Data Integration Guide - Loan Applications

Loan Application Data for Analysis

Application data fields, as well as customer profile data fields associated with the application, are an important driver of DataVisor’s ability to identify loan application fraud. This document serves as a general integration guideline for identifying application fraud as it pertains specifically to loans. 

Loan Application Data

Fields that are helpful for loan application use cases include (but are not limited to):

Category	Data Field	Description	Required
Account-Level Information	Account Number	Unique account number	X
	Loan Application ID	A unique ID that identifies the specific loan application	X
Bank-Level Information  (if available)	Bank Account Type	The type of account to which the loaned amount is sent to (e.g. savings, checking, etc.)
	Bank Account Number	The applicant’s bank account # for the account to which the loan will be disbursed
	Bank Routing Number	The applicant’s bank routing #
	Bank Account Name	The full name of the applicant as found in their bank account
	Bank Account Balance	The balance in the applicant’s bank account
Credit Information	FICO Score	The FICO Score of the applicant, as pulled from a credit report	X
	Credit Report Pull Date	The date when the report was last pulled by the loaning institution	X
	Credit Report Provider	The credit report agency from which the report was pulled (e.g. Equifax, Experian)
	Other Credit Report Data	Any other fields that are parsed from the credit report (as applicable)
	# of Recent Credit Inquiries	The number of credit inquiries requested by the applicant in the last X months (X could be 6, 12, 24, etc.)	X
Loan-Level Information	Loan Amount	The amount requested in the loan application (in US dollars)	X
	Loan Type	The type of requested loan (personal, commercial, business, etc.)	X
	Loan Drill-Down Info	Any channel-specific information around the loan  (e.g. for auto loans, make/model of car)
	Loan Product	The type of loan product, if applicable
	Loan Requested Amount	The amount requested by the applicant on the loan. May differ from the final loan amount	X
	Loan Application Time	Timestamp of the loan application	X
	Loan Duration	The total length of the loan period, preferably in either days or months	X
	Monthly Payment	Estimated monthly payment on the loan, if calculated by the lender
	# Good Payoffs in last X months	How many loans were successfully paid off by the applicant in the past X months (commonly X = 12 or 24)
	# of Delinquent Payments in last X months	How many loans had delinquent / no payment by the applicant in the past X months (commonly X = 12 or 24)
	Cosigner / Joint Applicant?	Is there a joint applicant or co-signer on this loan?	X
	Cosigner / Joint Applicant Information	If applicable, all personal info about the joint applicant or co-signer (name, email, address, phone, SSN, etc)
	OFAC Sanction Status	Whether the applicant appears on any OFAC sanction lists
	Citizenship Status	Is the applicant a US Citizen or Permanent Resident?
	Lender Branch Information	If applicant applied in person, branch geo information (address, latitude / longitude)
	Device Information	If the applicant applied online, information around the device used to apply (e.g. IP, Device ID, including 3rd party signals)
Label	Loan Never Paid Indicator	An indicator of whether the loan has not been paid off within X days  (X based on lender’s business policies)	X
	Loan Charge Off Status	A status of whether the lender has marked the loan as charged off	X
	Loan Charge Off Date	The date on which the loan was charged off by the lender	X
	Loan Charge Off Reason	The reason for charge off, if one is available

An example of loan application data:

Loan App ID	Account ID	Application Type	Transaction Time	Loan Requested Amount	Loan Duration	...
1203029281904	53277897	Business Loan	2022-04-16 12:35:06	4000.00	24	...
9303283057123	40098005	Personal Loan	2022-04-16 12:35:06	2500.00	12	...

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 / SSN	Unique identifier of the customer who performs the transaction	X
	Registration time	The time the user first registered with the loan provider	X
	Registration Channel	The method through which the customer first registered for an account with the lender
	Registration Address	For in-person registrations, the address of the location where the customer first registered for an account
	Registration IP	For online registrations, the IP from the device where the customer first registered for an account
	Product types	A list of products that the customer has previously subscribed to from the lender	X
	Customer tenure	Tenure of the customer
	Email	Email address of the customer	X
	Phone	Phone number of the customer, or phone prefix	X
	Full Name	The full name of the customer	X
	Address	The address of the customer	X
	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	X
	Date of Birth	Year of birth of the customer
	Annual Income	Annual income of the customer
	Monthly Rent	Monthly rent paid by the customer
	Employment Tenure	How long the customer has been working for their current employer
	Employment Status	Customer’s current employment status
	Employer	Employer name of the customer
	Applicant History	Any information about previous loan applications from the customer. Includes the following:  Prior accepted or declined loans Payoff status of such loans Loan amount for each loan previously applied	X

An example of customer profile data:

Customer ID	Name	Customer Tenure	Product Type	Email	Registration Time
53277897	Alice Jane	14 months	Premier Credit Card	[e-mail]	2014-01-01 12:03:03
40098005	John Doe	60 months	Premier Credit Card	[e-mail]	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.