ACH Risk

Use Case: ACH Risk

Problem

$62 trillion was transferred in the US via ACH in 2020. Efficiency is everything – but not at the expense of high risk. Consumers want their money transferred faster than the several days that ACH typically takes – but with speed often comes risk in ACH processing.

Mitigating ACH risk requires sophisticated cashflow and risk analysis in order to:

  • Ensure that the transfer to pay off a balance can clear
  • Understand historical transfer behavior
  • Detect return behavior for identifying potential chargeback fraud
  • Reduce the time consumers have to wait for an ACH transfer

How we help:

Attributes Endpoint

Pave’s 100+ attributes surface insights into a user’s cashflow and ability to make payments. Use these attributes in your own custom risk model. 

Connect your data sources to Pave and retrieve the following types of attributes within seconds:

  • Income history and predicted future income
  • Gambling habits and expenditure shocks
  • Current debt payments and predicted future bill amounts
  • Bank transfers and bank fees
  • 100+ more – we’re launching new attributes weekly

Affordability and Risk Scores

Pave’s affordability scores are trained on a large and growing dataset of payment history across our vast network of customers. Our scores help drive lift in your models for:

  • Predicting the likelihood of default on a cash advance or loan
  • Predicting failed payments or transfers
  • Predicting the advance or loan amount a user can afford to repay

Detect Other Financial Accounts

Often times a user will connect an account that has insufficient account history or doesn’t meet your qualifying criteria. 

Pave allows you to prompt users to connect additional accounts that have been detected in transaction history to:

  • Increase acceptances by prompting users to connect a different account if their connected account does not have sufficient history or data
  • Improve risk models by analyzing signals and behaviors across multiple connected accounts

Cashflow Analysis

Given that fintechs aren’t relying on credit scores to assess ACH risk, it’s necessary to accurately predict the expected balance by analyzing the user’s predicted income deposits, bill payments, transfer activity, and other risk signals.

Pave allows you to:

  • Receive a unified view of a user’s Cashflow patterns  
  • Receive a end-of-day balance for a user 
  • Get signals on a user’s typical behavior to monitor changes 
  • Lower NSFs and losses from failed ACH pulls