A Novel Framework for Commercial Loan Pricing and Risk Assessment Using Systemic Time Elasticity, Temporal Liquidity Distortion, and Recursive Economic Resilience

Abstract

Traditional loan-pricing frameworks assume linear risk and stable markets and fail to capture shifts in borrower resilience during volatility. This study introduces three corrective models:

•     STEM forecasts the number of months until a borrower’s cash flow exceeds debt obligations using operational data and sector volatility, enabling lenders to preempt distress with timely term adjustments.

•     TLDM revalues loans hourly, adapting to shifts in borrower liquidity and market funding costs, ensuring that pricing reflects real-time market dynamics.

•     The RERF scores survival odds after repeated stress events— such as covenant breaches or rate spikes—by weighing leverage, reserves, and distress history, guiding banks to allocate capital efficiently.

Tested on 14 historical corporate loans, these models reduce default misclassification by 32% and improve valuation accuracy by 26% over RAROC and IRB. They align with Basel III by improving risk-weighted asset classification and with IFRS 9 by refining forward-looking provisioning and impairment staging.

To operationalize the models, we integrated advanced machine learning. XGBoost reduced the parameter calibration error by 15%, improving the STEM forecast accuracy by 10%. LSTM networks identified borrower distress 20% earlier, cutting false negatives in RERF by 12%, and reducing TLDM's response lag during liquidity shocks. The SHAP explanations ensured regulatory transparency and auditability.

Each model has defined limitations. STEM overestimates recovery timelines for pre-revenue firms by up to six months. TLDM underreacts to liquidity events when data lags and missing cash flow breaks by 12–24 hours. The RERF underweights systemic tail risk, missing 15% of correlated losses in the 2008-style simulations.

These limitations guide future research. STEM should be tested on gig-economy cash flows to improve early stage borrower modeling. The RERF can be adapted for crypto-backed volatility. The TLDM can integrate geopolitical risk signals for real-time reactivity in cross-border lending.

Beyond banks, these models can inform regulatory stress-testing standards, enabling supervisors to better identify systemic vulnerabilities and foster industry-wide resilience. With these advancements, STEM, TLDM, and RERF usher in a new generation of lending modelsbuilt to anticipate crises and price risk with adaptive precision.