Improving testing efficiency is critical for companies today. Using a fully automated CI/CD process will help get you where you need to go when it comes to automating QA.

Discover how the LivePerson team is using Anodot to detect when a problem is brewing and to ensure more than 18,000 customers were getting the most out of their solution.

Advanced platforms already offer augmented analytics capabilities. Here’s how they’re creating a qualitative change for early adopters.

Find out why global companies like Payoneer, Credit Karma and SplitIt are using Anodot to detect and resolve operational incidents before they impact revenue and customers. The digital transformation and decentralization of the fintech segment has resulted in increasing complexities in payment transactions, more third party applications and a higher volume and velocity of payments data that need to be monitored in real-time. To make sure every payment transaction is completed as expected, payment operations teams must be able to find and fix payment issues as they’re happening anywhere along the end-to-end transaction path. Whether you’re a merchant, acquirer, or payments processor, it’s crucial to have complete visibility into your payments environment. Anodot’s powerful and easy-to-use anomaly detection and triage technologies help fintechs stay on top of their operations, deliver flawless customer experience, and optimize approval rates and fees. Anodot’s AI-driven platform learns the expected behavior across all permutations of digital banking — including payment approvals, merchant activity, partner APIs, deposits and withdrawals, login attempts, and more — and alerts teams in real-time to any incident, delivering the full context of what is happening, where and why.  

Anodot was one of 35 cutting-edge tech companies chosen to demo their software at the 2022 Finovate conference. Finovate conferences showcase banking and financial technology demos for senior executives in the industry. Presenting on behalf of Anodot was Yariv Zur, VP of Product at Anodot, and fintech executive Liron Diamant. Zur demonstrated how Anodot helps financial service and banking companies identify and remediate issues in the complex payment ecosystem. Attendees also learned how Anodot's AI-powered analytics platform can monitor, detect, correlate and remediate business critical incidents in real time. This gives financial service companies the ability to protect revenue and improve customer service. Fintech leaders like Payoneer, Tinkoff Bank, and eToro use Anodot to detect and resolve operational incidents before they impact the customer experiences. Anodot’s easy-to-use business monitoring solution helps fintechs stay on top of their operations, deliver flawless customer experience, and optimize approval rates and fees.

The fast-moving digital era has brought created the need for better, more automated revenue monitoring. Companies today can count on AI to monitor revenue streams.

Spotting anomalies is one thing. Making sense of them is another. In Part 3 of our guide, we focus on how to rank and correlate anomalies to uncover what really matters — and cut through alert fatigue in large-scale data environments. “For every anomaly found in a metric, there is a notion of how far it deviates from normal as well as how long the anomaly lasts. These notions are called deviation and duration, respectively.” What’s inside: How to assign significance scores to anomalies using Bayesian models Techniques for correlating related anomalies into a single, actionable incident The role of behavioral topology learning and metric relationships

Before you can detect what’s wrong, you need to understand what’s normal. Part 2 of our three-part guide focuses on how to model the "normal" behavior of time series data — a critical step in detecting anomalies accurately and at scale. “There are many patterns and distributions that are inherent to data. An anomaly detection system must model the data, but a single model does not fit all metrics.” What’s inside: Why assuming a single model or distribution is not enough How seasonality and changing patterns impact model accuracy Techniques for real-time, adaptive learning at scale Why automated, auto-tuned algorithms are essential for modern systems