Publication2023
A Sampling-Based Logistic Regression Model for Credit Card Fraud Estimation
The record
- Type
- Publication
- Date
- 1 January 2023
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 788 LNNS
- Pages
- 201-209
- Published
- 1 January 2023
- DOI
- 10.1007/978-981-99-6553-3_16
- Scopus ID
- 85181981354
- ISSN
- 23673370
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
16 Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels
Sampling-based logistic regression model achieves optimal accuracy and precision in credit card fraud detection supporting financial crime prevention.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9 Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
Machine learning methodology innovation in fraud detection represents research advancement in security applications.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-4.4 Public safety, cybersecurity and national security
Fraud detection innovation supports cybersecurity and protection against financial crimes.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “A Sampling-Based Logistic Regression Model for Credit Card Fraud Estimation”. Kortex.