Written by Don Lariviere
Operational efficiency and cost reduction are paramount priorities for modern businesses striving to stay competitive. As digital transactions surge, the hidden costs of payment processing are becoming more apparent—and surprisingly high.
That’s where data comes in. By leveraging real-time analytics and historical insights, companies can pinpoint inefficiencies, negotiate better rates, and optimize workflows. This post, brought to you by Proxet, explores how data-driven strategies can transform payment processing from a potential cost drain into a source of competitive advantage.
Payment routing optimization
By analyzing historical transaction data, businesses can dynamically route payments through the most cost-effective and reliable paths. This reduces transaction failures, minimizes fees, and accelerates processing times. Smart routing also helps in navigating regional regulations and achieving higher authorization rates.
A major e-commerce retailer, handling millions of transactions daily, used a payment orchestration platform powered by machine learning. By analyzing success rates and processing fees across multiple payment gateways and regional acquirers, they implemented dynamic routing. This automatically directed transactions to the provider with the highest success rate and lowest cost for each specific card type, currency, and geographic location. This optimization led to a significant increase in authorization rates and a reduction in overall processing fees by up to 15%, as they avoided higher charges from less efficient routes.
Source: Illustrative example based on capabilities of payment orchestration platforms like Adyen, Corefy or BlueSnap, which publicly discuss these benefits in their documentation and case studies.
Fee analysis and benchmarking
Transaction-level data allows companies to identify patterns in processing fees across acquirers, card networks, and geographies. Benchmarking these costs against industry standards reveals opportunities for negotiation or switching providers, ultimately reducing overhead. A large SaaS company found that their payment processing fees, while seemingly straightforward, varied significantly by transaction type and region. By implementing a detailed fee analysis system that ingested data from all their payment providers, they identified specific corridors (e.g., international transactions of a certain value) where they were paying above market rates. Armed with this granular data and benchmarking tools (like those offered by Pagos), they were able to renegotiate terms with their primary payment processor, achieving a 7% reduction in fees for high-volume international transactions and leading to six-figure annual savings.
Source: General industry practice and capabilities described by payment intelligence platforms such as Pagos, which specialize in helping businesses analyze and benchmark their payment costs. (See: pagos.ai)
Fraud detection and risk management
Machine learning models trained on transaction data can flag suspicious behavior in real-time. Proactive risk scoring and anomaly detection reduce fraud losses, improve compliance, and minimize manual review workloads.
As a prominent player in the payment processing industry, Aurora Payments provides comprehensive fraud protection services to enhance transaction security. While specific public case studies detailing their internal data analytics for fraud prevention are proprietary, the core of their offerings aligns with this principle. Payment processors like Adyen and Aurora leverage vast amounts of transaction data—including details like transaction amount, location, device, purchase history, and user behavior—to train advanced machine learning models. These models can detect subtle anomalies and patterns indicative of fraud that human analysts might miss. By integrating such data-driven solutions, Aurora Payments aims to proactively identify and prevent fraudulent transactions, thereby reducing chargebacks and safeguarding both their operations and their merchants' financial well-being. This capability is a cornerstone of a secure payment ecosystem.
Source: risewithaurora.com
Cash flow and settlement forecasting
Predictive analytics on payment inflows and outflows enable better liquidity management. Businesses can anticipate settlement delays, plan working capital needs, and optimize interest-earning cash reserves with increased accuracy.
A global manufacturing company struggled with unpredictable cash flow due to varying payment terms and international settlement cycles. They implemented an AI-powered cash flow management system that integrated data from their ERP, bank accounts, and payment gateways. By leveraging predictive analytics and machine learning, the system accurately forecasted daily cash positions up to 90 days out, considering seasonal trends, payment terms, and even historical settlement delays from different regions. This allowed the treasury team to optimize working capital allocation, reduce reliance on short-term borrowing, and increase returns on idle cash by intelligently deploying reserves, leading to millions in annual savings.
Source: Illustrative example based on capabilities described by solutions like SAP Taulia and Lucid Financials regarding AI-powered cash flow forecasting. (See: taulia.com/resources/blog/ai-powered-cash-flow-management-predictive-analytics-for-optimized-finance/)
Dispute resolution and chargeback analytics
Data-driven analysis of chargeback trends can help businesses understand root causes—whether operational errors, fraud, or customer dissatisfaction. This insight supports workflow improvements and better dispute win rates.
