tabular-decision-transformer-finance

Model Overview

The tabular-decision-transformer-finance model is a specialized Decision Transformer adapted for classifying structured, tabular data (often seen in financial risk assessment). Instead of predicting actions in a sequence, this model interprets the feature vector (sequence of normalized features) and classifies the overall outcome (e.g., loan risk). It leverages the power of the attention mechanism to capture complex, non-linear interactions between features, outperforming traditional tree-based models on complex, high-dimensional datasets.

Model Architecture

• Base Architecture: Decision Transformer (DecisionTransformerForSequenceClassification).
• Mechanism: The model treats the ordered features of a tabular row (e.g., [Age, Income, Credit Score, ...]) as a sequence of tokens. The Transformer Encoder processes this feature sequence, applying self-attention to understand the relative importance and correlation of different features to the final outcome.
• Output: The output of the final Transformer layer is passed through a simple linear classification head to predict one of three risk categories: High Risk, Medium Risk, or Low Risk.
• Input Features (Example Domain: Loan Risk): Age, Annual Income, Credit Score, Loan Amount, Default History (Categorical).

Intended Use

• Financial Risk Assessment: Classifying the risk level of a loan applicant, insurance claim, or investment.
• Structured Data Prediction: Providing highly accurate predictions on datasets where feature interactions are complex (e.g., predicting customer churn, equipment failure).
• Feature Importance Analysis: The internal attention weights can be used to provide interpretability on which features drove the classification decision.

Limitations and Ethical Considerations

• Interpretability: While attention weights offer some insight, the overall black-box nature of the Transformer is harder to explain to regulators than a simple decision tree. This is a critical limitation in highly regulated sectors like finance.
• Feature Ordering: Performance is sensitive to the order in which features are presented (encoded as the sequence). Features must be consistently ordered.
• Bias: Inherits and amplifies biases present in the training data, potentially leading to unfair or discriminatory risk assessments based on proxies for sensitive attributes (e.g., income as a proxy for socioeconomic status). Rigorous bias testing is required.
• Input Normalization: Requires all continuous features to be properly normalized or standardized before being passed as input.

Example Code

To classify a new financial profile:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Conceptual loading (A tokenizer is often a custom feature processor here)
model_name = "YourOrg/tabular-decision-transformer-finance"
# Assuming a custom tokenizer/processor handles feature -> tensor conversion
# tokenizer = TabularFeatureTokenizer.from_pretrained(model_name) 
# model = DecisionTransformerForSequenceClassification.from_pretrained(model_name)

# --- Conceptual Input Data ---
# A new applicant's features, normalized and ordered:
raw_features = [35, 75000, 720, 15000, 1] # Age, Income, Score, Amount, History (1=Yes)

# Input preparation (conceptual: features are tokenized/embedded into a tensor)
# In reality, this requires a specific processor to handle categorical/continuous embeddings.
input_tensor = torch.tensor([raw_features], dtype=torch.float32) 

# --- Conceptual Prediction ---
# with torch.no_grad():
#     outputs = model(input_tensor)
#     logits = outputs.logits
#     predicted_class_id = torch.argmax(logits, dim=1).item()

# Predicted class mapping (based on config.json)
predicted_class_id = 0 # Example result
label_map = {0: "High Risk", 1: "Medium Risk", 2: "Low Risk"}
prediction = label_map[predicted_class_id]

print(f"Input Profile: Age 35, Score 720, Amount $15k")
print(f"Predicted Loan Risk: **{prediction}**")