How Ericsson Uses QPR ProcessAnalyzer to Monitor 10–20 Million Trade Compliance Transactions Every Month

The Results: From Manual Reviews to Continuous, Fact-Based Oversight

The ability to analyze complete business flows allows Ericsson to investigate individual cases while still understanding the wider operational context around them.

When the analytics environment identifies an anomaly, the team can see where it occurred in the process, how frequently similar behavior appears and, when necessary, drill all the way down to the details of an individual transaction.

When Millions of Transactions Meet Complex Global Compliance Requirements

Trade compliance is not a simple linear process

One of Ericsson’s early realizations was that trade compliance could not be managed as a sequential series of activities.

Instead, compliance consists of numerous individual controls that must happen at the right points within the wider business flow.

That made understanding the complete operational flow — including where data originates, where it moves and how different systems interact — essential.

Manual monitoring could no longer keep pace

Ericsson’s trade compliance operations had historically included many manual, sequential and paper-based activities.

As transaction volumes increased, this model became increasingly difficult to scale.

The challenge was therefore not simply to analyze processes after something happened. Ericsson needed a way to continuously monitor what was happening across its business flows and identify situations requiring attention

In trade compliance, the important cases are often the exceptions

Traditional process analysis frequently focuses on the majority of transactions: bottlenecks, process performance, SLAs and average turnaround times.

For trade compliance, Ericsson often needs to do the opposite.

The critical question may be:

What is happening in the small number of cases that look different from everything else?

A change to transaction metadata, an unusual sequence of activities, a combination of product, country and recipient, or a system configuration issue can all be signals worth investigating.

Using QPR ProcessAnalyzer to Find the Cases That Matter

From snapshots to continuous trade compliance monitoring

By mapping Ericsson’s business flows and the underlying data, the trade compliance team created an analytics capability that continuously monitors transactions rather than relying solely on isolated reviews.

Machines can now perform monitoring at a scale that would require an impractical number of people to achieve manually.

QPR ProcessAnalyzer applies predefined criteria to automatically flag potential issues and generate a direct link into the relevant process view, so Ericsson can notify the right team and investigate what actually happened without searching through the data manually.

From an anomaly to the individual transaction

When predefined criteria identify unusual behavior, Ericsson can immediately investigate further.

The team can first see that something has changed, then examine where the change occurred within the operational flow and how frequently the same pattern appears.

If needed, analysts can drill all the way down to the individual case and inspect the transaction details recorded across Ericsson’s systems.
This combination of enterprise-scale process monitoring and case-level investigation enables the team to move efficiently from signal to deeper analysis.

Monitoring processes — and the systems behind them

QPR ProcessAnalyzer has also given Ericsson another valuable perspective: visibility into system performance.

In a highly integrated enterprise architecture, systems depend on information propagating correctly and on time.

Ericsson can use its analytics capability to identify when systems fall out of sync or when data does not move through the landscape as expected — helping distinguish operational behavior from underlying system issues.

A connected analytics environment built around shared data

Ericsson’s trade compliance analytics service combines multiple capabilities in one governed enterprise environment.

Its architecture includes Snowflake as the data platform, case management for handling identified anomalies, BI tools for reporting, AI and machine learning capabilities, and QPR ProcessAnalyzer as the main environment for advanced process analytics.

The objective is a connected analytics environment where users can work with the same underlying data regardless of which analytics tool they use.

This common data foundation also creates important opportunities for combining process mining, AI and machine learning in increasingly advanced trade compliance use cases.

From Reactive Investigation Toward Predictive and Preventive Trade Compliance

Massive transaction volumes become continuously analyzable

Ericsson’s analytics environment already contains more than 1.2 terabytes of data, with another 10–20 million transactions added each month.

QPR ProcessAnalyzer gives the trade compliance organization the ability to work with this scale without attempting to monitor every transaction manually.

Potential compliance risks can be isolated automatically

Instead of searching through millions of ordinary transactions, Ericsson can define criteria that identify unusual behavior and bring relevant cases to the team’s attention.

Analysts can then concentrate their expertise where it matters most.

Facts support investigation and mitigation

Once a potential issue has been identified, Ericsson can investigate what happened, where it happened, how frequently it occurs and what behavior or system condition contributed to it.

This enables mitigation actions to be based on observable facts rather than assumptions.

QPR ProcessAnalyzer provides operational context

Individual data points rarely tell the whole story. By placing an event within the wider business flow, QPR ProcessAnalyzer helps Ericsson understand what happened before and after it, where it occurred and how people, systems and transactions interacted around it.

This operational context becomes increasingly important as Ericsson expands its use of machine learning and large language models, because advanced analytics can evaluate individual signals in relation to the process in which they occurred.