Data Integration Guide - ACH Transfers

 

ACH Transaction Data for Analysis

Transaction data fields, as well as customer profile data fields associated with the transaction, are an important driver of DataVisor’s ability to identify transaction fraud in ACH transfers. Sensitive customer PII information is not required for our ability to detect. This document serves as a general integration guideline for identifying ACH fraud. Our team will work with the specific client on more detailed integration options.

ACH Transaction Data

Fields that are helpful for transaction data include:

 

Category

Data Field

Description

Required

Account linkage

 

 

 

 

 

Internal account number

Unique internal account number (can be hashed)

X

Internal customer ID

Unique internal customer ID

X

External acct routing number

Routing number of the linked external account

X

External acct number

Account number of linked external account (can be hashed)

X

Authentication method

Method used to authenticate the external account ownership, possible methods are. trial deposit, instant authentication via login, authentication through EWS AOA, ect

X

Status

External account linkage result

X

EWS AOA 

 

 

 

 

 

 

 

 

Internal account number

Unique internal account number (can be hashed)

 

Internal customer ID

Unique internal customer ID

 

External routing number

Routing number of the linked external account

 

External account number

Account number of linked external account (can be hashed)

 

EWS AOA matching score

Overall matching score of inquired account ownership

 

Last name matching results

Account owner last name match result

 

First name matching results

Account owner first name match result

 

SSN matching results

Account owner SSN name match result

 

External account status

Status of the inquired external account

 

ACH transaction

 

 

 

 

 

 

 

 

 

 

 

 


 

 

 

 

 

Internal account number

Unique internal account number (can be hashed)

X

Internal customer ID

Unique internal customer ID

X

ODFI_RDFI indicator

Initiation party of the ACH transaction

X

Debit/Credit

Is ACH a debit or credit for the internal account

X

Amount

Transaction amount

X

External account routing number

Routing number of the linked external account

X

External account number

Account number of linked external account (can be hashed)

X

Entry date

Date the ACH transaction is created

X

Effective date

For scheduled transaction, the date ACH transaction is scheduled for

 

Transaction CD

standard ACH transaction code

X

Return CD

For returned ACH, the return reason code

X

ACH transaction ID

Unique ACH transaction ID

X

Counterparty  Full Name

Full name of the counterparty account, i.e. the other party of the transaction

X

Counterparty Tax ID

Unique tax identifier of the other party, such as SSN / ITIN

X

Counterparty customer ID

Customer ID of the other party of the transaction, if available

 

Counterparty account number

Counterparty account number 

X

Counterparty routing number

Routing number of the counterparty account

 

ACH Transaction Outcome

ACH Return Label

Whether the ACH is Returned

X

ACH Return Code

ACH Return Code

X

Fraud Return or Not

Should this ACH return be treated as Fraud by DV system

 

 

An example of transaction data:

Transaction ID

Account ID

Transaction Type

Transaction Time

Amount

External Account Number

...

1203029281904

53277897

ACH

2016-08-16 12:35:06

88.4

42298131

...

9303283057123

40098005

ACH

2016-08-16 12:35:06

837.43

20381047

...

 

Customer Profile Data

Customer profile data is also important for fraud detection, when merging with transaction level data.DataVisor uses various types of at-rest and in-transit encryption methods to make sure the PII data shared in the customer profile data is handled in a secured way. If PII data sharing is not available, we recommend using SHA-256 hashing to hash the data before sharing the customer profile data with DataVisor.

Fields that are helpful for customer profile data include:

Category

Data Field

Description

Required

Customer Profile Data

Customer ID

Unique identifier of the customer who performs the transaction

X

Registration time

The time the user first registered in the platform

X

Product type

The type of product customer has

X

Customer tenure

Tenure of the customer

 

Email

Email address of the customer

X

Phone

Phone number of the customer, or phone prefix

 

Full Name 

The full name of the customer

X

Address

The address of the customer

 

Country

The country of the customer

X

City

The city of the customer

X

State

The state of the customer

X

Zip Code

The zip code of the customer

 

Year of Birth 

Year of birth of the customer

 

Annual Income

Annual income of the customer

 

Employer

Employer name of the customer

 


An example of customer profile data:

Customer ID

Name

Customer Tenure

Product Type

Email

Registration Time

53277897

Alice Jane

14 months

Premier Credit Card

alice_jane@gmail.com

2014-01-01 12:03:03

40098005

John Doe

60 months

Premier Credit Card

johndoe128@hotmail.com

2017-01-02 12:00:00


Data Structure Report

Before sending the full data over, please send over a data structure report to your Technical Account Manager. This process allows DataVisor to ensure a faster turnaround time and a higher quality of results during the fraud assessment. This data structure report should contain the following for each data set:

  • Schema
  • Preview of 10 rows
  • Earliest date and latest date (If there is an associated timestamp)
  • Number of rows

Data Anonymization

DataVisor’s algorithm is able to work with anonymized data fields if required. For sensitive PII information, such as name and address, anonymized information can be processed by our algorithm as long as the data structure remains.

Choosing a Connection Type: Batched or Real-time data transfer

For a batch data transfer, the client sends data in bulk to DataVisor. The frequency of batch data transfer is determined by client’s use case and business requirements. Batch transfers are faster to implement, as they don’t require an integration with DataVisor’s APIs. Account application data should be given as is at the end of the batch time period, and included in the batch. 

Real-time data connection

For a real-time data connection, the client integrates DataVisor’s APIs to send a real-time stream of data. After a ~2 week observation and tuning period, DataVisor returns real-time results, which can be used as an additional signal in the client’s fraud detection infrastructure. All account application data should be uploaded at the beginning of the fraud assessment. 

Formatting and transferring data

Formatting Data

DataVisor can accept data in the following formats:

  1. JSON (preferred) 
  2. Tab Delimited
  3. CSV

For other formats, like TXT or report exports, please contact your Technical Account Manager.

Example JSON format: 

{"txn_id":1203029281904, "customer_id":53277897, "txn_type":"Online", "event_time":"1423160520015", "time_zone":"UTC+0200", "amt":88.4, "txn_recipient":"Amazon Inc"}

Data Labels

Data labels are metadata that indicate which accounts are confirmed to be fraudulent. Although not required for DataVisor’s unsupervised technology, labels can be helpful to gauge results. If you have labels available, your DataVisor Technical Account Manager can help you determine whether it would be worthwhile providing them for the fraud assessment.

Transferring Data

DataVisor’s fraud detection solution can be deployed on-premise or in a cloud environment. For on-premise deployment, please contact us for customized data transfer details.

For cloud deployment, DataVisor will create an Amazon S3 bucket exclusively for you to ensure your data’s privacy. A customized python script will be provided to securely upload data to this bucket.

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