Fraud Detection in the Financial Sector Using Data Lakes

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Financial sector companies have the opportunity to leverage analytics through Hadoop to counter risk or increase profits.
The financial sector is increasingly moving towards new paradigms and technologies such as cloud and mobile , and this adds complexity when facing growing challenges such as:

Direct financial losses resulting from fraud and financial crimes.
Indirect losses due to reputational impact.
Significant operational costs to address these issues.
Customer satisfaction issues due to inconveniences caused by false czech republic number dataset that hinder legitimate transactions.
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All of this has increased the need for bank executives to improve their effectiveness in fighting fraud.

And it is precisely this increase in financial transactions through digital channels that is already enabling new ways to leverage big data to offer more secure and profitable services . Transaction weblogs and machine data provided by sensors open up new opportunities to monitor and analyze physical events in search of anomalous activities.

Companies in the financial sector now have the opportunity to leverage analytics through new platforms such as Hadoop , and they can do so to both counter risk and increase profits. Effective detection efforts that identify anomalous or fraudulent activities not only successfully counter legal or profitability-related risks, but also enhance a company’s brand image in an increasingly competitive environment . Therefore, successful big data management not only helps counter fraud, but also pleases customers who are looking for more security and a broader range of services.



The challenge
Data analysts in the financial and insurance industry struggle with the ever-increasing volume, variety, and velocity of data. With increasing consolidation in the industry and the growth of data silos, analysts waste a lot of time manually finding and reconciling fragmented, duplicated, inconsistent, inaccurate, and incomplete data across the organization . If they can’t access and share the data they need with each other in a timely manner, they risk producing incomplete reports and making predictions that can’t be trusted. And this can result in lower-quality fraud detection and risk management .

Traditional solutions have typically focused on either costly, manual, and time-consuming processes or on integrating fragmented point solutions. Both force analysts to wait weeks for useful data. However, a systematic approach to managing big data through data lakes enables analysts to quickly and repeatedly extract business value from a large amount of data without increasing risk .



Key Benefits
Find any data and discover important relationships
Machine learning-based data discovery enables financial services analysts to find new data and relationships that would otherwise be challenging and time-consuming to find manually . Real-time big data matching and linking accelerates and refines data mastery and data relationship discovery for all critical enterprise data. Data analysts can find any data and uncover the most important relationships to perform accurate and targeted fraud and risk analysis .