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Paper Citation Record · LEDGER

A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1912.02629.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1912.02629 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:48:52.564810Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T21:17:25.053505Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6b817ec5-3ffb-45a7-8ba0-3309884c8e20 · inbound

Density-Ratio Losses for Post-Hoc Learning to Defer cites this paper.

Density-Ratio Losses for Post-Hoc Learning to Defer A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection

Reference 82

Resolution
verified exact
arxiv_id, observed 2026-05-20T02:47:58.935060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T02:43:25.793783Z digest=sha256:4ea6127a083a803dcdf52300a0ace7c8daefbcb72d8d20400b989c1248b98976

Observation 76f7da30-0c1b-47d8-a3b2-ca1b90382c2c · inbound

SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection cites this paper.

SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:17:25.055343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T19:48:52.564810Z digest=sha256:b8b366e957871d254d728c0c6e8688215e54a8208b629f955f00c6fbf6ddd234