Transform your data sources into attributes and scores with Pave.dev
Discover how to accelerate your model development from months to days
Pave maps multiple data sources to our standardized data model.
Our AI infrastructure ingests:
Pave processes billions of transactions in near real-time to surface signals including:
Supercharge your decisioning with Pave’s Scores and 450+ attributes offering a real-time view of a user’s affordability.
Lenders use these predictions for the following use cases:
The Problem
In an increasingly complex credit environment, lenders need to supplement credit bureau data with additional borrower context to understand risk in real-time. To put it in perspective, it can take up to 90 days after a payment’s due date for a lender to report a missed payment.
By relying on lagging affordability indicators like FICO, lenders can quickly face losses by offering credit to people who can’t afford it – which also puts the borrower under more financial strain.
Lenders want to figure out:
Our Solution
We help to:
Drive critical credit decisions by unlocking a complete and real-time view of your consumers’ financial profile that typical credit reports fail to provide.
Raw transactions from data aggregators will often miscategorize Cash Advance deposits as “Payroll”. In the sample dataset of 2,843 users, an aggregator miscategorized over half a million dollars worth of cash advance deposits as “Payroll” or income.
Across the board, we’ve seen aggregators incorrectly record people’s income as double or triple what it actually is. Not analyzing this data correctly could mean providing someone with a cash advance amount that they can’t afford to pay back.
Pave’s classifiers return granular and accurate tags to distinguish between different types of income, debt payments, and spending. In this sample dataset of 2,843 users, Pave accurately identified and tagged these same transactions as “Cash Advance” rather than payroll.
Pave analyzes the transaction description, date, amount, and other factors to accurately distinguish between Brigit cash advance deposits, repayments, and subscription payments. This way, lenders can better understand the nuances of a consumer’s risk.
Use our pre-built Scores trained on billions of labeled datapoints:
Use a growing number of signals in your models with our Attributes Store including:
Transform raw bank transactions with our Cashflow API including:
Discover how to accelerate your model development from months to days
Generate attributes and scores in near-real time via our APIs and turnkey access to Pave’s secure and standardized Snowflake data warehouse. Accelerate your development from quarters to days.
Reduce the burden on your data science, data engineering, and infrastructure teams to process billions of data points in-house. Focus on proprietary data sources, models, and unique areas of value.
Leverage a growing number of Attributes and Scores including loan repayment behavior, affordability metrics, income stability, spend shocks, likelihood to repay, and more.
Check out our use cases for cash advance risk, collections risk, overdraft risk, lead scoring, and more.