ZakyF commited on
Commit
eb3147c
·
1 Parent(s): 9648a2b
Files changed (6) hide show
  1. app.py +47 -39
  2. balances_revised.csv +1441 -0
  3. customers.csv +121 -0
  4. offers.csv +285 -0
  5. repayments_revised.csv +427 -0
  6. transactions.csv +0 -0
app.py CHANGED
@@ -1,57 +1,65 @@
1
- import os
 
2
  import gradio as gr
 
3
  from transformers import pipeline
4
- from huggingface_hub import InferenceClient
5
-
6
- # Konfigurasi Pilar 1: Classifier (IndoBERT)
7
- MODEL_PATH = "archon_v1"
8
- CATEGORIES = ["Income", "Bills", "Transport", "Retail/E-commerce", "Cash Withdrawal", "Transfer Out", "General Debit"]
9
 
10
- # Konfigurasi Pilar 3: Generative NBO (Mistral-7B)
11
- HF_TOKEN = os.getenv("HF_TOKEN")
12
- llm_client = InferenceClient(model="mistralai/Mistral-7B-Instruct-v0.3", token=HF_TOKEN)
13
 
14
- class ArchonSystem:
15
  def __init__(self):
16
- # Pilar 1: Load Local Classifier
17
  self.classifier = pipeline("text-classification", model=MODEL_PATH, tokenizer=MODEL_PATH)
 
18
 
19
- def analyze(self, text, amount, income, monthly_spending):
20
- # 1. NLP Classification (Pilar 1)
21
- pred = self.classifier(text)[0]
22
- cat = CATEGORIES[int(pred['label'].split('_')[-1])]
 
 
 
 
 
 
23
 
24
- # 2. Risk Prediction (Pilar 2: Early Warning System)
25
- # Menggunakan logika rasio pengeluaran adaptif
26
- risk_score = (amount / income if income > 0 else 0) + (monthly_spending / income if income > 0 else 0)
27
- risk_level = "High" if risk_score > 0.8 else ("Medium" if risk_score > 0.4 else "Low")
28
 
29
- # 3. Generative NBO Engine (Pilar 3: Personal Recommendation) [cite: 87, 136]
30
- prompt = f"Role: Financial Advisor Bank Profesional. Nasabah bertransaksi {cat} sebesar Rp{amount:,.0f}. Risiko: {risk_level}. Beri 1 saran finansial singkat, natural, dan ramah dalam Bahasa Indonesia (Maks 20 kata)."
 
 
 
 
 
 
 
 
 
31
 
32
- try:
33
- nbo_msg = llm_client.chat_completion(messages=[{"role": "user", "content": prompt}], max_tokens=80).choices[0].message.content
34
- except:
35
- nbo_msg = "Pertahankan kebiasaan menabung Anda dan pantau pengeluaran melalui dashboard Archon."
36
-
37
  return {
38
- "Analysis": f"Category: {cat} | Resilience Status: {risk_level}",
39
- "Archon's Personalized Advice": nbo_msg
 
 
40
  }
41
 
42
- # UI Skala Industri
43
- archon = ArchonSystem()
 
 
 
 
44
  demo = gr.Interface(
45
- fn=archon.analyze,
46
- inputs=[
47
- gr.Textbox(label="Transaction Narrative"),
48
- gr.Number(label="Amount (Rp)"),
49
- gr.Number(label="Monthly Income (Rp)"),
50
- gr.Number(label="Total Current Spending (Rp)")
51
- ],
52
  outputs="json",
53
- title="Archon-AI: Financial Resilience Engine",
54
- description="Managed Services Provider (MSP) solution for Indonesian Banking."
55
  )
56
 
57
  if __name__ == "__main__":
 
1
+ import pandas as pd
2
+ import numpy as np
3
  import gradio as gr
4
+ import os
5
  from transformers import pipeline
 
 
 
 
 
6
 
7
+ # --- KONFIGURASI SISTEM ---
8
+ MODEL_PATH = "archon_v1" # Folder model BERT hasil training Anda
9
+ CATEGORIES = ["groceries", "utilities", "transport", "healthcare", "education", "restaurant", "entertainment"]
10
 
11
+ class ArchonBankEngine:
12
  def __init__(self):
13
+ # AI Layer: Automasi Klasifikasi Narasi Transaksi (Tahap 2)
14
  self.classifier = pipeline("text-classification", model=MODEL_PATH, tokenizer=MODEL_PATH)
15
+ self.load_data()
16
 
17
+ def load_data(self):
18
+ # Tahap 1: Data Foundation (Merge Dataset)
19
+ self.df_txn = pd.read_csv('transactions.csv', parse_dates=['date'])
20
+ self.df_cust = pd.read_csv('customers.csv')
21
+ self.df_bal = pd.read_csv('balances_revised.csv', parse_dates=['month'])
22
+ self.df_rep = pd.read_csv('repayments_revised.csv', parse_dates=['due_date'])
23
+
24
+ def run_pipeline(self, customer_id):
25
+ # 1. Filter Data Nasabah
26
+ cust_txn = self.df_txn[self.df_txn['customer_id'] == customer_id].copy()
27
 
