Smarter Digital Payment Monitoring to Protect Business Operations

You set your mug down on your desk and turn on your computer. You skim over various dashboards on one screen and sift through your email alerts on the other before pulling the regular reports, as you do every morning.

However, this morning proves to be unlike any other. It's about to take a nasty turn that will prevent you from finishing your morning coffee.

As you scan the online payment gateway dashboard, you notice a significant drop in transaction approvals overnight. You take a deep breath, rub your eyes, and secretly hope it's a mistake. What on earth has happened? It takes a moment to gather your thoughts. Where is the flaw? How much money is squandered? Which retailers are affected? What will account executives say? A scenario like this is unthinkable in any company that relies on dashboards to monitor payments and transactions.

Payments Monitoring Dashboards are Drowning in Data

Dashboards assist FinOps, BI, and commercial teams in visualizing business activities in order to ensure that the payment ecosystem runs smoothly. However, as the amount of data to collect and monitor grows, the effectiveness of traditional dashboards for monitoring digital payments is called into question.

Why is payment transaction monitoring using a dashboard insufficient?

1. Siloed monitoring and disconnection of data sources and teams

To deal with the abundance of data, business units monitor their data separately, gathering it from various sources and using various tools. This stifles collaboration and impedes efforts to conduct comprehensive analytics.

In our little story, you are completely unaware of the incident's causes or consequences. To get to the bottom of the problem, you need to start involving programmers, IT, finance, and product.

2. Lack of granularity and context

A dashboard can monitor specific metrics separately, but it cannot tell you whether or not specific data behavior is related. It's possible that you're dealing with a software failure, a configuration error, or malicious fraud. It is up to you to connect the dots and gather information. However, time is of the essence. Every minute that passes, the company loses money, both directly from uncompleted transactions and indirectly from the time analysts and developers spend on the investigation.

3. Alert storms and false positives

In the monitoring of payment gateway solutions, alerts take a respectable second place to dashboards. However, as more data is collected and the system becomes more agile, the likelihood of false negatives and false positives increases. Storm warnings are becoming more common. It's easy to see how a failure like the one in our example vanishes among the flood of alerts.

4. Retroactive monitoring and static thresholds

A dashboard keeps track of historical data. Past data behavior defines thresholds and alerts. As a result, whenever market or user behavior patterns change, the settings and definitions become obsolete.

Furthermore, each metric has regular data fluctuations that are ignored by static thresholds. Consider the same scenario, but with the drop in payment approvals remaining within the normal range because transaction numbers are usually at their peak at the time in question. You wouldn't even get an alert, and it would be a long time before you realized something was wrong. It may even take a customer complaint to uncover the flaw.

5. Extensive manual work

Thresholds must be manually adjusted. Furthermore, each business unit focuses on its own set of operational needs and objectives. Extensive manual work is required to obtain the complete picture and understand the relationships in data behavior. On excel files, teams end up filtering, organizing, prioritizing, and comparing data.

In our example, the coffee has long since gone cold, and you are still busy downloading the relevant reports that will help you determine who needs to be informed about what and which steps to take.

6. Human errors

So much manual labor not only wastes time but also leaves room for human error.

How to Save Time and Cost with Smarter Digital Payment Monitoring

1. Gain control over data volume and complexity

The volume of data to monitor is difficult for dashboards, but not for AI-powered platforms. Machine learning capabilities also add a level of autonomy to data processing by identifying patterns, similarities, and connections in data from a variety of sources and formats.

The insights provide answers to specific questions raised during the initial examination. It eliminates a lot of guesswork and directs you in the right direction. You'd be surprised at how much time you save and how much stress you avoid.

2. Connecting and correlating data for higher resolution

A tool that monitors all of your business data and isn't limited to the preferences of a single business unit can bridge the gap between teams and data silos, as long as it correlates all data points to identify patterns in data behavior.

Assume the system detects a simultaneous drop in server activity and synchronizes the drop in payment approvals with data from other teams' sources. Do you see where this is leading you? Instead of pressing the emergency button and rallying the entire company to solve the crisis so that you don't lose any more money, you'd instruct IT to redirect traffic to the affected server. Transactions would resume within minutes, and the relevant team would be able to investigate and resolve issues without the added stress of financial loss and customer distrust.

3. Accurate alerts – no more thresholds

Static thresholds are not the best way to detect anomalies in data. As long as metrics remain within the defined normal range, your payment monitoring dashboard tool is content. However, in order to get a precise picture of what normal data behavior looks like; you must take into account time-specific and seasonal fluctuations.

The machine learns what is normal for which metric at which time by recognizing regular patterns in each metric. Real-time data monitoring becomes much more precise and eliminates unnecessary alert noise.

4. Real-time monitoring

Many factors contribute to frequent shifts in transaction and consumer behavior: your company grows, your competitors change strategies, and new trends emerge. A monitoring tool that adapts to the new normal without the need for manual intervention can reduce uncertainty, time, and effort. With an AI-powered tool, you can stop pivoting endless excel files and instead let the tool learn and make the necessary adjustments.

When you combine the above, you can see how much manual labor becomes obsolete when you start using an AI-driven tool. At the same time, the possibility of human error is reduced.