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Pagos

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"Streamlines payment integration for digital businesses."

Case Study
Frequently Asked Questions
This company offers tools that simplify the integration and management of payment systems for digital businesses. Their solutions enable businesses to seamlessly incorporate diverse payment methods into their platforms, optimizing transactions and improving user experiences. This empowers companies to focus more on growth and strategy rather than dealing with the complexities of payment processes.
PAYMENTS DATA PLATFORM
TRANSACTION ANALYTICS
PAYMENT OPTIMIZATION TOOLS
REVENUE INTELLIGENCE
PAYMENT PROCESSING INSIGHTS
ETAIL WEST 2025
BlurbSTAR Case Study
Pagos.ai & Unnamed Financial Institution
Pagos.ai using BIN data to uncover issuer-level fraud.
30%
Increase in customer retention rates
45%
Reduction in fraudulent transactions
This profile remains unclaimed. Blurbs can only offer a partial, unverified case study.
1.
Situation
Issuer-level fraud with BIN data
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Financial institution faced issuer-level fraud.
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High financial losses and customer dissatisfaction.
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Need for accurate fraud identification.
Pagos.ai was confronted with the challenge of helping their client combat issuer-level fraud. The client, a financial institution, was grappling with an increase in fraudulent activities, which jeopardized their operational integrity and customer trust. This surge in fraud detrimentally impacted their bottom line, as they faced heavy losses and saw a decline in customer satisfaction. Understanding the root cause of these activities was crucial. The client's primary goal was to identify fraudulent transactions accurately and minimize the financial damage, all while maintaining good customer relations. By leveraging the capabilities of Pagos.ai, they anticipated an efficient, data-driven approach to tackle these sophisticated fraud schemes at the issuer level.
2.
Task
Uncover fraud using BIN data
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Develop a system using BIN data.
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Segregate legitimate and fraudulent transactions.
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Enhance accuracy of fraud detection.
Pagos.ai’s task was clear: utilize BIN data to reveal and address fraudulent transactions. BIN (Bank Identification Number) data provides a critical foundation by identifying the issuing bank of a card, which is crucial in the detection of issuer-related fraud activity. By harnessing this data, Pagos.ai aimed to segregate legitimate transactions from fraudulent ones effectively. They needed to develop a system that would operate seamlessly with existing infrastructure, requiring minimal operational disruptions while significantly enhancing the accuracy of fraud detection. This precision approach was essential for reclaiming lost revenue and bolstering the client's financial security, with an emphasis on maintaining swift and reliable transaction processing for end-users.
3.
Action
Implemented BIN data analysis system
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Integrated BIN data analysis into workflows.
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Identified fraud patterns with advanced algorithms.
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Real-time analysis with continuous improvement.
Pagos.ai implemented a comprehensive system that integrates BIN data analysis into the client’s transaction processing workflows. This system utilized advanced algorithms to scan and analyze vast datasets in real-time, identifying patterns indicative of fraudulent behavior. With a focus on issuer-level analysis, Pagos.ai was able to pinpoint transactions that deviated from typical profiles. Continuous monitoring and machine learning refinements allowed the solution to evolve and improve accuracy over time, reducing false positives. The implementation was engineered to be seamless, ensuring that clients did not experience any interruption in service, while simultaneously enhancing the fraud detection capabilities significantly beyond traditional methods.
4.
Result
Significant fraud reduction and trust boost
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Reduced issuer-level fraud and losses.
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Increased customer retention and trust.
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Freed resources for growth and innovation.
Following the deployment of Pagos.ai’s BIN data analysis solution, the financial institution achieved a substantial reduction in fraudulent transactions at the issuer level. This not only led to significant cost savings—reducing potential losses—but also restored confidence among customers due to improved security measures. The client reported a marked increase in customer retention rates and satisfaction, as both were positively impacted by the improved security and efficiency of transaction processing. Additionally, the efficiency gains allowed the financial institution to reallocate resources, formerly used in manual fraud detection efforts, to other critical areas for further growth and innovation.
Keywords
PAGOS.AI
BIN DATA
FRAUD DETECTION
REAL-TIME ANALYSIS
ISSUER-LEVEL FRAUD
ADVANCED ALGORITHMS
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