Datasets:

electricsheepafrica
/

africa-synth-insurance-fraud-detection-all

record_id int64	country string	year int64	claim_amount_usd float64	policy_duration_months int64	previous_claims_count int64	reporting_delay_days int64	document_completeness_score float64	claimant_age int64	claim_type string	seasonality_flag int64	amount_deviation_zscore float64	duplicate_claim_flag int64	fraud_label int64	fraud_probability float64	fraud_scheme_type string	detection_method string	recovery_amount_usd float64
1	South Africa	2,024	3,470.38	107	0	2	0.763	35	property	0	0.287	1	1	0.1163	inflated_claim	ml_model	1,749.6
2	South Africa	2,024	512.03	94	4	12	0.543	40	health	1	-1.188	0	0	0.1042	none	none	0
3	South Africa	2,024	4,450.99	82	2	6	0.625	55	travel	1	0.776	1	0	0.1512	none	none	0
4	South Africa	2,024	4,869.24	30	5	6	0.797	46	health	0	0.984	0	1	0.2996	collusion	rule_based	2,803.31
5	South Africa	2,024	50	75	0	27	0.882	40	life	0	-1.419	0	0	0.0802	none	none	0
6	South Africa	2,024	50	99	1	33	0.675	43	motor	0	-1.419	0	0	0.0738	none	none	0
7	South Africa	2,024	3,081.25	9	2	2	0.709	65	motor	0	0.093	0	0	0.1533	none	none	0
8	South Africa	2,024	2,104.27	22	0	11	0.589	26	property	1	-0.394	0	0	0.1255	none	none	0
9	South Africa	2,024	2,763.04	62	1	17	0.819	55	property	0	-0.066	0	0	0.1265	none	none	0
10	South Africa	2,024	923.3	44	1	1	0.974	38	motor	0	-0.983	0	0	0.1047	none	none	0
11	South Africa	2,024	4,734.68	86	2	21	0.852	37	travel	0	0.917	0	0	0.158	none	none	0
12	South Africa	2,024	4,511.14	49	1	2	0.889	46	motor	1	0.806	0	0	0.1886	none	none	0
13	South Africa	2,024	2,945.27	120	0	2	0.692	18	motor	0	0.025	0	0	0.0864	none	none	0
14	South Africa	2,024	5,279.93	36	1	26	0.819	25	life	0	1.189	0	0	0.1909	none	none	0
15	South Africa	2,024	3,828.52	44	0	14	0.677	43	health	0	0.465	0	0	0.1725	none	none	0
16	South Africa	2,024	909.56	1	0	49	0.623	43	health	0	-0.99	0	0	0.1319	none	none	0
17	South Africa	2,024	3,611.25	30	2	3	0.556	45	crop	1	0.357	0	0	0.1798	none	none	0
18	South Africa	2,024	690.46	29	3	6	0.947	47	health	0	-1.099	0	0	0.1285	none	none	0
19	South Africa	2,024	4,732.59	92	4	29	0.587	62	motor	1	0.916	0	0	0.1833	none	none	0
20	South Africa	2,024	2,690.16	41	2	3	0.722	30	travel	0	-0.102	0	0	0.1312	none	none	0
21	South Africa	2,024	2,393.3	25	0	4	0.75	43	property	0	-0.25	1	1	0.15	provider_fraud	ml_model	1,461.25
22	South Africa	2,024	1,301.96	38	1	7	0.493	47	motor	1	-0.794	0	0	0.1194	none	none	0
23	South Africa	2,024	5,489.59	68	3	34	0.839	42	motor	0	1.294	0	1	0.2233	provider_fraud	ml_model	2,773.38
24	South Africa	2,024	2,460.04	95	3	8	0.741	37	health	0	-0.217	0	0	0.1211	none	none	0
25	South Africa	2,024	1,857.68	118	0	2	0.735	29	travel	1	-0.517	0	1	0.0804	staged_accident	manual_review	1,228.05
26	South Africa	2,024	2,025.31	6	1	4	0.403	36	health	0	-0.434	0	0	0.1448	none	none	0
27	South Africa	2,024	3,971.08	101	1	15	0.786	54	health	0	0.536	0	0	0.1304	none	none	0
28	South Africa	2,024	3,603.98	47	1	3	0.444	64	health	0	0.353	0	0	0.1426	none	none	0
29	South Africa	2,024	3,708.01	97	1	7	0.544	18	motor	1	0.405	0	0	0.1086	none	none	0
30	South Africa	2,024	3,747.81	5	3	4	0.809	38	health	0	0.425	0	0	0.2162	none	none	0
