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case_id
string
claim_amount
float64
delay_days
float64
buyer_type
string
contract_present
float64
industry_sector
string
outcome_label
string
claim_imputed
int64
delay_imputed
int64
case_0
8,910,000
1,825
govt
1
Water Supply & Waste Management
escalation
0
0
case_1
38,033,930.743332
940
govt
1
Electronics
escalation
1
1
case_10
80,600,000
940
govt
1
Road Construction
escalation
0
1
case_100
1,461,000
940
govt
0
Financial Services
escalation
0
1
case_1000
82,160,179.934008
940
govt
1
Auditing
escalation
1
1
case_1001
7,085,444
940
private
1
Manufacturing
escalation
0
1
case_1002
665,504
886.5
private
1
Manufacturing
win
0
1
case_1003
53,872,390.197476
886.5
govt
1
Advertising
win
1
1
case_1004
2,644,574
940
govt
1
General Services/Goods
escalation
0
1
case_1005
207,900.96
886.5
private
1
General Business
win
0
1
case_1006
7,386,896
940
govt
1
Consultancy Services
escalation
0
1
case_1007
58,671,676.436168
574
private
1
Infrastructure
escalation
1
0
case_1008
621,348.05
3,598
private
1
Telecommunications Equipment
win
0
0
case_1009
58,153,622.366857
940
private
1
Hybrid Seeds Manufacturing
escalation
1
1
case_101
316,896
334
govt
1
Manufacturing/Supply of Cranes
win
0
0
case_1010
64,380,881.846098
940
private
1
General
escalation
1
1
case_1011
76,734,314.961241
940
govt
1
Energy Infrastructure
escalation
1
1
case_1012
78,179,204.700055
886.5
govt
1
Infrastructure
win
1
1
case_1014
31,401,984.058996
940
govt
1
Electrical Equipment Manufacturing
escalation
1
1
case_1015
576,658
886.5
private
1
Logistics
win
0
1
case_1017
107,360,290.005125
940
private
1
Oil & Gas Infrastructure
escalation
1
1
case_1019
66,600,000
886.5
private
1
Textile
win
0
1
case_102
336,551,252.345473
940
govt
0
Waste Management
escalation
1
1
case_1020
584,922.99
654
govt
1
Unspecified Business
win
0
0
case_1021
12,160,345
1,615
govt
1
Agriculture
win
0
0
case_1022
25,803,143
868
private
1
Construction
escalation
0
0
case_1025
50,395,544.094613
940
private
1
Manufacturing
escalation
1
1
case_1026
197,202,000
366
govt
1
Manufacturing/Engineering
win
0
0
case_1027
838,062
1,749
private
1
IT/Technical Services
win
0
0
case_1028
1,932,990
403
private
1
Construction
win
0
0
case_1029
103,668,863.596062
940
private
1
Textile
escalation
1
1
case_1030
32,084,716.995394
553
private
1
Poultry/Agro-based
win
1
0
case_1031
276,342,610
1,057
govt
0
Manufacturing
escalation
0
0
case_1032
83,293,388.43
2,475
govt
1
Infrastructure/Construction
win
0
0
case_103
353,854,010.469738
250
private
1
Unknown
escalation
1
0
case_1033
32,986,945.031729
886.5
private
1
Manufacturing (Insulation)
win
1
1
case_1034
58,293,703.789298
940
private
1
Construction
escalation
1
1
case_1035
58,293,703.789298
940
private
1
Construction
escalation
1
1
case_1036
3,616,362
886.5
govt
1
Telecommunication
win
0
1
case_1037
77,713,672.944387
940
private
1
Environmental Services
escalation
1
1
case_1038
154,460,408.063509
987.5
private
1
Textile/Garments
settlement
1
1
case_1039
2,909,498
2,321
private
1
Manufacturing (Batteries)
win
0
0
case_104
6,364,111
1,328
private
1
Manufacturing (Electrical)
win
0
0
case_1046
215,462.97
1,096
private
1
Facility Management
win
0
0
case_1047
24,640,000
1,738
govt
0
Manufacturing
win
0
0
case_1048
2,556,510.5
940
govt
1
Construction
escalation
0
1
case_1049
1,549,234
886.5
private
0
Pest Control Services
win
0
1
case_1054
636,450
886.5
private
1
Manufacturing
win
0
1
case_1055
1,906,368.11
1,132
private
1
Food Processing/Retail
win
0
0
case_1056
5,581,817
354
private
1
Manufacturing
win
0
0
case_1057
980,309
1,094
private
1
Manufacturing
win
0
0
case_1058
80,603,853
261
private
1
Automotive Manufacturing
win
0
0
case_1059
892,500
886.5
govt
1
Logistics/Agriculture
win
0
1
case_106
1,326,812,792.428858
886.5
private
1
Real Estate/Hospitality
win
1
1
case_1061
702,969
374
private
1
Travel/Hospitality
win
0
0
case_1062
81,986
982
private
1
Micro, Small and Medium Enterprise
win
0
0
case_1065
48,526,676
3,013
private
1
Telecommunications Infrastructure
escalation
0
0
case_1066
6,040,525
1,985
private
1
Electronics Manufacturing
escalation
0
0
case_1067
224,875
28
private
1
Beauty Services
win
0
0
case_1068
121,564
357
private
1
Manufacturing (Soap/Phenyl)
win
0
0
case_107
77,741,321
940
private
1
Unknown
escalation
0
1
case_1070
620,217
381
govt
1
Hospitality/Accommodation
settlement
0
0
case_1071
849,354,606
940
govt
0
Financial Services
escalation
0
1
case_1072
1,282,736
424
govt
1
Retail/Publishing
settlement
0
0
case_1073
10,713,081
940
private
1
Oil & Gas/Chemicals
escalation
0
1
case_1076
323,787,466.405901
940
govt
0
Financial Services
escalation
1
1
case_1080
12,069,410
1,248
private
1
Construction
settlement
0
0
case_1084
69,529,331.789493
886.5
private
1
Packaging
win
1
1
case_1085
688,658
886.5
private
1
Food Services
win
0
1
case_1086
50,395,544.094613
940
private
1
Manufacturing
escalation
1
1
case_1087
33,283,194.054176
886.5
govt
1
Manufacturing
win
1
1
case_1091
51,720,722.108539
886.5
private
0
Packaging
win
1
1
case_1092
58,293,703.789298
940
private
1
Construction
escalation
1
1
case_1093
4,588,689
357
private
1
Electrical Manufacturing
win
0
0
case_1094
35,809,453.865242
886.5
govt
1
Metal Manufacturing
win
1
1
case_1095
28,483,011.385077
940
govt
1
Electrical Services
escalation
1
1
case_1097
1,401,505
940
private
0
Plywood Manufacturing
escalation
0
1
case_1098
74,394,494.887556
886.5
govt
0
Metal Manufacturing
win
1
1
case_1099
5,942,986.21
2,608
private
1
Steel
escalation
0
0
case_1104
78,933,137
552
private
1
Construction
escalation
0
0
case_1105
341,560,524.803292
940
govt
0
Food Supply
escalation
1
1
case_1106
48,204,541.193968
940
private
1
Chemicals
escalation
1
1
case_1107
41,965,956.805648
886.5
govt
1
Electrical Infrastructure
win
1
1
case_1108
1,779,819,021
940
private
1
Manufacturing (Forging/Steel)
escalation
0
1
case_1109
39,744,450.940108
886.5
private
1
Construction
win
1
1
case_111
22,517,330
886.5
private
1
Manufacturing (Forging/Steel)
win
0
1
case_1110
266,325
940
private
1
Construction/Building Products
escalation
0
1
case_1111
1,437,826
886.5
private
1
Footwear
win
0
1
case_1112
78,448,742.990512
886.5
govt
0
infrastructure/railways
win
1
1
case_1113
230,292
90
private
1
hospitality
win
0
0
case_1115
16,000,000
987.5
private
1
construction
settlement
0
1
case_1116
20,990,266.67
886.5
govt
1
power/energy
win
0
1
case_1118
373,778.66
886.5
private
1
transportation/services
win
0
1
case_1119
1,374,715
886.5
private
1
textile/apparel
win
0
1
case_112
77,713,672.944387
940
private
1
Engineering/Manufacturing
escalation
1
1
case_1120
48,324,701.000458
886.5
govt
1
Manufacturing (Copper)
win
1
1
case_1121
1,601,688.77
886.5
private
1
Pharmaceuticals/Distribution
win
0
1
case_1124
30,000,000
987.5
private
1
Manufacturing (Textiles)
settlement
0
1
case_1125
77,745,694.559453
940
govt
1
Construction/Engineering
escalation
1
1
case_1126
1,377,480
886.5
private
1
Manufacturing (Steel)
win
0
1
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MSME Payment Dispute Dataset

