Anodot vs. Databricks Built Monitoring   Audience: Data, Operations, and Product leaders evaluating anomaly detection/monitoring platforms vs. a Databricks‑native build TL;DR  Anodot delivers turnkey, low‑noise anomaly detection with cross‑metric correlation, significance scoring, event-aware baselines, and an incident UX at enterprise scale. Databricks‑built solutions can reach high accuracy using your choice of models and Spark‑scale pipelines, but you must assemble feature engineering, training/retraining, correlation logic, alerting, and the incident workflow. What “quality of monitoring” means  Signal vs. noise: Anodot groups related anomalies into a single incident and ranks by significance to reduce alert storms; Databricks requires custom rules/joins to correlate signals from multiple metrics. Seasonality & events: Anodot baselines adapt to hourly/daily/weekly patterns and influencing events (e.g., holidays, launches). In Databricks you engineer features (calendars/promotions), choose models (Prophet/ARIMA/LSTM/Isolation Forest), and manage retraining. Context & RCA: Anodot’s incident view links correlated anomalies and contributing factors with optional business‑impact estimates. In Databricks you compose dashboards (Databricks SQL/Lakeview) and join detections back to dimensions/events. Head‑to‑head summary Example use cases (non‑industry specific) Traffic & conversion funnel – Detect drop in conversion in a specific region/browser version; correlate with latency spikes and a deployment event → one incident with priority. Data pipeline health – Spike in late‑arriving records in a bronze table; correlate with upstream API errors and streaming backlog. Cost & usage anomalies – Sudden increase in compute hours for a single workspace; correlate with schedule changes and job failures. In each case, Anodot (a) learns normal per segment, (b) correlates co‑moving anomalies, and (c) provides incident context/impact to accelerate RCA. Time to value Anodot: Connect Snowflake/Databricks/DBs/streams; auto‑discover metrics/dimensions; start baselining immediately; first alerts in hours/days. Databricks build: Data modeling → feature pipelines (DLT/Jobs) → train/evaluate (MLflow) → batch/real‑time serving (Mosaic AI Model Serving) → Databricks SQL Alerts → triage UX (dashboards or custom app).; first alerts in weeks to months Maintenance & ownership Anodot: Managed retraining for seasonality &drift; correlation/noise reduction; alert routing to Slack/PagerDuty/ServiceNow/JIRA/Opsgenie. Databricks build: Manage clusters, orchestration (Jobs/DLT), model registry/serving, drift monitoring, schema changes, alert logic, and dashboards. Capabilities not native to Databricks (you must build them) Cross‑metric correlation & incident grouping Significance scoring to prioritize incidents Holiday/event‑aware baselines (influencing events) Impact‑value estimation in‑alert Turnkey incident UX with noise controls When a Databricks build can make sense Need highly customized modeling or governance flows within Lakehouse. Narrow scope (limited metrics/sources) or exploratory R&D. Strong preference to keep all logic in‑house and staffed platform/ML teams. Architecture options Hybrid: Keep data in Databricks Delta; connect Anodot for detection/correlation; send incidents to collaboration/on‑call tools. Preserves data‑residency while accelerating TTV. References (selected) Anodot • Incident correlation & noise reduction: https://www.anodot.com/incident-detection/ • Influencing Events (event‑aware baselines): https://support.anodot.com/hc/en-us/articles/360017086719-Influencing-Events • Events overview (types incl. suppress/office hours): https://support.anodot.com/hc/en-us/articles/209776765-Events-Overview • Integrations & alert channels: https://support.anodot.com/hc/en-us/categories/201105765-Integrations • Scale & system paper: https://proceedings.mlr.press/v71/toledano18a/toledano18a.pdf • Platform case study: https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2022-12/anodot-case-study.pdf Databricks • Databricks SQL Alerts: https://docs.databricks.com/sql/user/alerts/ • Anomaly detection (data quality monitoring): https://docs.databricks.com/data-quality-monitoring/anomaly-detection/ • Delta Live Tables (pipelines/orchestration): https://www.databricks.com/discover/pages/getting-started-with-delta-live-tables • MLflow model lifecycle & registry: https://docs.databricks.com/mlflow/models • Workspace/Unity Catalog model registry details: https://docs.databricks.com/machine-learning/manage-model-lifecycle/workspace-model-registry • Mosaic AI Model Serving (real‑time): https://docs.databricks.com/machine-learning/model-serving/ • Batch inference patterns: https://docs.databricks.com/machine-learning/model-inference/ Recommendation: For broad, reliable monitoring with quick TTV and minimal upkeep, choose Anodot—optionally via a hybrid pattern to keep Databricks as your data plane while Anodot handles detection, correlation, and incident workflow.  

