Intelligent ACH: Predicting Returns Before They Happen

Key takeaways

• Intelligent ACH uses machine learning to predict which ACH transactions are likely to return before they are submitted, allowing a platform to proactively adjust actions.
• Predictions leverage customer history, account and transaction details, merchant patterns, cohort behavior, and external signals.
• A return probability score enables actions like timing optimization and pre-transaction outreach.
• Predictions are probabilistic, not certain, and subject to drift over time.
• Property management, subscription, and B2B subscriptions benefit the most from these predictions.

A property management platform runs its rent batch on the 1st. Some transactions return due to insufficient funds or closed accounts. The challenge lies in timing; the platform only learns about failures after they occur.

Intelligent ACH changes this by using machine learning to predict transaction outcomes based on historical data. For example, a tenant with multiple past R01 returns can be flagged before the batch is processed, allowing the platform to adjust its approach.

What does intelligent ACH mean?
Intelligent ACH integrates machine learning into transaction processing, shifting from reacting to returns to proactive prediction.

The prediction process involves optimal retry timing and transaction-level risk scoring, considering historical data and fraud signals.

What signals predict ACH returns?
ML models consider various signals:

End customer-level historical signals

• An end customer's past return history is a strong predictor of future returns.
• Specific return codes (e.g., R01 for insufficient funds) are more predictive than just the count.

End customer-level static signals

• Factors like account type and age affect baseline risks.

Transaction-level signals

• Amount and timing of transactions influence prediction probabilities.

Software customer-level signals

• A software customer's overall return rate can indicate structural issues.

Cohort-level signals

• Patterns among customers acquired through the same channel can inform predictions.

External signals

• Economic factors and significant events can affect return rates across all customers.

What can platforms do with return predictions?
Return probability scores enable operational changes:

Timing optimization

• Transactions submitted at optimal times reduce chances of returns.

Pre-transaction outreach

• Contacting customers about upcoming charges can prevent returns.

Method routing

• Alternative payment methods can be offered for high-probability returns.

Retry preparation

• Setting up immediate retry workflows for predicted returns.

Reserve management

• Adjusting reserves according to expected return volumes.

Software customer relationship management

• Engaging with customers who have high return rates for potential adjustments.

Fraud detection

• Flagging anomalous transactions before processing.

Operations capacity planning

• Preparing operations for potential workloads from predicted returns.

Limitations of ACH return prediction
Understanding the limitations is crucial:

• High return probabilities do not guarantee failure, necessitating caution in decision-making.
• Models may struggle with changing customer behaviors and cold-start issues for new accounts.
• Predictive actions must consider customer experience and compliance to avoid negative backlash.

How does Payabli support intelligent ACH?
Payabli offers tools and infrastructure for intelligent ACH, including:

• Comprehensive transaction history for accurate predictions.
• Contextual data on end customers and software customers.
• Real-time signals through webhook events.
• API access for integration of predictive capabilities.

Should you build or buy an intelligent ACH?
Considering whether to build in-house or leverage Payabli’s infrastructure depends on resources and expertise:

• Build: Requires substantial investment in ML expertise and maintenance.
• Use Payabli: Minimal integration needed, offering built-in intelligence for better outcomes.

Which verticals benefit from intelligent ACH?
Different verticals derive varying value:

• Property management: High value due to predictable payment patterns.
• Trade services: Moderate value with variable customer bases.
• Healthcare: Moderate; patterns can vary widely.
• Subscription services: High value during renewal cycles.
• Education: High value during tuition payment cycles.
• Government and utilities: Low overall impact due to already low return rates.
• B2B subscription: High economic impact from predictions.

Operationalizing predictions

• Pilot initiatives with limited scopes.
• Refine approaches based on results and continue scaling.

Continuous improvement

• Ongoing measurement should be built into workflow for sustained value.

The bottom line: ACH return prediction is becoming essential for platforms aiming to improve efficiencies and customer outcomes. Utilizing infrastructure with built-in intelligent capabilities provides significant advantages without the need for extensive ML investments.