Building an AI-Powered Smart Dunning System for Optimized Payment Recovery

Introduction.

Failed transactions are a common problem in subscription-based e-commerce businesses, leading to involuntary churn and revenue loss. To optimize transaction recovery, businesses must adopt smart retry strategies. AI-powered dunning systems have emerged as a cutting-edge solution, using machine learning to predict the best times to retry failed payments. In this article, we explore how machine learning models can be applied to build effective dunning systems, weighing the pros and cons of each approach.

The Payment Retry Problem

In e-commerce, payment failures can arise from various factors such as insufficient funds, expired cards, or temporary network issues. Traditional retry methods rely on fixed schedules, which do not account for customer-specific behavior, time patterns, or payment details. As a result, businesses using these rigid systems miss opportunities to recover failed transactions effectively. A smart dunning system uses data-driven models to provide more personalized and efficient retry strategies, significantly improving recovery rates.

AI-Powered Smart Dunning

An effective smart dunning system relies on different machine learning techniques. Below, we break down key approaches and their roles in optimizing retries for payment recovery.

Supervised Learning Models:

Supervised learning models, such as Gradient Boosting Machines and Logistic Regression, are widely used in dunning systems. These models are trained on historical data of successful and failed retries, using a variety of features like customer payment history, transaction timing, and payment method to predict the probability of success for future retries.

Gradient Boosting Machines, known for their ability to model complex relationships, are highly accurate in identifying patterns in transaction data. On the other hand, Logistic Regression provides a more interpretable model, helping businesses understand the weight of each variable. Both models are well-suited for businesses with large datasets and can adapt to changing customer behaviour over time.

Time-Series Models:

Time-series models like ARIMA and Prophet focus on patterns related to time, such as daily or monthly cycles in payment behavior. These models predict when customers are most likely to have successful transactions based on historical trends. For example, if customers tend to pay successfully on specific days, the model can time retries to coincide with those peak periods.

While time-series models are effective at capturing temporal patterns, they lack the flexibility to consider other variables, such as customer behavior or payment method, which may also play a significant role in determining retry success.

Deep Learning (LSTM Networks):

For businesses with complex and dynamic transaction data, Long Short-Term Memory (LSTM) networks offer an advanced solution. LSTMs are a type of recurrent neural network that can analyze sequences of customer transactions over time, capturing dependencies between past behaviors and future outcomes. This makes them ideal for identifying long-term patterns in payment success.

However, LSTM models require large datasets and significant computational resources, making them more suitable for companies that can support the infrastructure needed for deep learning.

Reinforcement Learning:

Reinforcement learning (RL) models dynamically adapt retry strategies based on feedback from previous attempts. The system learns from each retry, improving over time by adjusting its approach according to the outcomes. This technique is highly effective in environments where conditions are constantly changing, allowing businesses to refine their retry logic based on real-time performance data.

While RL offers flexibility and the ability to learn continuously, its implementation can be complex and requires ongoing monitoring to ensure the system is performing optimally.

What to Use and When:

The choice of machine learning model for a smart dunning system depends on the specific needs and transaction patterns of the business. For companies with large datasets and straightforward customer behaviors, supervised learning models like Gradient Boosting Machines provide reliable and interpretable results. Time-series models are particularly useful for businesses with well-defined cyclical patterns in payment behavior, while LSTMs offer powerful solutions for those managing complex, long-term customer data. Reinforcement learning is most suitable for businesses that operate in dynamic environments where conditions change frequently.

Outcome and Impact. AI-Powered Smart Dunning for Augment Eco.

At Augment Eco, a European leader in subscription-based e-scooter services, we implemented an AI-powered smart dunning system to address their challenge of recovering failed transactions. By leveraging customer-specific data, payment methods, and time-based insights, we developed a system that could predict the optimal time for retrying failed payments. This resulted in a significant improvement in the success rate of retries and reduced customer churn.

We used a supervised learning approach that analyzed transaction history and payment behaviors, allowing the system to adjust retry attempts based on real-time data. Additionally, by incorporating features like payment method analysis and seasonal trends, we were able to personalize retry strategies for each customer, improving the overall recovery rate and revenue.

This project not only optimized Augment Eco’s payment retry process but also created a scalable, flexible system that can evolve with the business as it grows, reducing their reliance on third-party services and lowering operational costs.

Conclusion.

Managing failed transactions is a significant challenge for e-commerce businesses, but AI-powered smart dunning systems provide a robust solution. By employing the right machine learning models, businesses can optimize retry strategies, minimize customer churn, and enhance revenue recovery. Whether it’s using supervised learning for transparency, time-series models for cyclical trends, or deep learning for complex customer behaviors, the right approach depends on the unique transaction patterns of the business.

If you’re facing challenges with transaction recovery, let’s talk! Our team of AI experts, full-stack developers, and CTO-level strategists can design and build custom AI solutions tailored to your needs. Reach out to us today to explore how we can help you transform your business with AI.