Pith Number

pith:7FL6I2DK

pith:2026:7FL6I2DKIWXFPNNQKWS6S7XZIO

not attested not anchored not stored refs resolved

The Payment Heterogeneity Index: An Integrated Unsupervised Framework for High-Volume Procurement Oversight and Decision Support

Kyriakos Christodoulides

The Payment Heterogeneity Index flags suppliers with atypical payment structures using only unlabeled data.

arxiv:2605.12547 v1 · 2026-05-09 · econ.EM · cs.LG · q-fin.ST · stat.AP

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\usepackage{pith}
\pithnumber{7FL6I2DKIWXFPNNQKWS6S7XZIO}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

PHI functions as an effective discovery tool where no confirmed labels exist, offering a transparent, lightweight screening mechanism for post-award oversight.

C2weakest assumption

That the structural signatures captured by the Gaussian Mixture Model components and tail-behaviour measure correspond to financially meaningful deviations (errors, fraud, or corruption) rather than benign differences in legitimate payment practices.

C3one line summary

The Payment Heterogeneity Index (PHI) is a new unsupervised metric combining tail sensitivity and Gaussian mixture model-based structural dispersion to identify suppliers with atypical payment regimes in high-volume procurement data.

References

32 extracted · 32 resolved · 0 Pith anchors

[1] Fazekas, Mihály and Tóth, István János and King, Lawrence P. , year =. Anatomy of Grand Corruption: A Composite Corruption Risk Index Based on Objective Data , journal =

[2] Uncovering High-Level Corruption: Cross-National Objective Corruption Risk Indicators Using Public Procurement Data , journal =

[3] Anomaly Detection: A Survey , journal =

[4] Detecting Fraud in Public Procurement: A 2024

[5] Public Procurement Fraud Detection and Artificial Intelligence Techniques: A Literature Review , booktitle =

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.198135Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f957e4686a45ae57b5b055a5e97ef94392f4244139c92a924b9f1ee1b7fd9693

Aliases

arxiv: 2605.12547 · arxiv_version: 2605.12547v1 · doi: 10.48550/arxiv.2605.12547 · pith_short_12: 7FL6I2DKIWXF · pith_short_16: 7FL6I2DKIWXFPNNQ · pith_short_8: 7FL6I2DK

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7FL6I2DKIWXFPNNQKWS6S7XZIO \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f957e4686a45ae57b5b055a5e97ef94392f4244139c92a924b9f1ee1b7fd9693

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "01895ef29b4f89eef16e33a1805f2cf75430ca7a2cfcecd8b6d86441865119b3",
    "cross_cats_sorted": [
      "cs.LG",
      "q-fin.ST",
      "stat.AP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "econ.EM",
    "submitted_at": "2026-05-09T20:59:29Z",
    "title_canon_sha256": "81eac1558cd061c5e67e0bf673e5e0f2bf4574ac223656f98a1dde3ddc09954a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12547",
    "kind": "arxiv",
    "version": 1
  }
}