Editing AI in Finance (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Credit risk model with SHAP explanations for regulatory compliance:'''
<syntaxhighlight lang="python">
import lightgbm as lgb
import shap
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

# Features: income, debt_ratio, payment_history, account_age, etc.
df = pd.read_csv("credit_data.csv")
X = df.drop("default", axis=1)
y = df["default"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)

model = lgb.LGBMClassifier(
    n_estimators=500, max_depth=6, learning_rate=0.05,
    scale_pos_weight=(y==0).sum()/(y==1).sum(),  # Handle class imbalance
    class_weight='balanced'
)
model.fit(X_train, y_train, eval_set=[(X_test, y_test)],
          callbacks=[lgb.early_stopping(50)])
print(f"AUC: {roc_auc_score(y_test, model.predict_proba(X_test)[:,1]):.4f}")

# SHAP for regulatory explainability
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)[1]  # Class 1 (default)

# Per-applicant explanation for adverse action notice
def get_adverse_reasons(applicant_idx, top_n=3):
    vals = shap_values[applicant_idx]
    negative_factors = sorted(
        [(X_test.columns[i], vals[i]) for i in range(len(vals)) if vals[i] > 0],
        key=lambda x: -x[1]
    )
    return negative_factors[:top_n]
</syntaxhighlight>

; AI applications in finance by domain
: '''Fraud detection''' → Gradient boosting (LightGBM/XGBoost), GNN for transaction networks
: '''Credit scoring''' → Logistic regression + SHAP (regulatory), LightGBM for performance
: '''Algorithmic trading''' → LSTM, transformers for price prediction; RL for execution
: '''Portfolio optimization''' → Mean-variance + ML factor models, deep RL
: '''Document processing''' → LLMs for earnings call analysis, contract review, regulatory filings
: '''Customer service''' → LLM-powered chatbots for banking queries, complaint triage
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