31	South Africa	2,024	7,511.62	76	2	36	0.641	18	motor	1	2.302	0	1	0.2127	inflated_claim	ml_model	3,306.63
32	South Africa	2,024	1,905.89	112	2	0	0.551	29	travel	0	-0.493	0	0	0.0833	none	none	0
33	South Africa	2,024	1,673.07	5	2	16	0.462	41	motor	0	-0.609	0	0	0.1437	none	none	0
34	South Africa	2,024	1,009.7	95	1	1	0.783	24	health	0	-0.94	0	0	0.0741	none	none	0
35	South Africa	2,024	4,155.15	14	2	28	0.762	33	property	0	0.628	1	0	0.1886	none	none	0
36	South Africa	2,024	5,283.74	118	0	11	0.681	31	health	0	1.191	0	1	0.1426	inflated_claim	manual_review	2,971.72
37	South Africa	2,024	2,549.32	90	2	0	0.804	40	travel	0	-0.172	0	0	0.113	none	none	0
38	South Africa	2,024	951.66	110	0	11	0.903	33	motor	1	-0.969	0	0	0.0744	none	none	0
39	South Africa	2,024	986.14	108	1	35	0.601	52	motor	1	-0.952	0	1	0.0791	staged_accident	tip_off	156.81
40	South Africa	2,024	4,231.3	38	2	62	0.488	37	health	1	0.666	0	0	0.1787	none	none	0
41	South Africa	2,024	4,435.16	50	1	5	0.848	27	health	0	0.768	0	0	0.1614	none	none	0
42	South Africa	2,024	3,994.94	83	0	1	0.411	19	crop	0	0.548	1	0	0.1221	none	none	0
43	South Africa	2,024	1,335.88	21	1	41	0.769	28	motor	0	-0.778	0	0	0.1134	none	none	0
44	South Africa	2,024	3,310.75	37	2	4	0.819	35	travel	0	0.207	0	0	0.1535	none	none	0
45	South Africa	2,024	3,056.71	12	2	28	0.685	44	motor	0	0.08	0	0	0.1767	none	none	0
46	South Africa	2,024	3,281.11	107	3	6	0.743	46	property	0	0.192	1	0	0.1391	none	none	0
47	South Africa	2,024	4,717.14	30	1	3	0.663	49	life	1	0.908	0	0	0.2048	none	none	0
48	South Africa	2,024	3,291.91	50	0	34	0.763	45	motor	1	0.198	0	0	0.1576	none	none	0
49	South Africa	2,024	4,293.61	55	3	4	0.716	50	motor	1	0.697	0	0	0.1949	none	none	0
50	South Africa	2,024	2,948.67	46	1	8	0.975	34	property	0	0.027	0	0	0.1384	none	none	0
51	South Africa	2,024	3,436.06	94	2	20	0.968	36	property	1	0.27	1	1	0.1235	provider_fraud	manual_review	1,584.85
52	South Africa	2,024	4,188.83	110	4	23	0.971	31	travel	0	0.645	0	0	0.1607	none	none	0
53	South Africa	2,024	50	69	3	12	0.519	28	property	1	-1.419	0	0	0.0847	none	none	0
54	South Africa	2,024	2,096.72	101	2	21	0.907	54	health	1	-0.398	0	0	0.0964	none	none	0
55	South Africa	2,024	1,765.18	14	3	5	0.9	36	travel	0	-0.563	0	0	0.1527	none	none	0
56	South Africa	2,024	1,394.47	116	1	2	0.577	24	health	1	-0.748	0	0	0.0717	none	none	0
57	South Africa	2,024	2,194.69	14	2	0	0.601	28	motor	0	-0.349	0	0	0.1341	none	none	0
58	South Africa	2,024	6,088.87	8	3	28	0.878	63	crop	0	1.592	0	0	0.2714	none	none	0
59	South Africa	2,024	895.17	3	1	19	0.879	41	health	1	-0.997	0	0	0.1282	none	none	0
60	South Africa	2,024	4,930.21	30	1	3	0.758	43	health	0	1.015	0	0	0.2145	none	none	0
61	South Africa	2,024	50	101	0	1	0.885	31	travel	0	-1.419	0	1	0.0654	phantom_claim	ml_model	16.29
62	South Africa	2,024	2,063.25	37	1	8	0.506	18	crop	0	-0.415	1	0	0.1087	none	none	0
63	South Africa	2,024	3,158.06	95	2	15	0.784	23	motor	0	0.131	0	0	0.1048	none	none	0
64	South Africa	2,024	4,089.69	51	4	1	0.626	30	motor	0	0.596	0	0	0.2001	none	none	0
65	South Africa	2,024	4,364.7	13	1	8	0.838	30	property	0	0.733	0	0	0.1906	none	none	0