Anodot vs. Snowflake DIY Anomaly Detection based Monitoring Audience: Data, Risk, and Product leaders evaluating anomaly detection for revenue protection TL;DR  Anodot delivers higher‑quality, lower‑noise anomaly detection at enterprise scale with correlation, significance scoring, and incident UX out‑of‑the‑box.  Snowflake‑built solutions can work for simple single‑metric checks or when you want full custom control, but they require significant engineering and ongoing maintenance.  What “quality of monitoring” means  Signal vs. noise: Anodot groups related anomalies into a single incident and ranks by significance; Snowflake functions flag anomalies but you must build correlation/dedup logic.  Seasonality & events: Anodot learns hourly/daily/weekly baselines and incorporates holidays/ promotions; in Snowflake you must engineer features (calendars, promotions) and retraining. Context & RCA: Anodot provides incident views with dimensions and contributing factors; Snowflake requires you to build joins, dashboards, and triage UX (e.g., Streamlit/SiS).  Head‑to‑head summary     Fintech revenue‑protection examples (Anodot)  1) Payments authorization drop  Signal: 12% YoY dip in approval rate for BINs tied to Region X from 09:00–11:00.  Correlation: Concurrent spike in 3DS challenge rates + gateway timeouts. Outcome: Single incident auto‑routed; impact estimate: −$425k projected daily revenue if unresolved.  2) Card funding/Top‑up latency  Signal: P95 latency for bank‑transfer top‑ups exceeds learned band only for a specific PSP + currency. Correlation: Queue backlog anomalies on the ETL path feeding the ledger table.  Outcome: Ops and PSP team paged once (not 20 times); customer‑visible incidents prevented.  3) Chargeback anomaly by merchant cluster  Signal: Standardized residuals on chargeback rate spike for mid‑risk MCC cluster.  Correlation: Marketing campaign event overlay + new fraud‑model rollout window. Outcome: Rapid root cause isolation, campaign throttled, fraud rules adjusted.  In each case, Anodot: (a) learns normal per segment, (b) correlates co‑moving metrics, (c) provides incident context + optional $‑impact.  Time to value  Anodot: Connect Snowflake (and other sources), auto‑discover metrics/dimensions, begin baselining; first useful alerts typically within hours/days.  Snowflake build: Data modeling → feature pipelines → model creation  ( SNOWFLAKE.ML.ANOMALY_DETECTION ) → inference jobs → alerting → triage UX. Expect weeks before wide coverage; months to reach parity on correlation/noise reduction.    Maintenance & ownership  Anodot: Managed model lifecycle (seasonality, drift), correlation tuning, noise reduction, integrations (Slack, PagerDuty, ServiceNow, JIRA, Opsgenie).  Snowflake build: You own tasks, warehouse sizing, model retraining cadence, schema drift handling, dedup/aggregation logic, UI, on‑call for failures.  Capabilities you don’t get “for free” in Snowflake  Cross‑metric correlation & incident grouping  Significance scoring to prioritize   Holiday/event‑aware baselines (“influencing events”)   Built‑in $‑impact estimation   Incident UX out‑of‑the‑box with deep integrations  When a Snowflake‑native build can make sense  Narrow scope (few metrics), simple thresholds.  •   Strong need for fully custom logic with a staffed ML/platform team.  •   Strict data‑residency constraints and acceptance of the engineering investment. Architecture options  Hybrid (recommended for Snowflake‑first teams): Keep data in Snowflake; connect Anodot for  detection/correlation; send incidents to Slack/PagerDuty/ServiceNow. Minimal data movement, fastest value.  Anodot  Product overview & anomaly detection: https://www.anodot.com/platform/  Correlation & incidents: https://www.anodot.com/product/anomaly-detection/  Business impact (Impact Value) & event awareness: https://www.anodot.com/product/business monitoring/  Snowflake connector & integrations: https://www.anodot.com/integrations/snowflake/  Snowflake  Snowflake ML Anomaly Detection (SQL): https://docs.snowflake.com/en/user-guide/snowflake-ml/ anomaly-detection  Cortex ML functions overview: https://docs.snowflake.com/en/user-guide/snowflake-cortex/ • Alerts & Notifications: https://docs.snowflake.com/en/user-guide/alerts  Tasks & scheduling: https://docs.snowflake.com/en/user-guide/tasks  Streamlit in Snowflake (incident/triage UI option): https://docs.snowflake.com/en/user-guide/ui-streamlit    Recommendation: For broad, low‑noise fintech revenue protection with quick ROI and minimal upkeep, adopt Anodot—optionally via the hybrid pattern to keep Snowflake at the core.   

