D&B revolutionizes its Payment Fraud Detection System with Knoldus

  • project-icon

    Partner: Dun & Bradstreet

  • tech-icon

    Technologies Used: Scala, Akka, Esper, GridGain

  • domain

    Domain: Finance

About Dun & Bradstreet

Dun and Bradstreet is a global corporation that provides its partner organizations with information on commercial credit, data & analytics reports on businesses. D&B is globally recognized for generating an organization’s business credit reports that help third party stakeholders to analyze the financial performance and health of the organization

Currently, Dun & Bradstreet generates analytics for about 100 million companies globally. Its proprietary suite of reports is helping organizations to leverage analytics to decide whether to allocate business credit to a customer, continue or initiate business with others or to establish their credibility & reputation in the industry.

The Risk Management platform needed an addendum for an efficient payment fraud detection mechanism

Dun & Bradstreet’s risk management platform allowed partner organizations to access deeper intelligence to get a clear picture of the credit history, payment defaults or potentially fraudulent activities of the organizations they were doing business with. This helped them to mitigate risks through receiving notifications & alerts whenever a business begins to pose a threat in the future.

And for this; system needs to be efficient and meet the SLAs, alerts & notifications have to be processed fast so that the partner organizations using the platform are able to modify their business strategies right on time so that they don’t suffer any losses. However, there were significant roadblocks in achieving D&B’s goals because of the challenges that the Risk Management platform was bringing out.

Let’s understand these challenges at a deeper level.


For partner organizations relying on the Dun & Bradstreet platform, timing is the most critical factor. If there is a bankruptcy report or a default payment in the credit history of one of the companies that a partner organization is associated with, then they would need to get alerts on that immediately. But what made this difficult was the fact that there were millions of customers which gave rise to huge volumes of alerts on an hourly basis.

Major challenges that D&B was facing:

  1. The system’s performance was not up to the desired standard. The platform was slow and sluggish. For instance, if an organization had signed up for a package that allowed them to receive alerts of any payment frauds or defaults within 4 hours, it was taking as long as 24 hours. Since timing was a critical factor, this was an unaffordable drawback.

  2. The system was unable to scale and work seamlessly in high-load scenarios as it was handling millions of customers and huge volumes of alerts

  3. The addition of new functionalities to the platform was a challenge.

Solutions: Succeeding in fraud analytics

Knoldus collaborated with the Dun & Bradstreet engineering team to identify the business needs and document critical non-functional requirements. D&B wanted to make sure that it meets all the requirements of its service-level agreement (SLAs) The solution was laid down with:

  • Complex event processing Conventional data management architectures could not suffice as there’s a lot of uncertainty involved when it comes to fraud detection of any kind. Therefore, many real-time event-driven applications like a fraud prevention system rely on Complex event processing.

  • A complex event processing (CEP) approach enabled D&B’s system to detect fraudulent patterns in a company’s credit history with immediate effect and initiate communication with the concerned party. CEP enables matching of incoming events in near real-time to a pattern so that businesses can receive the latest insights and higher management can act upon it in the real-time. For instance, if a credit card customer swipes his card in California, it is not likely that he/she will swipe the same card in the UK. Esper was used for complex event processing along with GridGain on which all the data crunching was done for timely alerts.

  • Duplication of alerts was removed The alerts that were applicable to multiple clients were not regenerated, rather they were re-circulated so as to remove the duplicate instances and thus drastically improve the efficiency of the system.

  • Test Harness with a CI/CD pipeline As the system was lacking a lot of test scenarios, it presented challenges while adding new functionalities to the platform and launch new features on time. To solve this problem, we introduced a test harness for the system along with CI/CD pipelines which means we covered the entire system with an end-to-end test suite. A test harness introduces automated test frameworks that allow the system to be tested under varying load conditions.

  • Scala was the language of choiceScala Programming Language was used to writing concise and maintainable code which was further extended to Akka.

A glimpse of D&B’s Risk Management Platform

Knoldus Dashboard

Technical Architecture

Knoldus Dashboard


  • Faster Alerts & Business Insights: Reduced the response time to 30 mins from days or weeks to detect fraud and report to analysis.

  • Handle 8X Alerts: The system handles 8x the volume of alerts now that it was earlier able to and is scaling horizontally on GridGain.

  • Onboard 3X Clients: Dun & Bradstreet was able to onboard many more clients as the system could now function under varying load conditions.

Download the detailed version of the case study:

Building a real-time payment fraud detection system? Knoldus can help.

Without digital transformation today, there can be no innovation. And Digital transformation means robust software applications that not only meet but also exceed customer expectations.

In the world of finance where speed is a key factor and uncertainty is the norm, meeting Application SLAs is more important than ever. The applications associated with the industry need to be responsive and provide a high-quality service while integrating efficient risk management. Knoldus’ Payments Risk Management solution offers real-time fraud detection and prevention using machine learning and advanced analytics designed to:

  • Maximize revenue and growth

  • Target and manage fraud loss

  • Address compliance requirements

  • Improve operational efficiencies

  • Elevate customer experience and ensure trust

If you’re building a robust risk-management application, Knoldus experts can help. Our engineers have deep experience in all aspects of reactive and distributed computing, big data, machine learning, and data sciences, and are able to provide your team with critical advisory-level services at key points in your journey. Knoldus solution enables to you deliver the flexible, reliable, scalable and performance demanded in today’s challenging, dynamic environments.

Drop us a line at or contact us here and let’s discuss how Knoldus can help as your trusted IT partner.

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