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New Technologies Enhance Financial Data Mining and Analysis

With the advancements in technology, the way information is retrieved and organized is changing rapidly. In the field of financial data mining and analysis too, new technologies such as Hadoop, NoSQL and Storm are enhancing access to complex and unstructured data. Real-time financial analysis is crucial to financial decision making for better profit planning, cash management, capital investment, dividend policy, and return on investment. The use of these new technologies to analyze and process big data is playing an important role in financial market operations.

Importance of Data Mining in the Financial Sector

  • Due to wide spread usage of electronic banking, the volume of transactional data has grown considerably. It is beyond human capability to analyze large volumes of amount of raw data. New data mining tools allow financial analysts and bankers to analyze hidden patterns and trends to make predictions on the following aspects:
    • How customers will react to adjustments in interest rates
    • Which customers will be likely to accept new product offers
    • Which customers will be at a higher risk for defaulting on a loan
    • How to enhance customer relationships for higher profit
  • Maintaining strong and effective customer relations is a critical issue in banking. For this, banks need to invest extra resources to better understand their existing and prospective customers. Mining customer data allows them to identify customer problems, improve management of their accounts, initiate real-time dialogue with customers and improve overall customer experience.
  • Banks use data mining techniques to identify their most profitable credit card customers or high risk loan applicants. Analyzing past data helps banks to predict customers who are likely to change their credit card affiliation which allows the development of strategies such as special offers to retain these customers.
  • Data mining assists credit card issuers in detecting fraudulent credit card transaction. Its artificial intelligence techniques and algorithms can detect fraud by extracting knowledge from unusual patterns from gathered data.
  • Analysis of social media streams such as LinkedIn, and Twitter, helps stock traders to analyze trading activity, and it’s trending over time, and to make investment decisions accordingly. Software applications like NETPROPHET make use of data mining techniques for stock prediction.

All businesses generate a huge volume of data as a part of day to day operations and through other internal and external sources. Transforming this data into knowledge is possible only with use of data mining software. Traditional databases cannot easily handle or store different types of data in raw form including social and rich document data. New big data technologies like Hadhoop have made this possible.

With global competition and dynamic markets, the banking and finance industry is continually faced with unique challenges. Harnessing real-time information allows them to improve their competitiveness and flexibility. Professional data mining services are available to help financial analysts extract valuable information from big data using the latest tools.

About Julie Clements

Julie Clements

Joined the MOS team in March of 2008. Julie Clements has background in the healthcare staffing arena; as well as 6 years as Director of Sales and Marketing at a 4 star resort. Julie was instrumental in the creation of the medical record review division (and new web site); and has especially grown this division along with data conversion of all kinds.