66	South Africa	2,024	4,545.36	57	0	25	0.663	38	property	0	0.823	0	1	0.1747	inflated_claim	rule_based	731.25
67	South Africa	2,024	2,032.8	28	3	14	0.79	48	life	0	-0.43	0	0	0.1578	none	none	0
68	South Africa	2,024	1,782.83	35	1	3	0.847	38	crop	0	-0.555	1	1	0.1251	staged_accident	manual_review	799.13
69	South Africa	2,024	4,687.55	63	0	3	0.887	43	motor	0	0.894	0	0	0.1814	none	none	0
70	South Africa	2,024	2,379.13	95	4	6	0.786	49	motor	0	-0.257	0	0	0.141	none	none	0
71	South Africa	2,024	50	71	0	4	0.517	55	motor	1	-1.419	0	0	0.078	none	none	0
72	South Africa	2,024	306.77	27	2	8	0.613	27	health	1	-1.291	0	0	0.0925	none	none	0
73	South Africa	2,024	777.2	87	0	16	0.591	43	motor	0	-1.056	0	1	0.088	identity_fraud	manual_review	479.53
74	South Africa	2,024	3,893.75	84	1	5	0.63	43	property	0	0.498	0	0	0.1472	none	none	0
75	South Africa	2,024	3,113.34	29	1	4	0.762	41	life	0	0.109	0	0	0.162	none	none	0
76	South Africa	2,024	4,319.07	103	3	4	0.859	33	property	0	0.71	0	0	0.1513	none	none	0
77	South Africa	2,024	1,860.04	102	0	18	0.618	26	property	0	-0.516	0	1	0.0842	phantom_claim	cross_validation	932.17
78	South Africa	2,024	3,148.79	73	5	11	0.679	36	life	0	0.126	0	0	0.1889	none	none	0
79	South Africa	2,024	4,176.3	36	1	3	0.827	41	health	1	0.639	0	0	0.1847	none	none	0
80	South Africa	2,024	2,119.44	43	3	13	0.75	50	motor	1	-0.387	0	0	0.1475	none	none	0
81	South Africa	2,024	3,804.91	53	3	2	0.721	36	health	0	0.454	0	0	0.1769	none	none	0
82	South Africa	2,024	1,343.76	23	2	21	0.942	24	health	0	-0.774	0	1	0.1086	staged_accident	manual_review	216.24
83	South Africa	2,024	2,001.28	37	2	7	0.559	26	motor	0	-0.446	0	0	0.1155	none	none	0
84	South Africa	2,024	1,960.18	17	2	6	0.731	23	life	0	-0.466	0	0	0.1221	none	none	0
85	South Africa	2,024	169.15	19	2	2	0.758	37	property	0	-1.359	1	1	0.1026	provider_fraud	ml_model	76.64
86	South Africa	2,024	3,871.34	90	1	28	0.713	21	property	0	0.487	0	0	0.1181	none	none	0
87	South Africa	2,024	1,767.31	16	3	1	0.759	28	health	0	-0.562	0	0	0.1415	none	none	0
88	South Africa	2,024	2,827.49	57	0	14	0.803	57	property	0	-0.034	0	0	0.1284	none	none	0
89	South Africa	2,024	3,857.64	52	1	4	0.753	36	property	0	0.48	0	1	0.158	collusion	manual_review	1,966.97
90	South Africa	2,024	3,782.37	6	1	15	0.195	39	motor	0	0.442	0	0	0.1936	none	none	0
91	South Africa	2,024	4,263.85	100	0	4	0.879	22	motor	0	0.682	0	0	0.1214	none	none	0
92	South Africa	2,024	2,583.33	119	1	23	0.663	37	health	0	-0.156	0	0	0.0971	none	none	0
93	South Africa	2,024	1,868.74	77	3	4	0.728	18	property	0	-0.512	0	0	0.1007	none	none	0
94	South Africa	2,024	2,624.62	81	2	2	0.513	39	property	0	-0.135	0	0	0.1179	none	none	0
95	South Africa	2,024	50	28	0	2	0.793	23	property	0	-1.419	0	0	0.0851	none	none	0
96	South Africa	2,024	50	102	1	3	0.877	53	health	0	-1.419	0	0	0.0688	none	none	0
97	South Africa	2,024	50	95	1	10	0.628	61	health	1	-1.419	0	0	0.066	none	none	0
98	South Africa	2,024	606.06	53	0	28	0.631	30	motor	0	-1.141	1	0	0.0887	none	none	0
99	South Africa	2,024	3,679.5	85	0	0	0.866	19	travel	0	0.391	0	0	0.115	none	none	0
100	South Africa	2,024	807.95	115	1	28	0.735	32	motor	0	-1.041	0	0	0.0703	none	none	0

