Fraud and risk analysts use various Big Data technologies to prevent fraud, limit potential risks, and comply with regulations in the digital age. For instance, real-time data processing tools and risk management models are crucial to modern analysts as the ability to prevent both fraud and risk before they occur is vital for companies, the inability to do so can result in the loss of millions in revenue, and an irreversible loss of costumers.
Regulatory mandates are complex and constantly changing. Most require the aggregation, analysis, and reporting of diverse data sets. The massive data sets often scattered throughout a company and managed by different teams. Financial service providers are implementing Big Data solutions, which includes data aggregation, analysis, data governance, and reporting capabilities. By streamlining compliance efforts, a financial service provider can improve regulatory reporting reduce operational costs.
Financial fraud can cost a firm millions, and in some cases, ruin its reputation. In order to detect fraud, and possibly prevent future fraudulent acts, financial service providers are aggregating numerous types of data to identify previously unseen patterns and anomalies. A successful fraud prevention solution can help financial firms reduce fraud investigation associated operational costs.
It is hard to find an industry with more data than the financial industry. Today’s financial service providers aggregate and analyze various data types from numerous sources, including but not limited to financial transaction data, merchant data, social media data, and data within the providers' mainframe. Every interaction between a financial service provider and a client produces actionable data, and to stay competitive, protecting this data is imperative.
Fraud and Risk Use Cases
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