In a recent article published by Economic Times on Dec 29, 2023, titled "Banks Told to Explore Dashboard with Real-Time Info on Services," the Reserve Bank of India (RBI) has urged banks to embrace real-time transparency through the creation of an online dashboard. Anodot, a leader in business monitoring, is at the forefront of transforming the banking sector with its advanced real-time business monitoring dashboard designed for internal usage within banks. [embed]https://youtu.be/hRM_yX9zu1I[/embed] What does this mean? It means it's high time for banks to embrace the significance of real-time actions, and dashboards (like Anodot) can be here to lend a hand.   Why Anodot's Real-Time Monitoring is the Best for Banking Excellence As banks grapple with technical glitches causing service disruptions, Anodot offers a robust solution—an advanced real-time monitoring dashboard designed for internal use. This dashboard empowers banks to proactively identify issues that could affect security, revenue, or customer experience, ensuring a seamless and secure banking environment. Immediate Benefits for Banks with Anodot features Anodot's real-time monitoring dashboard provides banks with unparalleled capabilities to: Optimize Revenue Streams: Immediately Identify anomalies impacting revenue streams, allowing swift corrective action to be taken. Enhance Security: Detect potential security threats in real-time, safeguarding sensitive information and customer data. Improve Customer Experience: Proactively address issues that could impact customer experience, ensuring satisfaction and loyalty.   Banks, facing penalties for service disruptions now have an opportunity to elevate their monitoring capabilities with Anodot's solution. By integrating Anodot's real-time dashboard into their operations, banks can move beyond reactive measures and adopt a proactive stance, identifying and resolving issues before they escalate. Insights for Retail Investors For investors in bank stocks, Anodot's real-time dashboard underscores the importance of technology in mitigating risks. It highlights the potential for banks to invest in advanced business monitoring solutions, enhancing their resilience and market standing. Impact on Industries: Opportunities with Anodot Anodot's real-time monitoring dashboard has far-reaching effects on various industries: Fintech Software: Integrating Anodot's solution becomes pivotal for offering robust and secure financial services. IT Services & Infrastructure: Anodot's advanced monitoring drives demand for modernization and proactive issue resolution. Telecom: Ensuring connectivity for seamless banking services gains prominence with Anodot's real-time insights. AdTech and Gaming: Anodot's success in AdTech and Gaming showcases the adaptability of its monitoring solutions, providing insights and trust in dynamic and high-transaction-volume environments.   Anodot's Exceptional Pedigree in Real-time Monitoring Excellence Long-Term Structural Improvements Anodot has over ten years of experience providing real-time monitoring that can lead to valuable insights. Resilient Infrastructure: Banks undergo a structural overhaul, enhancing their technology foundations for peak capacity, security, and process complexity. Responsible Innovation: The rollout of new features prioritizes stability, aligning innovation with execution capability. Level Playing Field: Uniform visibility enables objective comparisons, preventing weak IT budgets from hiding behind marketing claims. Business Continuity: Standardized redundancies, instant failovers, and geographic contingencies minimize the impact of outages. Trust Building: Reliable functionality establishes credibility, driving financial inclusion and a more inclusive banking experience. However, balancing transparency responsibly is crucial to avoid trivializing customer complaints without appreciating the scale of difficulties involved. Short-Term Positives: Immediate Benefits and Scrutiny with Anodot In the short term, Anodot's real-time monitoring dashboard offers: Granular Insights: Instant visibility enables proactive issue resolution, ensuring a secure and seamless banking experience. Optimized Operations: Swift corrective actions based on Anodot's insights result in revenue optimization and enhanced customer satisfaction. Vendor Accountability: Clear visibility into recurring issues allows banks to hold third-party vendors accountable, ensuring robust partnerships. Leadership Motivation: Public metrics serve as a performance indicator, motivating urgency in stabilizing systems. The extent of progress depends on the successful integration of Anodot's real-time dashboard across banks and effective communication within the industry. Companies Impacted with Anodot The adoption of Anodot's real-time monitoring dashboard has different implications for various companies, including Companies Gaining with Anodot: Fintech Companies with Real-time Data Analytics Solutions powered by Anodot Digital Payment Infrastructure Providers benefitting from Anodot's reliability insights Banks with Robust Digital Infrastructure leveraging Anodot's predictive analytics IT Consulting and System Integration Firms with expertise in Anodot-powered solutions Anodot collects and analyzes data across the entire payment stack and ecosystem. All metrics are monitored at scale, enabling operators to achieve complete visibility over the payments environment. Anodot’s patented correlation engine correlates anomalies across the business for holistic root cause analysis and the fastest time to resolution, leading to significantly improved approval rate, performance, availability, and customer experience.   Banking Excellence is Possible with Anodot as a Partner As the Economic Times highlights the RBI's push for real-time transparency, Anodot is here to equip banks with advanced monitoring capabilities. With its powerful dashboard, Anodot is a game-changer for banks looking to thrive in the digital age by boosting revenue, enhancing security, and improving customer experience. And it's not just limited to banking—Anodot's adaptability spans industries like fintech and telecom, backed by a decade of specialized experience. By integrating these real-time insights, banks protect their operations and foster trust and loyalty among customers, gaining a competitive edge. The message is crystal clear: embracing proactive, real-time monitoring with Anodot isn't just about avoiding penalties or disruptions—it's about seizing an opportunity for transformation and delivering excellent service.   Discover how Anodot can enhance your financial business with flawless customer experience, payment optimization, and operational excellence. Let's talk.   

