This in-depth article covers the value in using machine learning to create highly accurate, real-time, scalable forecasts for your business demand and growth.

After much debate, we ranked the most note-worthy anomalies of the 2010s - the most unexpected people, events and trends to shake the spheres of business, politics, entertainment and pop culture. Find out what - and who - made the list.

Razorpay, India's largest payment solution provider, enables frictionless transactions, revolutionizing money management for online businesses. Founded in 2014, Razorpay offers a fast, affordable, and secure way for merchants, schools, e-commerce, and other companies to accept and disburse payments online. The Challenge Solid anomaly detection is crucial for Razorpay, particularly when serving businesses in payment management. Sudden drops in success rate drops, ticket resolution delays, or fraudulent transactions can impact customer finances and decrease client satisfaction with Razorpay. Other issues Razorpay was facing: - Slow issue detection - Lack of real-time/near real-time alerts - Delayed critical alerts resulting in financial losses - Manual effort for anomaly and fraud detection - Challenges in tracking alerts across dimensions - Lengthy post-anomaly detection root cause analysis (RCA) The Solution Anodot was the partner Razorpay needed to address key issues like ticket resolution time and fraud detection. With a user-friendly UI for non-tech business users and ML forecasting capabilities, Razorpay can enhance the customer experience with automated monitoring and real-time anomaly detection. Main KPIs tracked in Anodot: - Payments SR in different business verticals - Customer success ticket creation and average time to close - Fraudulent transactions in different payment channels - Average payment checkout time - Refunds claimed as fraud Read Full Case Study Here  Anodot: Real-time alert and forecast platform using ML and AI for business monitoring   Real-time communication With Anodot, analytics and engineering teams can receive alerts across multiple channels, including Slack for seamless communication and collaboration for efficient monitoring and problem-solving.   Enhanced customer support  Anodot is open to building customer-requested features and provides a seamless onboarding experience to familiarize users with the tool quickly. Answering all questions and providing optimized, structured solutions.   Removal of manual anomaly detection Anodot's real-time alerts help reduce the financial impact on the company. Ops and analytics can spend less time fixing anomalies and more time on innovation and operational efficiency.   "Anodot is a valuable asset for sending timely alerts and notifications to the right recipients while facilitating quick and easy feedback."  Nishant Thakar BI and Data Strategy, Razorpay

While it certainly isn't meant to capture the full range of the Anodot platform, it does give you a sense of how you can dig deeper into your metrics to find hidden anomalies you may not have known were there.

“Anodot sets itself apart with automated anomaly detection, rather than manually setting thresholds.”

With Puma, we integrated revenue measures first as this is was their initial goal for using Anodot. However, while working with data, we decided to expand our view to a much broader metrics than just revenue.

With expanded use of microservices, you’ll find yourself confronted with challenges in meeting your service level agreement. These are the service mesh technologies and monitoring tools that will help you better manage service-to-service communication.

BioCatch is a leading cybersecurity company dedicated to protecting financial institutions from fraud while reducing friction in user experience. Trusted by more than 196 financial institutions and monitoring over 10 billion sessions per month, BioCatch brings context to every digital interaction—distinguishing genuine users from sophisticated threats. The Challenge: Monitoring a Complex Data Ecosystem at Scale   BioCatch's fraud detection system relies on dozens of proprietary calculations (“features”) that output either binary decisions (true/false) or risk scores (0–1000). These calculations power client-specific solutions across multiple device types, products and platforms. Any changes to these components could potentially skew features and influence fraud scores in unexpected ways. Key Pain Points: Dynamic threshold management: Previous monitoring tools couldn't learn thresholds dynamically and required extensive manual maintenance. Limited configuration options: Existing solutions lacked the flexibility needed to fine-tune alert rates to BioCatch's specific requirements. Scale challenges: With multiple inputs influencing scores across several dimensions, manual monitoring had become an impossible task Late detection: The team needed to identify changes to scores or input components as quickly as possible, before they could impact fraud risk assessment and erode client trust.  “There are so many moving parts in BioCatch’s ecosystem—our clients’ websites, third-party services and internal ETLs—any change can skew features and unexpectedly impact customer scores. We needed an ML-driven monitoring solution that learns patterns dynamically, without manual thresholds.” — Shira Mintz, VP Data Science, BioCatch   Why Automated Anomaly Detection Matters for Fraud Prevention In the fraud detection industry, maintaining accurate and stable scoring systems is critical for: Customer trust: Ensuring reliable fraud detection without false positives Brand reputation: Preventing fraud while maintaining smooth user experiences Operational efficiency: Enabling teams to focus on high-value analysis rather than manual monitoring Scalability: Supporting growth without proportional increases in monitoring overhead The Solution: Anodot's Cloud-Native Anomaly Detection Platform   BioCatch selected Anodot after evaluating solutions that could meet their specific requirements: Cloud-based service requiring no on-premises infrastructure. Dynamic learning capabilities that automatically fits optimal confidence band without manual threshold setting. Seamless integration with Snowflake and Datadog, for real-time data ingestion.  Implementation and Results The partnership between BioCatch and Anodot required collaborative iteration to optimize the solution for BioCatch's unique fraud detection use case. The implementation process involved multiple rounds of fine-tuning to achieve optimal alert configurations. Primary Use Cases   Biocatch’s data science team uses Anodot to monitor: Fraud scores returned to customers Internal components (such as user profiles) that influence scoring Various inputs across their fraud detection pipeline Key Benefits Achieved Improved Score Accuracy Early detection of changes to scores and influencing inputs ensures more accurate fraud detection, directly improving both BioCatch's reputation and their customers' experience. Real-Time Monitoring at Scale Automated monitoring eliminates the impossible task of manually tracking all inputs and scores across multiple customers, enabling BioCatch to scale their operations effectively. Reduced Maintenance Overhead Dynamic threshold learning significantly reduced the manual configuration and maintenance required compared to previous monitoring solutions. Reduced Detection Latency Issues that previously could go unnoticed for a long duration are now detected within hours, enabling rapid response and preventing prolonged impact on fraud detection accuracy. Alerts that make a Real (Time) Difference   Several weeks ago, a BioCatch customer made changes to data sent in API calls without informing BioCatch. These types of undocumented changes can significantly impact fraud detection accuracy and typically go unnoticed for weeks. However, Anodot detected the anomaly within hours, enabling BioCatch to respond immediately and maintain score accuracy. "Customers make changes on their side without understanding the implications to the data we receive and certainly without informing us. Anodot can usually alert on these changes within a few hours, giving us insight into unexpected changes to the data that could have gone unnoticed for weeks." — Daniel Gordon, Team Leader, Data Science Group, BioCatch Measurable Value and ROI   While BioCatch hasn't conducted formal ROI measurements, the practical value is clear in daily operations: High signal-to-noise ratio: False positives are easily filtered out, while true alerts provide extremely high value. Rapid response capability: Fast detection enables immediate corrective action before customer impact. Prevention of undetected issues: Catching problems that could otherwise persist for weeks without detection Advanced Capabilities Driving Additional Value   Beyond anomaly detection, BioCatch has leveraged several of Anodot's advanced features: Data enrichment in Anodot  The BioCatch team manages uniform features across multiple data sources. Anodot's ability to enrich data with source information enables the team to create a focused set of actionable alerts for comprehensive yet manageable monitoring. Dynamic Routing of Alerts Anodot's intelligent alert routing maps specific metric properties to associated channels. BioCatch implemented dynamic routing to ensure the right teams receive only relevant notifications, significantly reducing noise levels within shared channels. Influencing Metrics The 'influencing metrics' condition allows users to check any desired measurement before triggering an alert for a specific metric. BioCatch uses this feature to incorporate volume measurements into their alerting logic, customizing these conditions to achieve tighter control over false positive rates.

