Decision Support Systems in Banking

“A decision support system (DSS) improve operational efficiency and business performance by enhancing the ability of stakeholders to make faster, smarter decisions based on information, rather than gut instinct alone.” (Information Builder, 2019).

DSS gathers information relating to projection in terms of sales and revenues, comparative analysis of sales between varying periods and inventory analysis (Techopedia, 2019).

Dan (2015) highlighted the five (5) types of DSS as Data-driven DSS, Communication-driven DSS, Document-driven DSS, Knowledge-driven DSS and Model-driven DSS.

Opportunities

According to (UK Essays, 2016), DSS can help bank achieve speedy communication, improved communication and collaboration, improved data management and quality support; DSS and Data mining (DM) when combined to complement each other can support a bank in achieving the following:

  • Credit Risk
  • Fraud detection
  • Customer retention
  • Product marketing
  • Knowledge organisation, distribution and refinement

Using DeLone and McLean model to evaluate DSS in banking sector based on parameters like system quality, information quality, service quality, user satisfaction and individual impact; it shows that system quality, information quality and service quality affects user satisfaction which in turns influences clearly the individual impact (Manchanda & Saurabh, 2014).

Braendle et al. (2014) in their study using fuzzy SERVQUAL method shows that with DSS in banking sector, not only that service quality can be measured but the impacts of Corporate Social Responsibility (CSR) and e-Banking can be measured as well.

In the area of e-Banking industry, DSS has been established as a useful tool in the process of decision making and the enthusiasm of accept is overwhelming (UK Essays, 2013).

Challenges

DSS Requirement varies between different categories of banks – commercial bank, development banks, etc. so adaption is not easy; low level of awareness about embedded models of DSS in banks among bank staff is a challenge to the banking sector (Yanwei, 2010).

Reference

  1. Braendle, U., Sepasi, S., & Rahdari, A.H. (2014). Fuzzy Evaluation of Service Quality in the Banking Sector: A Decision Support System. Fuzzy Economic Review, 19(2), 47-79.
  2. GDRC (2019). Types of Decision Support Systems (DSS). Retrieved 12 February, 2019, http://www.gdrc.org/decision/dss-types.html
  3. Information Builder (2019). Decision Support System. Retrieved 12 February, 2019, from https://www.informationbuilders.com/decision-support-systems-dss
  4. Manchanda, A., & Saurabh, M. (2014). An empirical application of DeLone and McLean model in evaluating decision support system in the banking sector of Oman. Journal of International Technology and Information Management, 23(2), 47-58. doi: 10.1108/eb010840
  5. Techopedia (2019). Decision Support System (DSS). Retrieved 12 February, 2019, from https://www.techopedia.com/definition/770/decision-support-system-dss
  6. UK Essays (2016). Decision Support Systems In Banking Information Technology Essay. Retrieved 12 February, 2019, from https://www.ukessays.com/essays/information-technology/decision-support-systems-in-banking-information-technology-essay.php
  7. UK Essays (2013). Decision Support System in the E-banking Sector. Retrieved 12 February, 2019, https://www.ukessays.com/dissertation/literature-review/banking/decision-support-system-in-technology-of-the-e-banking.php?vref=1
  8. Yanwei, M. (2010). Decision Support System – A study of strategic decision makings in banks. Retrieved 12 February, 2019, from https://www.scribd.com/doc/36262377/DSS-in-Banks

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