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Dun & Bradstreet

D&B revolutionizes its Payment Fraud Detection System with Knoldus

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Headquarters

Jacksonville, FL

Industry

Finance

Technology Used

Scala, Akka, Esper, GridGain

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

Challenges

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.

Solutions

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.

Results

Reduced the response time to 30 mins from days or weeks to detect fraud and report to analysis.

Challenges

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:

  • 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 took as long as 24 hours. Since timing was a critical factor, this was an unaffordable drawback.
  • 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.
  • The addition of new functionalities to the platform was a challenge.

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 by receiving notifications & alerts whenever a business begins to pose a threat in the future.

And for this, the system needs to be efficient and meet the SLAs; alerts & notifications have to be processed fast so that the partner organizations using the platform can modify their business strategies right on time so that they don’t suffer any losses. However, there were significant roadblocks to 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.

Solution

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 choice Scala 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

Results

  • 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.

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