The gaming industry has always been a highly lucrative and adored field. According to online gaming industry statistics, it is projected to surpass $33.77 billion by 2026. However, a downside emerges when governments impose substantial taxes on the income generated from gaming. It's happening now. The Indian government has decided to impose a 28% tax on online gaming, which may lead to a funding shortage and a decrease in investor confidence. Undoubtedly, many gaming companies will look for new strategies to save costs. Business monitoring is a powerful strategy that reduces costs, maintains performance, and enhances user experience. Let's explore the power of business monitoring, its benefits for the gaming industry, and Anodot's prominent role in this service. Enhancing Savings Identifying problems early is crucial for businesses, especially in the gaming industry. Spotting potential bugs is essential for a great user experience and saves time on resolution. Anodot's Business Monitoring and Anomaly Detection platform offers valuable solutions for identifying and preventing costly abnormalities in gaming operations. Here's how: Early Detection: Anodot helps online, and mobile gaming companies spot and troubleshoot game-specific problems early on. By keeping an eye on real-time metrics and data, it catches any unusual behavior that could affect player experience or revenue. (Check out this story.   Real-time Alerts and Forecasts: Anodot's autonomous monitoring solution provides real-time alerts and forecasts for revenue-critical business incidents. This allows gaming companies to proactively address potential problems before they escalate, enhancing operational efficiency. Cost Anomaly Detection: Anodot's machine learning can also keep an eye out for any unexpected cost changes. This way, gaming companies can better manage their expenses and find ways to save some cash. Enhanced Player Experience: Timely detection of abnormalities can lead to quicker resolution, reduced downtime, and improved customer satisfaction so your players can keep gaming without interruptions! Unexpected things can happen in your gaming operations that are beyond your control. But hey, you can still handle the abnormalities by using ML analytics. Our insights can help you save money to keep making your game the best it can be! Improving User Experience Your users are the key to your gaming success. When they have a blast playing your game, they'll keep returning for more and recommend it to other gamers! Unfortunately, the same thing can happen if your users aren't happy with your game. And when dealing with those new taxes, you'll want to keep your existing players and attract new ones consistently. Here's how Anodot can help: Proactive incident management: By promptly detecting anomalies, Anodot enables gaming companies to address issues that may negatively affect the user experience, minimizing downtime and maximizing player satisfaction. Comprehensive anomaly grouping: Anodot's platform can group anomalies across different silos, allowing businesses to quickly identify and address incidents that impact user experience, ensuring a seamless gaming experience for players. Optimized decision-making: With real-time business monitoring and anomaly detection, gaming companies can make informed decisions to optimize player experience and avoid potential losses. Enhanced user retention and brand reputation: By effectively detecting and addressing anomalies that impact user experience, Anodot's solution helps gaming companies retain players, boost player satisfaction, and maintain a positive brand reputation, contributing to long-term success in the competitive gaming industry. It only takes one bug or glitch to get players turned off from your game. To maintain a healthy user experience, staying alert for possible anomalies is imperative. Of course, getting a partner can help this process stay easy and automated! Optimizing Performance Keeping a close eye on and quickly resolving anomalies is important for top-notch performance in the gaming industry. So, how exactly does anomaly detection contribute to better gaming performance? Immediate Issue Detection: Proactive monitoring detects anomalies that may affect gameplay performance. Real-time tools and analytics help gaming companies identify issues like server latency, network congestion, or hardware failures early on. This allows swift action to address problems promptly. Enhanced Performance Optimization: Identifying anomalies offers valuable insights into game performance metrics. Real-time analysis enables gaming companies to identify bottlenecks, optimize server capacity, fine-tune game mechanics, and improve load balancing. These optimizations lead to smoother gameplay, reduced lag, and improved performance. Competitive Advantage: In the gaming industry, high-performance gameplay sets a company apart. Resolution of anomalies enables gaming companies to deliver superior gameplay experiences. By consistently providing high performance, companies can gain a competitive edge, attracting and retaining more players. Final Thoughts As of 2023, there are 3.220 billion gamers worldwide. The expansive market encompasses various demographics and can be very profitable for gaming companies. However, new industry regulations may emerge, impacting operations and triggering a chain reaction in how issues are addressed and resolved. This is why business monitoring is incredibly powerful. It effortlessly anticipates errors before they escalate into problems, offering remarkable benefits such as cost savings, enhanced user experience, and improved performance in the gaming industry. With Anodot's AI-powered business monitoring and anomaly detection, you can effortlessly tackle errors before they occur. So, no matter what new taxes come your way, rest assured that you're already cutting costs with Anodot. Let's talk! 

Companies that use artificial intelligence and machine learning to independently monitor databases and the data that's being stored are reaping huge wins in saved time and costs. And it's typically the DataOps teams that can take this project on to success.