Payoneer's payment platform streamlines global commerce for more than 5 million small businesses, marketplaces, and enterprises from 200 countries and territories. Leveraging its robust technology, compliance, operations, and banking infrastructure, Payoneer delivers a suite of services that includes a cross-border payments, working capital, tax solutions, and risk management. Airbnb, Amazon, Google, WalMart and Rakuten are among its many customers. With millions of financial transactions happening on its platform 24x7, Payoneer closely monitors 190,000+ performance metrics in every area across the company. They are watching for any indication that something is off kilter with the business — for example, an unexpected decline in people registering for a new account, or a glitch in an API with third party software — in order to address issues quickly. Anodot helps Payoneer stay on top of its business through timely anomaly detection of various metrics and highly accurate forecasts for currency distributions. Here are a few examples of how Payoneer uses Anodot: Log Analysis to Quickly Spot Issues Yuval Molnar is Senior Director of Production Services at Payoneer. His group created an integration between the Coralogix log analysis platform and Anodot for autonomous anomaly detection. The metadata from every service Payoneer has — now some 1,000+ services — goes into Coralogix and then is fed into Anodot to look for anomalous behaviors within the logs. Using Anodot, Payoneer has been able to increase time to detect critical incidents by 90% and increase visibility into payment operations 3X. "Now we have a monitor in place that checks anomalies in terms of number of errors or number of logs, which is awesome," says Molner. "This is something we didn't have before and it's a game changer because we completely eliminated false positive alerts and vastly accelerated time to detection of real problems." Spotting Trends in Customer Care Payoneer monitors the types of calls coming into its customer care team. Every time a customer calls the care center, the service agent logs the subject and the sub-subject of the reported issue. From time to time, there is a trend where the call center is getting a lot of complaints about a specific subject. With Anodot, service agent reports are fed into the system. If there is a trend where a particular issues is increasing in frequency, Anodot will automatically product an alert in order to get the issue remediated quickly. Forecasting Currency Needs The nature of Payoneer's business is that customers can withdraw funds from their own accounts at any time. Payoneer must have sufficient funds available, in the currency the customers prefer, to meet withdrawal demands. The treasury team at Payoneer must make forecasts for account locations in more than 100 countries in 50 different currencies. The team had been computing the forecasts manually, relying on their own experience and using excel spreadsheets. Payoneer now uses Anodot's forecasting solution to learn patterns in data pertaining to customer withdrawals. Anodot is able to predict which currency could experience a shortfall. With Anodot, payment companies like Payoneer, fintechs, merchants and online sellers can benefit from autonomous payment monitoring. Our AI-based solution learns the behavior of each metric and adapts to seasonality. Anodot can identify payment anomalies even through fluctuating demand, proactively alerting payment teams, and automating resolution via KPI. Monitoring for Cybersecurity Issues Aviv Oren is EMEA Regional Manager of Production Services at Payoneer. He supports internal teams who need to know about anomalous conditions or activities. Oren calls it monitoring-as-a-service. "We are the ones getting alerts from Anodot on technical issues and we notify the appropriate groups about them," he says. "Before Anodot, we tried detecting events using static thresholds. That resulted in a lot of false positives. We got alerts when there was really no issue but had to check to make sure things were okay. That wasted a lot of our time," says Oren. Oren's team also monitors cybersecurity metrics to help detect malicious activity against the company's numerous applications and systems. For example, the production services team gets an alert when there's an unusually large number of successful logins, an increase in unsuccessful logins, or even a drop in successful logins. Anomalies in these metrics could be indicative that an application's login page is being attacked or harvested.