Applying a legal provision frequently requires both structured rule-based reasoning and sensitivity to open-textured terms developed in case law. The actio pauliana of Article 42 of the Dutch Bankruptcy Act (Faillissementswet, Fw), which allows an insolvency practitioner to avoid transactions that prejudice creditors, requires both. We present a neuro-symbolic model of Article 42 Fw. Its symbolic core is written in Catala in a literate style that keeps statute and formalisation aligned and was developed through pair programming, with open-textured terms represented as boolean inputs where neural components couple to the symbolic solver. A neural routine structures a case's natural-language facts into boolean and date inputs. Neural subroutines resolve the open-textured terms through case-based reasoning over Dutch civil judgments, retrieved through a pipeline that filters on the requirement being resolved and expands along the procedural chain of a case. Segmenting and labelling those judgments attains high precision and recall against a manually annotated gold standard of 100 judgments. On ten hand-annotated cases the neuro-symbolic model handles most criteria reasonably but struggles with the knowledge requirement. Retrieval depth shows no clear quantitative effect, although retrieved judgments provide explicit grounding in prior case law.
Figures & tables
Figure 1: Actio pauliana model based on Pannevis para. 7.2.1 [ 23 ] , translation based on Warendorf [ 32 ] .
Score
Score
Section
P
R
F1
n
Section
P
R
F1
n
Course of proceedings
1.00
0.99
0.99
100
Established facts
1.00
0.97
0.99
76
Dispute
0.98
0.94
0.96
63
Grounds of decision
0.95
0.92
0.94
114
Decision
0.99
0.99
0.99
101
Other
0.98
1.00
0.99
128
Micro average (all)
0.98
0.97
0.98
582
Table 1: Section segmentation performance on the Rechtspraak corpus, reporting precision, recall, and F1 per section against 100 manually segmented judgments.
mistral-medium-3.5-128b:fp8
gpt-5.4
Element
P
R
F1
n
P
R
F1
n
Voluntary performance
0.69
0.97
0.81
39
0.85
0.90
0.88
39
Prejudice to creditors
0.89
0.91
0.90
34
1.00
0.82
0.90
34
Knowledge
0.86
0.92
0.89
39
0.97
0.92
0.95
39
Micro average
0.80
0.94
0.86
112
0.93
0.88
0.91
112
Table 2: Pre-retrieval annotation performance per element against 100 manually annotated judgments.
k=0
k=5
k=10
Model
P
R
F1
P
R
F1
P
R
F1
gemini-3.5-flash
0.64
0.88
0.74
0.67
0.90
0.77
0.63
0.88
0.74
gpt-5.5
0.68
0.90
0.77
0.67
0.90
0.77
0.67
0.90
0.77
mistral-medium-3.5-128b:fp8
0.66
0.82
0.73
0.65
0.82
0.72
0.66
0.82
0.73
Table 3: Neuro-symbolic model performance, reporting micro-averaged precision, recall, and F1 of the boolean criteria and the final verdict over ten judgments against the gold standard, at retrieval depths k=0 , k=5 , and k=10 .
k=0
k=5
k=10
Model
Subroutine
P
R
F1
P
R
F1
P
R
F1
gemini-3.5-flash
Voluntary
0.78
0.88
0.82
0.88
0.88
0.88
0.78
0.88
0.82
Prejudice
0.67
1.00
0.80
0.60
1.00
0.75
0.67
1.00
0.80
Knowledge
0.10
0.40
0.16
0.16
0.60
0.25
0.10
0.40
0.16
gpt-5.5
Voluntary
0.88
0.88
0.88
0.88
0.88
0.88
0.88
0.88
0.88
Prejudice
0.67
1.00
0.80
0.75
1.00
0.86
0.75
1.00
0.86
Table 4: Per-subroutine micro-averaged precision, recall, and F1 at retrieval depths k=0 , k=5 , and k=10 .
Model
k=0
k=5
k=10
gemini-3.5-flash
7
7
6
gpt-5.5
7
7
7
mistral-medium-3.5-128b:fp8
6
6
6
Table 5: Correct final verdicts (avoidable under Article 42 Fw) out of ten gold-standard cases, by model and retrieval depth.