Description

This dataset contains structured case-level information for MSME payment disputes in India.

The dataset was constructed from structured extraction of:

  • MSME arbitration awards
  • Commercial court decisions
  • Public legal case repositories

All records are anonymized and structured for machine learning purposes.

Dataset Size

  • ~4,600 structured cases
  • 3 outcome classes:
    • win
    • settlement
    • escalation

Features

Column Description
claim_amount Monetary claim value
delay_days Payment delay duration
buyer_type govt / private
contract_present Whether formal contract exists
industry_sector Sector classification
claim_imputed Indicator if claim was imputed
delay_imputed Indicator if delay was imputed
outcome_label Target outcome (win / settlement / escalation)

Preprocessing Steps

  • Removed rare labels
  • Removed invalid zero values
  • Outcome-based delay imputation
  • Model-based claim imputation
  • Missingness flags added (claim_imputed, delay_imputed)

Intended Use

  • Research in legal AI
  • Tabular ML benchmarking
  • MSME risk analysis
  • Delay & claim prediction modeling
  • Fairness & bias studies in legal outcomes

Limitations

  • Structured data only (tabular)
  • No raw legal document text included
  • Synthetic augmentation applied in some cases
  • Not an official government dataset
  • Potential selection bias (only decided/arbitrated cases)
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