Online dating service eharmony faced a high volume of chargebacks, impacting their revenue and merchant account health. They implemented a chargeback analytics solution that categorized disputes by reason code, customer segment, and product. By analyzing the data, they discovered that a significant portion of chargebacks stemmed from "friendly fraud" (customers forgetting subscriptions) and unclear billing descriptors. They responded by improving their customer communication for renewals and implementing clearer billing statements. Furthermore, the data helped them build stronger evidence packets for legitimate disputes, leading to a reduction in overall chargebacks by 30% and an increase in their dispute win rate from 12% to over 60%.
Source: Case studies and capabilities discussed by chargeback management platforms like Chargeflow and Kount, which highlight significant reductions in chargebacks and improved win rates through data analysis and automation.
(See: chargeflow.io/blog/chargeback-statistics-trends-costs-solutions; kount.com/chargeback-response-case-studies)
Automated reconciliation
Automating reconciliation between internal records and bank/payment provider reports eliminates manual effort, reduces human error, and accelerates financial close processes. Data pipelines ensure near real-time accuracy in financial reporting.
Large enterprises like Walmart and Bank of America have successfully implemented automated reconciliation systems to streamline their financial operations. These industry leaders have utilized reconciliation software to automate tasks, identify errors, and integrate with their existing financial systems. This adoption has allowed them to cut their reconciliation time by up to 70% and achieve substantial reductions in human error rates. By automating this traditionally manual process, they've not only boosted efficiency but also improved the accuracy and speed of their financial reporting, crucial for such high-volume organizations.
Source: General industry trends and benefits described by financial automation solution providers like Fiserv and Nomentia, which emphasize time and cost savings through automated reconciliation. (See: fiserv.com/content/dam/fiserv-ent/archive-files/final-files/white-papers/Frontier-Reconciliation_whitepaper_1020.pdf; nomentia.com/blog/optimized-reconciliation-with-automation-expert-tips)
Transaction volume forecasting
Analyzing historical payment trends helps predict future volumes across channels and time periods. This supports better infrastructure scaling, SLA planning, and helps reduce over-provisioning of resources or under-preparation during peak times.
An online ticketing platform experienced significant fluctuations in transaction volumes due to event seasonality and sudden popular releases. By leveraging transaction volume forecasting models that analyzed past sales data, marketing campaign impacts, and external factors like holidays, they could predict peak loads with greater accuracy. This allowed them to dynamically scale their payment processing infrastructure (e.g., server capacity, database resources) and staffing for customer support and fraud review teams. This proactive scaling minimized system outages during peak times and optimized operational costs by avoiding unnecessary over-provisioning during slower periods.
Source: Illustrative example based on common challenges faced by high-volume transaction businesses and solutions provided by payment infrastructure and analytics companies. (See: J.P. Morgan's insights on "Five payment trends to help power your business in 2025" discussing similar concepts).
Customer segmentation and profitability analysis
Segmenting customers based on transaction behavior, payment preferences, and revenue contribution allows for better service tailoring and fee structuring. It helps prioritize high-value customers and optimize costs for less profitable segments.
A fintech company offering various payment services noticed that some customer segments were disproportionately costly to serve. By performing customer segmentation based on payment methods used, transaction frequency, average transaction value, and support inquiries, they identified their most profitable and least profitable customer groups. For less profitable segments, they introduced incentives for using lower-cost payment methods or self-service options. For high-value segments, they tailored premium service offerings and proactive support, improving retention and lifetime value, ultimately leading to a more optimized cost-to-serve ratio across their customer base.
Source: General marketing and business intelligence practices and insights provided by customer segmentation platforms like Userpilot, which discuss how understanding customer segments leads to tailored strategies and optimized resource allocation. (See: userpilot.com/blog/customer-segmentation-examples)
Conclusion
The digital economy demands that businesses move beyond viewing payment processing as a mere cost center. As demonstrated through these powerful use cases – from enhancing fraud detection for organizations like Aurora Payments and eharmony to automating reconciliation for giants like Walmart and Bank of America – leveraging data is the key to unlocking significant operational efficiencies and cost reductions.
By embracing advanced analytics, machine learning, and strategic data management, companies can optimize every facet of their payment ecosystem, from smart routing and fee analysis to predictive forecasting and customer segmentation. This transformation not only streamlines financial operations but also converts payment data into a strategic asset, driving smarter business decisions and fostering a sustainable competitive advantage.
Proxet can help you embark on this transformative journey.
With deep expertise in scalable data platforms, AI-powered analytics, and custom software development, we empower businesses to harness the full potential of their payment data. Whether you need to build robust data pipelines, implement sophisticated fraud detection models, automate complex reconciliation processes, or gain deeper insights into your payment flows, Proxet provides the strategic guidance and technical capabilities to turn these opportunities into tangible results for your organization.
Partner with us to revolutionize your payment operations and convert your data into profit.