Why the Payments Industry Should Use AI to Improve OpEx and Customer Experience

Through digital transformation, payments companies have dramatically accelerated the speed of online transactions. With this positive evolution, however, comes inevitable challenges. For instance, the data volumes payments companies need to process are rapidly and continually expanding, which complicates ensuring smooth operations for customers and users – not to mention satisfying compliance requirements with various regulatory bodies. Increasing the number of employees on Operations and Finance teams can help mitigate payments failures and optimize payments behavior, however the exaggerated overhead can quickly become untenable.

IT and application monitoring tools can serve as an invaluable resource for establishing and maintaining smooth payments operations, but with so many monitoring tools available, many payments companies struggle with sprawl and insufficient resources to adequately maintain their systems.

In an attempt to cope with the slew of false positives produced by different monitoring tools, companies often find themselves saddled with a massive, expensive network operations center (NOC), and constant alerts transform working conditions into noisy, unfocused, fragmented and siloed environments. What’s more, too often payments companies are forced to conduct their monitoring efforts retroactively. With a lack of real-time, actionable insights, many monitoring tools end up doing little to promote efficient and accurate payments processes.

These challenges are common and understandable, however the unfortunate reality remains: Each and every time a payment fails, payments companies lose revenue and possibly customers, too. With transaction volumes continuing to explode, now more than ever organizations can’t afford any processing errors. Payments companies also can’t afford a lack of comprehensive business-level visibility and control, as it results in longer mean time to recovery (MTTR) for customer experience, greater customer churn and a variety of revenue-related issues — all of which can lead to bad press and damaged brand reputation.

Additionally, when monitoring systems are incapable of processing critical payment transactions quickly and scalably enough for today’s realities, payments companies run the risk of failing to comply with service-level agreements and any federal/government regulations, which can lead to financial penalties and/or lawsuits.

A leader in digital payments experienced business-level visibility challenges firsthand

Digital payments company Payoneer experienced some of these processing and business-level visibility challenges firsthand. A global payment and commerce-enabling platform that powers growth for millions of small businesses, marketplaces and enterprises, including eBay, Amazon, Google and Walmart, Payoneer delivers a suite of services that include cross-border payments, working capital, tax solutions, risk management and payment orchestration for merchants. With more than five million customers worldwide, the company monitors millions of business and technical metrics to keep their payment gateway running smoothly.

Initially, Payoneer relied on traditional monitoring and log analysis solutions. However this manual, multi-system approach led to siloed monitoring, and a cumbersome and incomplete view of business processes. The burden fell on production services to configure alerting (as opposed to individual teams), incidents took at least 24 hours to resolve and high false positive rates drained resources. Overall, the company’s existing, resource-intensive process to integrate new data sources to maintain business-level visibility simply wasn’t sustainable.

User-friendly AI enabled increased visibility 3X and improved time to resolution by 90%

Payoneer quickly recognized the need for a new approach and began using AI to automate their business monitoring. With AI technology, Payoneer was able to integrate their business monitoring and data platform, enabling them to substantially improve how they leveraged their existing data to find and remediate issues that otherwise would have been missed. Manual monitoring and inefficient internal systems that had long overextended IT and Operations teams were replaced with a turn-key platform capable of autonomous monitoring and real-time anomaly detection. By providing access to cross-silo visibility, Payoneer’s AI implementation also allowed multiple teams across the company to work from one, cohesive monitoring platform and instantly identify incidents most applicable to them.

Most importantly, Payoneer was able to efficiently overcome its visibility challenges by choosing to automate their business monitoring with user-friendly AI technology that anyone in the company could use — not just engineers or data scientists. By leveraging accessible automation to monitor all service logs, detect any errors and false positives, and accurately identify root causes, more teams were able to take ownership of payments optimization and confidently maintain accountability. Operations and Finance teams, in particular, were able to use AI to efficiently handle reconciliation and boost payments approval rates, which ultimately contributed to greater success for the business through improved customer satisfaction and revenue loss prevention. To date, Payoneer’s user-friendly AI implementation has increased the company’s visibility by 3X and improved their time to resolution by 90%. 

Industry relevance and success requires autonomous business monitoring

To manage monitoring system sprawl and gain real-time, actionable, business-level visibility, today’s leading payment companies need to incorporate integrated and accessible AI technology, i.e., AI that’s business-focused and intuitive for all employees, not just IT teams. By moving away from inefficient, manual monitoring, the speed of digital payment processes can be accelerated even further, transactional issues can be found and fixed as they occur, and OpEx can be streamlined.

With fewer disparate tools to manually maintain, payments companies can also gain the opportunity to free up valuable resources, refocus team capacity on innovation and improve their competitive market position. Furthermore, by embracing autonomous business monitoring, payment companies can eliminate unproductive work cultures with little confidence or accountability, improve customer experiences, and boost lifetime customer value and overall relationships.