The total transaction value of digital payments is projected to exceed $1.7 billion by the end of 2022. Each one of these transactions generates masses of data that contains critical insights for merchants, payment service providers, acquirers, fintechs, and other stakeholders in the payments ecosystem. Having real-time access to these insights has the power to drive growth through customer and market understanding. It also has the power to protect against tremendous revenue loss by mitigating the risk of payment issues and fraud. The payments data mandate   Real-time payment monitoring and detection of transaction incidents is one of payment data’s most important mandates. Whether there’s an increase in payment failures, a drop in approval rates, or other issues — operations, payments, and risk managers must be able to see what went wrong, where, and why.  This is the only way they can accelerate root cause analysis and quickly triage and resolve payment incidents.  But gaining complete visibility into the payments ecosystem in real time in order to detect anomalies immediately is a great challenge, though one that no organization can afford to ignore.  Time could not be more of the essence. Consider what happens if there is a glitch in an API to a backend payment system that is crucial for approvals. If transactions can’t access the relevant API, the payment acceptance rate will plummet and revenue will be lost during the unexpected downtime.  So, while organizations are collecting large volumes of data every day, if the data can’t be used to protect the organization against payment incidents and potential loss, the value of the data will never be realized and losses will continue to impact business health.  Challenges of optimizing payments data   The bridge between collecting payment data and using it effectively is full of obstacles, which primarily fall into three categories – access, process and infrastructure, and complexity. Access to user data User onboarding: Aggregating user data is complicated by the fact that assuring a good user onboarding and registration experience typically requires asking as few questions as possible (to avoid abandonment due to complicated and time-consuming processes). Owning the relationship: Most payments stakeholders don't necessarily own the end user relationship. This means they don’t have access to the relevant user data, making it all the more difficult to detect which user activities are anomalous. Tokenization: Access to user data is also hindered when using external tokenization, which keeps most of the user and card information with the tokenizer rather than with the merchant or payments service provider. Data privacy: Detecting anomalous behaviors requires aggregating data about user behavior. However, data privacy regulations and regulators limit the usage of personal user information. Equal access: Even when the right user data is being collected by the organization, not all departments have equal access to it, nor is it shared sufficiently and frequently enough by those who do have access.    Process & infrastructure related Processes are manual resulting in monitoring and detection that are slow and error-prone with real-time outcomes being impossible to achieve. Real-time collection and analysis for timely decision making is impractical due to the complexity involved with the implementation and application of the numerous APIs required for collection. Intelligent insights provided in real time are typically out of reach since no one-size-fits-all solution can address the variety of incidents that occur during the specific recovery and handling processes of each organization.   Complexity The payment ecosystem is continually growing with more systems and data sources than ever, making it very difficult to collect and connect relevant payments data. Sources and data formats are fragmented, also making the task of aggregating data into one coherent source of truth a difficult task. Different payment methods and flows carry different data sets impacting the ability to unify operational data. Not all data is being collected via APIs leaving a lot of gaps since not all the data can be gathered.   Getting the most out of payments data   The goal of overcoming these challenges is to be able to get the most out of payments data. In order to optimize payment operations, teams should be able to:   Leverage data for actionable insights specifically into user activity in order to detect anomalous behaviors. Access all relevant user data, which is enabled by integrations that entail implementing every relevant API, not only those which are related to payments instructions. Gain a fuller picture of user behaviors for better understanding what is anomalous, which is enabled by embedding external data sources into the existing data management environment. Analyze data to build forecasts regarding activity, money flow, user behaviors, seasonality, and more, and not only for understanding what has happened, which drives a better understanding of potential risk. Make intelligence-driven decisions and remove the burden of manual work from payments personnel, which is enabled by AI and machine learning. Better understand the scope and patterns of user behaviors and payments trends, which is enabled by analyzing data across multiple time periods and granularities. Anodot for payment intelligence    Anodot for payment monitoring and real-time incident detection overcomes the challenges to payment operations, incident detection and remediation. Anodot’s AI-powered solution autonomously monitors the volume and value of payment data, including transaction counts, payment amounts, fees, and much more.  The solution delivers immediate alerts when there are payment approval failures, transaction incidents and merchant issues. Our patented correlation technology helps to identify the root cause of issues for accelerating time to remediation.  Anodot automates payment operations, seamlessly integrating notifications into your organization’s workflow. And by filtering through alert noise and false positives to surface the most important issues, it minimizes the impact on revenue and merchants. Turnkey integrations aggregate data sources into one centralized analytics platform. With impactful payment metrics and dimensions pre-configured into the solution, anyone in the organization can leverage data for insights and actions. 

