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Paper Citation Record · LEDGER

Bank Loan Prediction Using Machine Learning Techniques

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.08886.

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

pith.paper-citation-record.v1
2410.08886 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 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:51:01.996473Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T14:21:51.107165Z

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 0159df57-2038-41be-8988-a8d3d03f5b54 · inbound

QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction cites this paper.

QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction Bank Loan Prediction Using Machine Learning Techniques

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-16T05:51:01.996473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T05:51:01.996473Z digest=sha256:73249c9dbfab4469a5838bd41ec0cb222576d38679974cfdaf3622d9c3d55532

Observation abd8126d-2f40-44e7-af29-9a9a24c57ad7 · inbound

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches cites this paper.

Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches Bank Loan Prediction Using Machine Learning Techniques

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-05T14:21:51.151432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-08-05T14:21:46.474850Z digest=sha256:a9bf2608fc296b459eabeeb725cc75a64cbc6a93709f5b14789af1a7fb3767c0