Descriptive analytics for operational risk intelligence in financial services

• Main Author: Villaraza, Jeannie R.
• Format: text
• Language: English
• Published: Animo Repository 2023
• Subjects: Financial services industry > Risk management; Financial services industry > Information technology; Databases and Information Systems
• Online Access: https://animorepository.dlsu.edu.ph/etdm_infotech/16; https://animorepository.dlsu.edu.ph/context/etdm_infotech/article/1017/viewcontent/Descriptive_Analytics_for_Operational_Risk_Intelligence_in_Financ.pdf
• Institution: De La Salle University

The impacts and consequences of COVID-19 pandemic significantly changed the strategic direction of many industries globally. In the financial services industry operational resiliency of many financial institutions were challenged driving the shift from the traditional risk management approach into the implementation of risk intelligence solutions. Risk experts recognized the importance of using emerging technologies to build the capability to learn from external sources to supplement internal risk management processes as an important factor to support operational resiliency. The purpose of the study is to develop an analytics system that will provide operational risk intelligence from a reliable external source useful for financial institutions to gather external insights and support operational resiliency. It involved stimulation of business need for an analytics system in a selected financial institution to validate insights derived from the system and impact to the operational risk management process. The methodology used is the Knowledge Discovery in Databases (KDD) Process Model appropriate for discovering external risk intelligence. Multiple iterations of system development and evaluation were performed to identify business relevant insights that primarily relate to the business, product, cause, and event types. The overall result of the descriptive analytics revealed new operational risk intelligences that can be presented to various risk management discussions confirmed by the risk experts and focus group discussion participants. Therefore, the stimulation activity of the study helped recognize the need for an analytics system to augment the internal risk management process in the selected financial institution. Key words: data analytics, operational risk management, descriptive analytics, risk intelligence, financial services