Smooth payment operations are critical for every merchant’s success. At its most basic level, a seamless and reliable payment process is the key to assuring transaction completion, which is at the very core of a merchant's financial strength.  However, when payment data systems fail to deliver insights about issues regarding approvals, checkouts, fees or fraud, the result is revenue loss and sometimes customer churn. While there are technology solutions that can process millions of transactions daily, there are many challenges to effective payment monitoring, leaving timely identification and speedy resolutions too often out of reach. Payment monitoring challenges There are many challenges to accurate and timely payment monitoring.  Among the most formidable are the increasing complexity of the payment ecosystem, the unreliability of static thresholds, the growing success rates of fraud attempts, and manual analysis processes that are too slow for assuring timely resolutions. Let’s take a closer look. The increasingly complex payments ecosystem  Today’s payments landscape is comprised of many different systems, technologies, methods, and players. There are credit and debit cards, prepaid cards, digital wallets, virtual accounts, mobile wallets and mobile banking, and more. To complicate matters, many organizations that process payments rely on multiple third-party payment providers, who are sometimes their direct competitors. There is an additional challenge for companies offering a localized experience to customers. Using local payment systems sometimes means relying on unstable payment networks and represents a measurable risk to the integrity of payment processes. This broad and ever-growing ecosystem can be confusing and difficult for merchants and payment services providers to orchestrate, especially when it comes to determining the optimal path for monitoring, detecting, and remediating issues with the payment process. Static thresholds  Merchants today typically either monitor transactions manually or receive alerts on payment issues based on static thresholds whose definitions are driven by historical data. But user behavior patterns are dynamic, which means that static, historically driven settings and definitions are not reliable for detecting (and handling) issues in real time. This frequently results in missing incidents or discovering incidents too late after the damage has been done.  The increasing success rates of payments fraud attempts The global ecommerce industry is poised to grow into a $5.4 trillion market by 2026 and with it – online and digital payment fraud is also growing exponentially.  Fraudster techniques have become increasingly more sophisticated and their success rates are higher than ever. Last year, $155 billion in online sales were lost to fraud, and this number is expected to continue to grow.  And according to the recent AFP Payments Fraud and Control Report, 75% of large companies with annual revenue at over $1 billion were hit by payment fraud in the past year, and 66% of mid-size companies with annual revenues at under $1 billion. Manual analysis Even when a payment incident is detected, understanding the root cause for accelerating remediation can still be very challenging. Whether at merchants or financial services organizations, those who are charged with understanding the root cause of payment issues and remediating them are typically faced with having to manually scour through multiple dashboards. This approach which is very time intensive is no longer viable. Decisions need to be made in real time and actions must be taken immediately. A delay in mitigation is not something any organization can afford, as it drives revenue loss. Rules based routing Many merchants and payment services companies route payments as driven by simple rules engines. However, this approach  is not designed to address today’s needs for fast, efficient, and smart routing. To overcome all of these challenges, what these organizations and every merchant needs is a way to detect payment issues faster and get alerts in real time when there are revenue-critical incidents in their payment operations.  This is where AI-powered payment monitoring comes in. AI takes payment monitoring to a whole new level When we introduce AI-powered analytics and real-time monitoring to the task, merchants are finally empowered to overcome the above-mentioned challenges and prevent revenue loss. They can monitor all of their payment data and capture continuous insights into their payment lifecycles. They can know instantly upon the appearance of a suspicious trend or when payments fail and receive real-time alerts that provide the full context of what is happening, including incident impact, timeline, and correlations. Additional benefits include: Faster root cause detection AI-driven payment monitoring learns the normal behavior of all business metrics, constantly monitoring every step in the payment lifecycle, and providing crucial workflow insights.  This happens automatically, where merchants and payment service providers get much-needed visibility into what happened, where it happened, why it happened, and what they should do next, for faster than ever time root cause detection. Real-time actions  When payment monitoring is driven by AI, monitoring and alerting is real-time, empowering organizations to detect and act upon any deviation from normal transaction behavior.  This way they can capture incidents before they impact the customer experience.  Noise alert reduction By learning what impacts customers and the business and what doesn’t, billions of data events can be distilled into a single, scored, highly accurate alert. This makes storms, false positives, and false negatives a thing of the past, and enables teams to focus on the incidents that bring a measurable impact to revenues and the customer experience. Moreover, users will no longer need to subscribe to alerts, which often wind up in the ‘graveyard of alerts’ folder, with no measurable value for the payments operation. Accelerated time to remediation When needed insights can be gathered automatically at the right time, there is no need to sift through endless graphs on multiple dashboards.  AI enables the correlation of anomalies for immediately identifying the contributing factors to incidents, and receiving the full context required for expediting the right remediation actions. How Anodot can help Anodot brings an AI-powered solution for autonomously monitoring the volume and value of a merchant’s payment data, including transaction counts, payment amounts, fees, and more. The solution detects payment incidents 80% faster and profoundly accelerates resolutions, sending immediate alerts when there are transaction or merchant issues, and payment or approval failures. Notifications are seamlessly integrated into existing workflows, with only the most important issues being surfaced to prevent time being wasted on false positives, and profoundly reducing alert noise by 90%. Anodot's patented correlation technology helps to identify the root cause of issues with 50% faster root cause analysis. The out-of-the-box solution comes with turnkey integrations, and is pre-configured with impactful payment metrics and dimensions. This way, organizations can accelerate ROI and time to value.  