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⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

African Insurance Fraud Detection

Synthetic insurance claims dataset for fraud detection across 12 Sub-Saharan African countries, covering three operational scenarios.

Variables

Variable	Type	Description
`record_id`	int	Unique row identifier
`country`	str	Country name
`year`	int	Reporting year
`claim_amount_usd`	float	Claim value in USD
`policy_duration_months`	int	Months since policy inception
`previous_claims_count`	int	Prior claims on the policy
`reporting_delay_days`	int	Days between incident and report
`document_completeness_score`	float	Completeness of submitted docs (0–1)
`claimant_age`	int	Age of claimant (18–85)
`claim_type`	str	motor, health, property, life, travel, crop
`seasonality_flag`	int	1 if festive-season month (Nov–Jan)
`amount_deviation_zscore`	float	Z-score of claim amount vs country mean
`duplicate_claim_flag`	int	Potential duplicate claim detected
`fraud_label`	int	1 = fraudulent, 0 = legitimate
`fraud_probability`	float	Modelled probability of fraud (0–1)
`fraud_scheme_type`	str	Scheme category (or "none")
`detection_method`	str	How fraud was identified (or "none")
`recovery_amount_usd`	float	Amount recovered from fraudster

Scenarios

Scenario	Fraud rate	Description
`baseline`	~12 %	Typical operational conditions
`enhanced_detection`	~12 %	Same fraud rate, higher detection power
`fraud_wave`	~22 %	Elevated fraud across all countries

Files

data/baseline.csv
data/enhanced_detection.csv
data/fraud_wave.csv

Intended Use

Training and benchmarking tabular classification models for insurance fraud detection in African markets. Suitable for gradient-boosted trees, logistic regression, and neural network approaches.

Limitations

Synthetic data with simplified correlations; does not capture real-world inter-claim dependencies, organised fraud networks, or temporal dynamics beyond a single year.

License

CC-BY-4.0

Downloads last month: 73

12 countries:

South Africa, Nigeria, Kenya, Ghana, Tanzania, Uganda, Rwanda, Ethiopia, Senegal, Côte d'Ivoire, Zambia, Mozambique

3 scenarios:

22 % fraud)

360 000 total records

(10 000 per country × 12 countries × 3 scenarios)