Descriptive analytics for operational risk intelligence in financial services
Clicks: 1
ID: 286354
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #2,993 of 3,757 articles by views in Malay Journal
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
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
| Reference Key |
persistent_1760658324_68f1839423837
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Villaraza, Jeannie R. |
| Journal | Malay Journal |
| Year | 2023 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.