In today’s payments ecosystem, the ability to monitor and use payment data effectively represents a real and essential competitive advantage. Intelligent operations should be a strategic goal for the entire company, and when executed properly, will enable you to build a future-proof payment operations infrastructure.  With the increased proliferation of AI technologies, the payment operations space has been fundamentally changed, and the traditional legacy BI approach relying on dashboards and static thresholds is no longer adequate. AI and ML are critical to accelerating revenue, improving operational efficiency, and providing better customer experiences. Global payments leaders are increasingly relying on AI to monitor and optimize their payment operations resulting in lower costs, higher approval rates, and fewer declined transactions.  In this White Paper: The Journey to Intelligent Payment Operations we define five levels of payment operations maturity, with Level 1 being the least mature and Level 5 employing the most advanced practices: As we advance through levels, we consider 3 key characteristics: monitoring, operations and data; define the key metrics and dimensions we measure; and offer suggestions for how to level up. Additionally, we asked our Chief Data Officer, Dr. Ira Cohen, to share his insights on improving monitoring effectiveness no matter your maturity level or platform. Level 1 : Responders — Analytics and BI At this level, organizations are highly reactive. They operate based on dashboards and static alerts, relying on non-standardized and fragmented manual processes using email and spreadsheets — neglecting advanced tools and leading practices that drive operational performance and resilience.  Level 1 companies are focused only on the most basic aspects of monitoring — using dashboards that are monitored visually and alerts based on manual static thresholds that result in alert noise, false positives and false negatives.  Tips from our CDO When integrating to a vendor - Implement all APIs provided by them — including those not relevant to initiating transactions. Establish your KPIs in simple bullet points (e.g., increase payment acceptance) and understand your most influential dimensions. Eliminate noise by grouping values of dimensions that may affect the results but do not affect the business Measure the impact! Make sure you're monitoring business vs. technical stuff — you might see a glitch, but the impact is minimal. Understand your seasonality and incorporate special events into your monitoring systems Level 2 : Guardians — Automated Anomaly Detection Level 2 organizations have developed more proactive and collaborative processes, incorporating AI- and especially Machine Learning-based technologies to drive operational speed and agility. They implement a robust anomaly detection system which serves as the gatekeeper of their business and the frontline protector of their customers and revenue.  AI-based anomaly detection provides financial institutions with the capabilities needed to detect issues early and take preemptive actions before they turn into crises. They do so by automatically learning the data’s normal behavior, including seasonal and other complex patterns, to identify and alert stakeholders on any combination of metrics that behave abnormally.  Operations teams in Level 2 companies can send any data stream for monitoring, which will be continually analyzed for anomalies. Instead of looking at dashboards, they monitor for anomalies and use AI-based scoring to triage and resolve issues according to their severity and impact. Tip from our CDO Avoid false positives in KPIs that are ratios (e.g., payment success rate) by augmenting with logic that also looks at the volume behind the ratio (e.g., # of payment attempts). Not doing so will alert the team frequently at night, when payment volumes are low and the ratio fluctuates. Level 3 : Analysts — Automated Correlation  Correlation is a marquee trait for Level 3 organizations. They have moved beyond basic single metric/stream anomaly detection and have a comprehensive view of their business and incident impact with event and anomaly correlations.  Applying anomaly detection on all metrics and surfacing correlated anomalous metrics helps draw relationships that not only reduce time to detection (TTD) but also support shortened time to remediation (TTR). This frees up both data professionals and their operations counterparts to collaborate on automation initiatives, simplify complex internal operating structures, and enable support of more complex payment ecosystems. Tips from our CDO Slow drifts are typically detectable only at higher time scales (e.g., daily/weekly). If the payment success rate slowly declines because of a small glitch for a few transactions, it would be hard to notice at lower time scales (e.g., minutes/hours). Monitor high level KPIs (e.g., payments) at lower time scales and more granular breakdowns of the KPI (e.g., payments by country, gateway, provider, merchant, etc.) at a higher timescale. More granularity results in very low volumes of payments for some combinations — making it nearly impossible to detect issues. For example, if the number of payments for a merchant in a certain country using a specific gateway is very low for a time scale (e.g., 1 payment per hour on average), detecting drops in payments and success rates at minute and even hourly time scales will be very hard. But at a daily time scale, the volume will be high enough to detect meaningful drops. Level 4 : Masters — Augmented Root Cause Analysis  Organizations at this level are masters of payment operations. Payment operations are automated across the board using AI. They have sophisticated practices and tools in place for detection, correlation, incident triage, and remediation. In these companies, data professionals and operations are focusing on judgment-based work and are empowered to deal with high value activities such as business strategy, product development, and advanced analytics. For Level 4 companies to reach the upper echelon of Level 5, they have to make remediation core to their payment operations strategy. The good news is they have all the pieces in place: a centralized monitoring platform; autonomous learning of data at scale; accurate learning models; event and anomaly correlation; noise reduction mechanisms; and support for quick root cause analysis.  Tip from our CDO For KPIs with low volumes, monitor the time between transactions and detect when it becomes too long compared to normal. This provides the ability to detect failures in very low volume KPIs. Level 5 : Visionaries — Automated Remediation  Level 5 companies are robust in all dimensions of payment operations maturity, but what really separates them from others is their journey to automated remediation. They codified their remediation strategies using webhooks and triage workflows to enable automated remediation. As a result, teams at Level 5 companies can identify and fix challenges more efficiently, resulting in improved customer experience and much lower incident costs.  