Big Data Use Cases in Financial Services

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Big Data Use Cases in Financial Services

Big Data Use Cases in Financial Services
Most companies believe that big data can be a great asset when analyzed correctly and put into proper use. Financial institutions understand that big data mining will give them a great opportunity when it comes to standing out against fellow competitors. Currently, the data landscape for financial service providers is changing at a high speed in that even the institutional data generated is not enough. This means that this data has to be used together with open data, like that collected from social media, to make the decision-making process more effective. Financial service providers are now using data science and machine learning to collect and analyze big data, which is crucial for reinventing their businesses. They are aware of the massive potential in big data. That is why they have incorporated data science and machine learning technologies in their institutions to enable them to propel their businesses to a whole new level. Below are the big data use cases in financial services:

  • 1. Compliance and regulatory requirements
    There is a heavy regulator framework put in place to govern the financial services industry. Financial service providers are being strictly monitored and are required to file regular reports with regards to their operations. Financial acts have been put in place that require monitoring deals and also keeping records of any trade dealings. This data can then be used for surveillance of trade that can identify abnormal trading patterns.
  • 2. Detection of fraud
    Firms understand what big data can do with the right data experts in place. You can outsource data experts from service providers like Activewizads, and all your big data needs will be solved. Analytics has been of great help to financial service providers when it comes to differentiating fraudulent activities from legitimate business transactions. With the help of analytics and machine learning, based on the customer’s history you can identify any abnormal activity or unusual behavior as being an indicator of fraud. The analysis will not only help with the identification of theses cases, but also it will give suggestions on the course of action to take. This includes the blocking of transactions that seem irregular, thus preventing the fraud before it actually occurs.
  • 3. Customer segmentation
    Financial service firms are under pressure to transform from product-oriented businesses to customer-oriented businesses. The best way of making this change is understanding your customers, which can be made possible through segmentation. With big data you can group the customers in different segments, which can be determined by either demographics, daily transactions, or external data, like their home value. Promotions and marketing campaigns will then be tailored in accordance with each customer segment.
  • 4. Management of risk
    Each and every business must actively engage itself in risk management, lest it suffers great losses. The need for risk management is even greater for financial service providers. Regulatory schemes have been put in place, which require companies to manage their liquidity risk by stress testing. Financial service providers can also use big data to determine the customer risk by analyzing their portfolios. With big data, the financial firms are also able to obtain alerts in real-time when the risk threshold has been surpassed.

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