By Brian Lam
In an industry that relies substantially on information, financial services institutions have long adopted the practice of gathering and analyzing data to improve business activities and decisions. In following more recent trends towards the automation of this process, financial businesses have begun turn their attention towards data mining and its benefits. Businesses such as banks, credit-card companies, insurance companies, etc. are implementing data mining tools and software within their companies to reap the benefits it has to offer. There are numerous applications of data mining within the finance industry, including illegal stock trading detection and customer relationship management. Although, as valuable as data mining can be for a company, it still has its challenges and limitations.
The benefits of data mining are invaluable to a financial organization such as an insurance company. For example, before implementing data mining, an insurance company was required to base their decisions off of “anecdotal evidence or hearsay” (Mazier, 2002). This would be an issue as the data gathered from personal statements could be incomplete or inaccurate. However, with data mining, these companies can now use more reliable data that would result in higher-quality decision making. Data mining for financial service companies is beneficial as it helps refine their business goals towards improving customer relations, evaluating competition, and analyzing the market.
With the advance of computing technology and software, data mining tools have become readily available for just about any company with the infrastructure to implement the tools. A short list of data mining tools and software include; Apache Hadoop, Oracle R Enterprise, SAS Enterprise Miner, IBM SPSS Modeler, and SAP Business Objects. These tools allow the selection and gathering of vast amounts of data to be processed and analyzed. More specifically, data mining tools processes raw data into; “description and visualization, association and clustering, and classification and estimation” (Koh, et al., 2002). This information now becomes useful to a company as it can be used to guide their future decisions.
Data mining can be applied in many different ways that can be invaluable to a company within the financial industry. One such application is detecting correlations that involve the illegal trading of stocks. A global stock exchange company, NYSE Euronext, implemented a “new markets surveillance platform that both sped up and simplified the processes which experts analyzed patterns within billions of trades” (Turner, et al., 2013). This system allowed the company to search and identify repeating signs that represented possible circumstances of illegal trading. Another application of data mining is improving customer relationships. As online interactions begin to overshadow face-to-face interactions, companies must turn to customer relationship management systems to maintain customer loyalty. For example, insurance company, Prudential, uses a system that “predicts the likelihood of customers switching carriers” (Mazier, 2002). Preventative measures are then taken to retain their customers. The various applications for data mining in the financial services industry are beneficial in enriching a company’s business activities.
Despite the value that data mining technology can be for a financial company, there are some drawbacks. One such challenge is that, while financial businesses are eager to adopt the idea of the use of data, many are not yet willing to buy in and implement the technology into their companies despite having the resources to do so. The interest is there, yet there is still some hesitation (Turner, et al., 2013). A limitation of data mining is that businesses should not look to it as the only answer. According to the vice president for CNA Insurance, Rama Prasad, “if a company’s business process is not sound and fails to address data-quality issues, no data mining product will help” (Mazier, 2002). Data mining is not the solution to a company’s issues and problems, but rather an accompaniment to its tools and resources for improving business activities.
When the issue of money is at the forefront of a company’s business, it is clear why the financial services industry has always been ahead of the game when it comes to the gathering and analysis of data. Now that technology has improved, data mining has become an efficient and cost effective resource for the companies that are willing and capable of implementing it. The amount of benefits and applications for data mining is vast and will only continue to grow as the software and technology grows. Despite the limitations and challenges, data mining is an invaluable tool for companies within the finance industry.
Koh, Hian Chye; Chan Kin Leong Gerry. Data mining and customer relationship marketing in the banking industry. Singapore Management Review; Singapore24.2 (2002): 1-27.
Mazier, E E. Insurers are striking gold with data mining technology. National Underwriter, Life, health/financial services ed.; Erlanger106.40 (Oct 7, 2002): 48-50.
Phua, Clifton; Lee, Vincent; Smith, Kate; Gaylor, Ross. A Comprehensive Survey of Data Mining-based Fraud Detection Research.
Turner, David; Schroeck, Michael; Shockley, Rebecca. Analytics: The real-world use of big data in financial services. IBM Global Services (May 2013).