With the competitive edge and agility offered by their intelligent operations, Level 5 companies outperform and outpace their less mature counterparts. ​​The journey to level 5 isn’t straightforward, nor is it the same for everyone. So regardless of your current level, you need to pay attention to the Level 5s to understand the challenges coming your way. Tip from our CDO ML-based monitoring is paving the way for autonomous remediation. Soon, ML-based systems will recommend a remediation action based on previous incidents; execute the action through the remediation engine, and fine tune its operations through a closed feedback loop, increasingly improving its reactions. The promise is exciting, but the reality is complicated. Learn more about the route to automated remediation and the eight essential components that automated remediation will depend on to (hopefully) operate successfully. Leapfrog maturity levels with Anodot Anodot's autonomous business monitoring platform helps merchants and payment providers to identify and resolve payment issues faster, route payments intelligently, and optimize approval rates. Anodot customers report 80% faster detection times, 90% drop in alert noise, and 30% reduction in incident costs. By optimizing the payment transaction process, their operations teams can focus their efforts on digital transformation and value-added initiatives.  Payments Operations Business Package Anodot, working with major fintech clients, has developed a turn-key monitoring solution for payment operations teams, utilizing industry best practices and ML domain expertise. Anodot’s payment monitoring business package is built to deliver value fast: Easy integration to any payment data source.  Out-of-the-box alerts and dashboards. Completely autonomous learning, monitoring, and correlations.  Slick and simple UI makes root cause investigation a breeze.  Triage workflows and automated actions. Payment companies and financial institutions can quickly expand their monitoring coverage with additional business packages offered by Anodot: Customer Experience & Support — Monitoring users impressions, funnel, Customer support events, calls, chats and emails.  Treasury & FX — Monitoring deposits, accounts balances, fluctuations,  Fraud — Monitoring trends, anomalies and suspicious behavior

The core value Anodot delivers to customers is AI-powered, autonomous monitoring of critical business KPIs in order to protect revenue and manage costs. But Anodot's value extends beyond our product — to our people. Each Anodot customer has a dedicated Customer Success Manager (CSM) to ensure they are getting maximum value and ROI from Anodot's platform. We'd like to highlight one of our Customer Success Managers, Uriah Mitz, who is working with global eCommerce giant, PUMA. Uriah has more than 6 years of experience implementing AI and ML products. He tells us in his own words about his experience working with PUMA and helping them achieve their business goals. Customer Success My role as a CSM involves a deep understanding of the customer’s vertical, the customer’s environment and the customer’s needs in order to provide the best solution and get the most value from Anodot. A good CSM has to be customer-oriented and have a strong sense for people and business. At Anodot, our goal as CSMs is not trying to sell the customer additional products. Rather, we focus on leading and supporting the customer since from the kick off meeting until the customer is fully on-boarded and independent. https://youtu.be/f6UYebNtjos PUMA's pain points PUMA's Senior DevOps Manager, Michael Gaskin, was interested in Anodot based on the experience he had with another Anodot customer. Michael understood the difficulties he was facing and wanted to monitor all revenue aspects of Puma’s websites which were not clear enough. Before Anodot, Puma did not have a tool to distinguish what was normal, or abnormal, across their 45 eCommerce websites.  For example, one of the revenue incidents caught by Anodot was gift card purchases in Switzerland that were not working. In general, for a website that spans many countries, gift card purchases appeared to be working well, but shortly after we implemented payment types into Anodot we discovered the problem in Switzerland which could have cost Puma a lot if it was discovered later.  Onboarding with Anodot At Anodot, we first try to understand the primary pain points of the customer. When we fully understand the challenges, we discover with the customer the needed dimensions we want to measure. We build a diagram of the pain point, how we are going to tackle it based on the available data, where the data will be fetched and the time resolution we want to monitor. Integration with Anodot is very simple. We have plenty of data sources under our Business Collectors umbrella and we can connect to any data source in 3-4 minutes. After integrating the data we want to monitor. Our AI-powered system automatically starts to analyze business data, finding seasonality behaviors and detecting anomalies. At this point, the customer gets full training of the system, including how Anodot works, how to see the data, how to find the relevant anomalies, how to create new alerts, how to tackle complexed issues with influencing metrics, injecting events in timeline, etc. The average onboarding process usually takes up to 6 weeks. PUMA Use Cases 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. We looked at the data and went backwards: How many transactions are made every minute? How many items in average for each transaction? What is the conversion rate? How many items added to the cart? What is the % of add to cart and items per transaction? What is the # of returning customers? We also added dimensions to all of those measurements (KPIs) such as payment method, currency, language, country, etc. All of these dimensions help Puma find the root cause of the problem related to the buying funnel (TTD) faster and to fix it much earlier than if they didn't have Anodot. (TTR) Future Focus - Future Verticals In addition to all of the above, we are currently working on adding another measure - the amount of website failures to measure the user experience in order to fix issues faster (improvement of TTD and TTR). In the near future we will add more use cases, such as customer experience, by measuring the processing time of the website. We will work on ads effectiveness by measuring the logins from ads worldwide and measuring the success rate of campaigns by adding events to Puma’s timeline to better understand sales behavior and much more. The Power of Anodot Anodot's AI-powered business monitoring solution opens a window to insight that no one has ever seen before in the business. By dicing data into multiple dimensions, problems that aren’t known and trends that no one has ever seen become crystal clear. No more wasting time attempting to understand the root cause of a drop in a static dashboard, no more time waste on false positives, or guessing invisible trends trying to be compared wrongly. Metric correlations in Anodot is a powerful tool which can help companies in any vertical understand business in a perspective never seen before. From a point of view of a CSM, it’s an exciting journey every time.