Plaid upgrades its income verification suite

Monday 21 August 2023 10:20 CET | News

US-based data network Plaid has upgraded its income verification suite to improve automation while minimising fraud risk.


This product upgrade comes in the context of a recent shift towards digitisation, which has significantly transformed the way individuals manage their financial matters according to Plaid. The same source reveals that over 90% of Americans utilise digital financial applications to oversee their monetary affairs presently. Furthermore, as indicated by the Federal Reserve, one out of five Americans do not possess a credit card, resulting in a more intricate process of assessing their creditworthiness. 

With the accelerated pace of financial transactions, lenders are increasingly in need of real-time data to make more accurate decisions, while also tackling the rise of fraudulent activities and reducing costs. In this context, Plaid is announcing enhancements to Plaid Income, aimed at enhancing automation and minimising fraud risk. 

Incorporating cash flow data directly from sources into conventional credit reports offers consumers the ability to share their financial information during decision-making in real time. A recent survey conducted by Plaid revealed that 83% of traditional lenders expressed openness to integrating cash flow data such as bank account balances and transaction history to modernise lending options. Plaid's platform facilitates lenders in verifying borrower income through three methods: bank connection, payroll connection, or by uploading pay stubs or bank statements.


US-based data network Plaid has upgraded its income verification suite to improve automation while minimising fraud risk.


What are the main updates?

Plaid has implemented several updates to its product to provide users with access to accurate and current income data. Specifically, the machine learning model has been refined to better identify and categorise various income streams, including detecting pay frequency and categories, enabling users to gain a clearer understanding of customer finances. 

Moreover, a new feature has been introduced, allowing instant notifications of income fluctuations for customers engaged in salaried, gig, or freelance work. This aids in refining decision-making models. Plaid has also established new partnerships to enhance coverage of payroll data. 

While income documents remain crucial in lenders' underwriting processes, Plaid has broadened its support for these documents by including bank statements in addition to pay stubs and specific tax documents. 

The company has also stepped up its efforts to detect document tampering by integrating measures to detect document manipulation during the income document verification process. This includes identifying over 30 fraud indicators using machine learning technology, such as identifying if an original paystub has been altered.

A fraud risk score is assigned to each document, aiding lenders in determining appropriate actions. Apart from traditional credit reports, cash flow data can provide deeper insights into borrowers. Plaid's Assets API facilitates access to transaction histories spanning up to two years, lender-specific transaction categorisation, account balances, and account identity information.

Plaid's Identity Verification (IDV) seamlessly integrates with the onboarding process, alongside account linking and income checks, ensuring compliance with Know Your Customer (KYC) requirements. This integration enhances security and fraud prevention.

As for payments, the Plaid Transfer feature enables lenders to offer near-instant disbursements through ACH or Real-Time Payment rails, reducing the waiting period for borrowers to access funds.

For more information about Plaid, please check out their detailed profile in our dedicated, industry-specific Company Database.

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Keywords: product upgrade, credit scoring, fraud prevention, digitalisation
Categories: Fraud & Financial Crime
Companies: Plaid
Countries: United States
This article is part of category

Fraud & Financial Crime


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