Implementing Data Analytics to Curb Impersonation and Fraud payment

We resolved card and payment related issues and prevented their further occurrence.

About the Industry

The advancement of smart technologies in the banking industry has brought many positive results. Frequently changing consumer behavior with multiple regulatory frameworks and norms has brought about a paradigm shift in the industry.

The incorporation of Artificial Intelligence and Machine Learning has revolutionized the digital journey of this industry. Similarly, the rising use of data analytics is bringing a positive shift in banking practices. Enhanced governance with advanced financial reforms is targeting not only services but also safety measures for the customers. Though the rising compliance cost is posing an alarming threat to the industry, measures to mitigate such issues are underway.

About the Client

Our client is a US-based management consulting firm that is active across multiple areas, including Energy & Utilities, Healthcare, Life sciences, and Manufacturing. The organization holds specialized expertise in providing financial, banking, and insurance-related services.

Currently, the company is serving in thirty nations and employs more than three thousand employees. Along with core management consulting, our client also offers specialty services. To successfully transform the business landscape, the company is fully automating its business processes.

The Business Challenges

We analyzed this client’s situation and identified some of the pain areas. The organizational challenges were as follows: –

  • Card cloning was the first and foremost challenge the organization was facing.
  • Money withdrawal from another continent without the account holder’s knowledge of such incidents was also rising.
  • The issue of impersonation was taking a toll. False and unanimous generation, cloning & copying of credit cards was a major concern.
  • Card double-swiping was a weakness at the organizational level.

The Business Solution

From our understanding of their business process, we were able to identify the gaps that were generating issues. We implemented the following solutions to fix the flaws: –

  • Our expert data analytics team prescribed the implementation of a spark streaming technique.
  • This technique provided near-real-time predictions of potentially fraudulent activities that might occur in the future.
  • Our machine learning approach trained the collected historical data and categorized it as per type of issue. This classification further simplified the process of analysis and helped mitigate the organizational pain areas.

Key Results

  • The issue of card cloning got significantly reduced.
  • We successfully curved the number of cases of faulty money withdrawal.
  • The cases of double card swiping were reduced to a great extent.

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