AI-Driven Financial Forecasting: Leveraging Big Data And Machine Learning In Modern Payment Systems

Abstract

Artificial Intelligence (AI) is becoming a cornerstone of modern-day operations for several financial and retail Personal Finances Management (PFM) companies. Customer engagement and activation levels among the customer base are becoming more important KPIs as established players aggressively compete with new fintech startups for market share. It is critical to create an optimal blend of strategies and marketing techniques to successfully convert an at-risk customer into an active customer. In this regard, AI can make a huge impact through anomaly detection and forecasting of churn behavior of a customer across onboarding to active stages.

Churn is a strategic concern in payment processing as it poses a significant risk to the financial viability and success of the business. [1]Understanding and managing churn is a key factor in determining the long-term success of payment processing firms. It is increasingly important to gain an in-depth understanding of the churn process in modern competitive payment processing environments. Churn should not only be identified and predicted but also measures should be taken to react to these predictions. Focus should be on converting at-risk customers into active customers before it is too late.

While churn identification and prediction have been conducted extensively in the retail and fintech markets, there remains a lack of research in the payment processing domain. Additionally, almost all of these models are treated as black boxes which fall short of providing reasoning behind the predictions. Being a heavily regulated domain with high stakes involved, it is critical that churn prediction models be both highly accurate and interpretable. The development of such AI-powered predictive analytics tools can enable powerful capabilities in financial forecasting and ultimately help identify if the influx of data is a boon or a bane for its adoption in financial payments.