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為什麼支付行業應該使用人工智能來改善運營支出和客戶體驗

通過數字化轉型，支付公司大大加快了在線交易的速度。然而，隨著這種積極的演變，不可避免的挑戰也隨之而來。例如，支付公司需要處理的數據量正在快速且持續地擴大，這使得確保客戶和用戶的順利運營變得複雜——更不用說滿足各種監管機構的合規要求了。增加運營和財務團隊的員工人數可以幫助減少支付失敗並優化支付行為，但是誇大的開銷很快就會變得站不住腳。

IT 和應用程序監控工具可以作為建立和維護順暢支付操作的寶貴資源，但是由於可用的監控工具如此之多，許多支付公司都在努力應對蔓延和資源不足的問題，無法充分維護其係統。

為了應對由不同監控工具產生的大量誤報，公司經常發現自己背負著龐大、昂貴的網絡運營中心 (NOC)，不斷的警報將工作條件轉變為嘈雜、分散、分散和孤立的環境。更重要的是，支付公司經常被迫追溯性地進行監控。由於缺乏實時、可操作的洞察力，許多監控工具最終在促進高效和準確的支付流程方面無能為力。

這些挑戰是常見且可以理解的，但不幸的現實仍然存在：每次付款失敗時，支付公司都會失去收入，也可能失去客戶。隨著交易量的持續爆炸式增長，現在組織比以往任何時候都無法承受任何處理錯誤。支付公司也無法承受缺乏全面的業務級可見性和控制，因為這會導致更長的客戶體驗平均恢復時間 (MTTR)、更大的客戶流失和各種與收入相關的問題——所有這些都可以導致不良媒體和品牌聲譽受損。

此外，當監控系統無法快速且可擴展地處理關鍵支付交易以適應當今現實時，支付公司將面臨不遵守服務水平協議和任何联邦/政府法規的風險，這可能導致經濟處罰和/或訴訟。

數字支付領域的領導者親身經歷了業務級別的可見性挑戰

數字支付公司 Payoneer 親身經歷了其中一些處理和業務級可見性挑戰。 Payoneer 是一個全球支付和商業支持平台，為包括 eBay、亞馬遜、谷歌和沃爾瑪在內的數百萬小型企業、市場和企業的增長提供動力，Payoneer 提供一套服務，包括跨境支付、營運資金、稅務解決方案、風險商戶的管理和支付編排。該公司在全球擁有超過 500 萬客戶，監控數以百萬計的業務和技術指標，以確保其支付網關平穩運行。

最初，Payoneer 依賴於傳統的監控和日誌分析解決方案。然而，這種手動、多系統的方法導致了孤立的監控，以及繁瑣和不完整的業務流程視圖。配置警報的負擔落在了生產服務上（而不是單個團隊），事件至少需要 24 小時才能解決，而且高誤報率耗盡了資源。總體而言，該公司現有的、資源密集型的、用於集成新數據源以保持業務級可見性的流程是不可持續的。

用戶友好的 AI 使可見性提高了 3 倍，解決時間縮短了 90%

Payoneer 很快意識到需要一種新方法，並開始使用人工智能來自動化他們的業務監控。通過人工智能技術，Payoneer 能夠整合他們的業務監控和數據平台，使他們能夠顯著改善他們利用現有數據來發現和修復原本會被遺漏的問題的方式。長期過度擴展 IT 和運營團隊的手動監控和低效的內部系統被一個能夠自主監控和實時異常檢測的交鑰匙平台所取代。通過提供跨孤島可見性的訪問權限，Payoneer 的 AI 實施還允許公司內的多個團隊在一個有凝聚力的監控平台上工作，並立即識別最適合他們的事件。

最重要的是，Payoneer 能夠通過選擇使用公司中任何人（不僅僅是工程師或數據科學家）都可以使用的用戶友好型 AI 技術來自動化其業務監控，從而有效地克服其可見性挑戰。通過利用可訪問的自動化來監控所有服務日誌、檢測任何錯誤和誤報並準確識別根本原因，更多的團隊能夠掌控支付優化並自信地保持問責制。尤其是運營和財務團隊能夠使用人工智能有效地處理對賬並提高付款批准率，最終通過提高客戶滿意度和預防收入損失為業務取得更大成功做出貢獻。迄今為止，Payoneer 的用戶友好型人工智能實施已將公司的知名度提高了 3 倍，並將解決問題的時間縮短了 90%。

行業相關性和成功需要自主的業務監控

為了管理監控系統的蔓延並獲得實時、可操作的業務級可見性，當今領先的支付公司需要整合集成且易於訪問的 AI 技術，即以業務為中心且對所有員工（而不僅僅是 IT 團隊）具有直觀性的 AI。通過擺脫低效的手動監控，可以進一步加快數字支付流程的速度，可以在交易問題發生時發現並解決它們，並且可以簡化運營支出。

由於需要手動維護的不同工具更少，支付公司還可以獲得釋放寶貴資源的機會，將團隊能力重新集中在創新上，並提高其競爭市場地位。此外，通過採用自主業務監控，支付公司可以消除缺乏信心或責任感的非生產性工作文化，改善客戶體驗，並提高終生客戶價值和整體關係。