Large Earned Wage Access Provider Leveraged Pave's Cashflow Analytics

Reduce defaults by 27% and increase customer satisfaction by 28% with AI-powered cashflow intelligence

Transforming Risk Assessment with Real-Time Cashflow Data

A leading Earned Wage Access provider partnered with Pave to revolutionize their risk management approach, moving beyond traditional credit scoring to leverage real-time cashflow analytics for more accurate lending decisions.

By implementing Pave's Paycheck Prediction Model and Bill Tracking capabilities, they achieved remarkable improvements in both risk mitigation and customer experience.

27%

Reduction in Defaults

28%

Increase in Paycheck Prediction Accuracy

82%

User Engagement with Bill Tracking

Success #1

Reducing Defaults

Our Paycheck Prediction Model delivered exceptional results by providing unprecedented accuracy in predicting customer payment capabilities:

Detects payroll transactions with high precision

Clusters recurring payroll streams for pattern recognition

Accurately predicts the next payment date and amount

This model enables risk teams to assess payroll frequency to predict repayment likelihood, which in turn informs collections decisions and reduces overall portfolio risk.

Success #2

Empowering Cashflow Management through Bill Tracking

The EWA provider achieved 82% engagement on their Bill Tracking feature, helping users manage their cashflows by displaying upcoming bill details.

The Bill Tracking feature is powered by Pave's Recurring Expenditures Endpoint, which returns:

DA user's monthly bills

Upcoming bills and amounts

Bill categories, logos, and more

“"I have been RAVING about it! It is so convenient to know what is in my bank account and how much I need to save and spend that money”

— Customer Review

Upcoming Opportunities

Building on these successes, Pave is helping the EWA provider unlock even greater potential through enhanced cashflow analytics and scoring capabilities.

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Increase Approvals

Problem

Segment of users are being denied due to current underwriting criteria

Lack of income detection

Ineligible income

Solution

Approve users that would have otherwise been denied using Pave'sCash Advance History Scoreand income detection (Recurring Income)

Approve users who are approved by other providers

Create a fallback mechanism for denied users

Increase Advance Amounts

Problem

All users receive the same starting cash advance amount

Users need to prove repayment ability over months

Leads to poor user experience, churn

Solution

Increase advance amounts for customers by using theCash Advance History Score

Pave surfaces a new users' past repayment across other apps

Offer higher advances to new and existing users

Increase retention