Mar 11, 2024
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Service categories
Service Lines
Big Data
Domain focus
Banking & Financial Services
Other
Frameworks
Hadoop
Challenge
An Eastern European online bank faced difficulties with real-time fraud detection, understanding customer behaviour, and obtaining timely market insights.
To tackle these issues, the bank turned to Modsen for a Big Data solution. They acknowledged the necessity of an advanced analytics platform to manage growing data volume and complexity, delivering real-time insights for better decision-making.
An Eastern European online bank faced difficulties with real-time fraud detection, understanding customer behaviour, and obtaining timely market insights.
To tackle these issues, the bank turned to Modsen for a Big Data solution. They acknowledged the necessity of an advanced analytics platform to manage growing data volume and complexity, delivering real-time insights for better decision-making.
Solution
Modsen's platform is a scalable, flexible Big Data solution with a microservices architecture for modularity, flexibility, and third-party integration. Features include distributed storage for real-time structured and unstructured data, scalable to petabytes; data processing and analytics engine for complex queries, machine learning, and real-time analysis; advanced analytics tools like Hadoop, Hive, and PowerBI for data aggregation, querying, analysis, and real-time processing; security measures such as multi-factor authentication, encryption, and access controls for data protection.
The platform supports various use cases, including risk management, fraud detection, customer sentiment analysis, and personalized marketing.
Modsen's platform is a scalable, flexible Big Data solution with a microservices architecture for modularity, flexibility, and third-party integration. Features include distributed storage for real-time structured and unstructured data, scalable to petabytes; data processing and analytics engine for complex queries, machine learning, and real-time analysis; advanced analytics tools like Hadoop, Hive, and PowerBI for data aggregation, querying, analysis, and real-time processing; security measures such as multi-factor authentication, encryption, and access controls for data protection.
The platform supports various use cases, including risk management, fraud detection, customer sentiment analysis, and personalized marketing.
Results
After implementing the Big Data solution, the bank saw improvements in risk management, fraud detection, and customer satisfaction. Advanced analytics and machine learning provided real-time insights, leading to a 20% reduction in fraudulent activities; a 15% increase in customer satisfaction; and a 35% improvement in risk management analysis.
These results increased the bank's profitability, with an estimated $2 million annual revenue increase due to reduced fraud and higher customer satisfaction. Overall, the solution enhanced operational efficiency and positively impacted financial performance.
After implementing the Big Data solution, the bank saw improvements in risk management, fraud detection, and customer satisfaction. Advanced analytics and machine learning provided real-time insights, leading to a 20% reduction in fraudulent activities; a 15% increase in customer satisfaction; and a 35% improvement in risk management analysis.
These results increased the bank's profitability, with an estimated $2 million annual revenue increase due to reduced fraud and higher customer satisfaction. Overall, the solution enhanced operational efficiency and positively impacted financial performance.
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