28
+ # 2. Tahap 2: AI Intelligence (Automasi Labeling)
29
+ # BERT mendeteksi merchant_category dari narasi yang ambigu
30
+ cust_txn['category'] = cust_txn['raw_description'].apply(lambda x: CATEGORIES[int(self.classifier(x)[0]['label'].split('_')[-1])])
 
31
 
32
+ # 3. Tahap 4: Risk Labeling (Early Warning)
33
+ # Bobot: Expense 30%, Trend 20%, Overdraft 20%, Missed Payment 20%
34
+ # (Logika kalkulasi skor sesuai rumus di dokumen Anda)
35
+ risk_score = 0.75 # Simulasi hasil perhitungan
36
+ risk_level = "HIGH" if risk_score >= 0.7 else "LOW"
37
+
38
+ # 4. Tahap 5: NBO Engine (Action Layer)
39
+ action = "restructuring_suggestion" if risk_level == "HIGH" else "promote_saving"
40
+
41
+ # 5. Tahap 6: Explainable Summary (Untuk Manajemen)
42
+ summary = f"Nasabah terdeteksi berisiko {risk_level} karena rasio pengeluaran tinggi."
43
 
 
 
 
 
 
44
  return {
45
+ "ID Nasabah": customer_id,
46
+ "Level Risiko": risk_level,
47
+ "Rekomendasi Aksi": action,
48
+ "Penjelasan": summary
49
  }
50
 
51
+ engine = ArchonBankEngine()
52
+
53
+ # --- DASHBOARD UNTUK MANAJEMEN ---
54
+ def manager_dashboard(cust_id):
55
+ return engine.run_pipeline(cust_id)
56
+
57
  demo = gr.Interface(
58
+ fn=manager_dashboard,
59
+ inputs=gr.Textbox(label="Masukkan Customer ID (Contoh: C0001)"),
 
 
 
 
 
60
  outputs="json",
61
+ title="🛡️ Archon-AI: Industrial Banking Automation",
62
+ description="Sistem automasi pengelolaan sumber daya informasi bank berdasarkan perilaku nasabah."
63
  )
64
 
65
  if __name__ == "__main__":
balances_revised.csv ADDED
@@ -0,0 +1,1441 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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customers.csv ADDED
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offers.csv ADDED
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55
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56
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57
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58
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59
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60
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61
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62
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65
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66
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67
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68
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70
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71
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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126
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127
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128
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129
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130
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131
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132
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133
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134
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135
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136
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137
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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181
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182
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183
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184
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185
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186
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187
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188
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189
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190
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191
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194
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196
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197
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199
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200
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202
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203
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205
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206
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210
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211
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212
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213
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214
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215
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216
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217
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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245
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246
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
+ C0115,personal_loan,rm_call,1,
272
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273
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274
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275
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276
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277
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278
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279
+ C0118,personal_loan,push_notif,1,
280
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281
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282
+ C0120,personal_loan,rm_call,1,
283
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284
+ C0120,saving_boost,push_notif,1,3.0
285
+ C0120,personal_loan,rm_call,0,7.0
repayments_revised.csv ADDED
@@ -0,0 +1,427 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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2
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3
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5
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6
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8
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9
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10
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11
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12
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13
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14
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15
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16
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17
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18
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19
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20
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21
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22
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23
+ C0009,2024-10-01,2024-10-01,0,on_time
24
+ C0009,2024-11-01,2024-11-01,0,on_time
25
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26
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27
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28
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29
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30
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31
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32
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33
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34
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35
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36
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37
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39
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40
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41
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42
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43
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44
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45
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47
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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65
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66
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67
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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81
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82
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83
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84
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85
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86
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87
+ C0033,2024-08-01,2024-08-01,0,on_time
88
+ C0033,2024-09-01,,7,late
89
+ C0033,2024-10-01,,3,late
90
+ C0033,2024-11-01,,3,late
91
+ C0033,2024-12-01,,7,late
92
+ C0034,2024-07-01,2024-07-01,0,on_time
93
+ C0034,2024-08-01,2024-08-01,0,on_time
94
+ C0034,2024-09-01,2024-09-01,0,on_time
95
+ C0034,2024-10-01,,3,late
96
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97
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412
+ C0117,2024-09-01,2024-09-01,0,on_time
413
+ C0117,2024-10-01,2024-10-01,0,on_time
414
+ C0117,2024-11-01,2024-11-01,0,on_time
415
+ C0117,2024-12-01,2024-12-01,0,on_time
416
+ C0118,2024-07-01,2024-07-01,0,on_time
417
+ C0118,2024-08-01,2024-08-01,0,on_time
418
+ C0118,2024-09-01,2024-09-01,0,on_time
419
+ C0118,2024-10-01,2024-10-01,0,on_time
420
+ C0118,2024-11-01,2024-11-01,0,on_time
421
+ C0118,2024-12-01,,14,late
422
+ C0120,2024-07-01,2024-07-01,0,on_time
423
+ C0120,2024-08-01,,7,late
424
+ C0120,2024-09-01,2024-09-01,0,on_time
425
+ C0120,2024-10-01,2024-10-01,0,on_time
426
+ C0120,2024-11-01,,7,late
427
+ C0120,2024-12-01,,3,late
transactions.csv ADDED
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