Enhancing Financial Predictions with LSTM and LIME: A Cognitive Decision-Making Model Approach

This study combines the Long Short-Term Memory (LSTM) network with Local Interpretable Model-agnostic Explanation (LIME) to enhance both predictive accuracy and interpretability in financial risk assessment, specifically loan default prediction. For instance, considering the ability of LSTM neural networks to capture temporal dynamics and long-range dependencies in data, they can excel with tasks such as credit risk prediction in Peer-to-Peer (P2P) lending platforms. Nonetheless, a common criticism of LSTMs is that they are too much like a “black box” because it can be hard to interpret how the model came up with an output. This is limited to a black box for selfinterpretation and by means of LIME we tackle this providing insights into the model decision by highlighting key features like loan amount, interest rate and annual income. The LSTM model was also successful with a training accuracy of 0.89 and testing accuracy of 0.85, ensuring precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F 1$</tex>-scores which closely corresponded to each other. LIME was employed to clarify individual predictions and improve trust and transparency in AIdriven financial decision-making. A hybrid method that employs the prediction strength of LSTM with the interpretability of LIME provides a more transparent and interpretable tool to financial institutions in assessing loan default risks. In addition, the conclusions drawn in this study may be quite helpful for enhancing credit risk models and adoption processes of financial sector. This in turn lends credence to the pragmatic portability of this methodology across other financial products, like credit cards and mortgages. Finally, we discuss the potential for real-time deployment, future research directions, and limitations, including computational complexity and dataset specificity.