Sample: 537049, 535583

Description: Bank Identification Number (BIN) of the payment method used (e.g., for card transactions).

BIN Condition

Reference: condition_BINS_[number]

Type: array (string[])

Sample: [537049, 535583]

Description:

  • List of BINs used for comparison against transaction data.

  • Used together with the BIN metric and the selected operator (ANYOF / ALLOF).

  • Defines which BINs should be included or required for the condition to be considered true.

Notes: Works as a filter for transactions based on BINs.

Issuer

Reference: ISSUER

Type: string

Sample: TERRA BANK

Description: Name of the issuing bank or financial institution that issued the card or payment method.

Issuer Condition

Reference: condition_ISSUERS_[number]

Type: array (string[])

Sample: ["TERRA BANK"]

Description:

  • List of issuer names to be compared against transaction data.

  • Used together with the ISSUER metric and the operator (ANYOF / ALLOF).

  • Determines which issuers’ transactions are included or required to satisfy the condition.

Notes: Enables targeted monitoring by issuer.

Error Code

Reference: ERROR_CODE

Type: string

Sample: 1001, 2003

Description: Represents the error response code returned by the Payment Service Provider (PSP) or internal system when a transaction fails.

Error Code Condition

Reference: condition_ERROR_CODES_[number]

Type: array (string[])

Sample: [1001, 2003]

Description:

  • Array of error codes used for comparison against transaction data.

  • Defines which error codes are to be checked under a condition.

  • Works with ERROR_CODE metric and operator (ANYOF / ALLOF).

Notes: Helps detect specific failure reasons or categories of PSP errors.