# Explaining Deep Learning Models for Tabular Data Using Layer-Wise Relevance Propagation
## 2 Related Work
Due to regulatory reasons and ease of explainability, interpretability, and transparency many institutions use traditional approaches such as decision trees, random forest, regression, and Generalized Additive Model (GAM), at a cost of lower performance. However, there are examples of DL models that have been applied in financial applications e.g., for forecasting prices, stock, risk assessment, and insurance. Taking specific model examples, GAMs are relatively easy and transparent to understand and are used for risk assessments in financial applications [43; 44; 45]. The authors in [46] use traditional XGboost and Logistic Regression (LR), with LR principally used for comparison purposes. After training the model, the Shapley values [33] from the testing set of the companies are calculated. The testing set contains explanatory variables values. They also use a post-processing phase correlation matrix to interpret the predictive output from a good ML model that provides both accuracy and explainability. [PERSON] et al. [9] proposed a DL model to predict stock prices. In the first step of this work, a specific model was used to reduce the noise and make the data clean for LSTM. This system showed good results for predicting stock prices through price rate of change. In [47], a decision support system from financial disclosures is proposed. It uses a deep LSTM model to predict whether the stock is going up or down. The authors have also focused on answering whether the DL model can give good results on short-term price movement compared to the traditional approach of the bag of words with logistic regression, SVM, etc. The results show that DL based systems, as well as transfer learning and word-embeddings, improve performance compared to naive models. Whilst the performance of these models is not very high, the approach gives a baseline for future research to using DL in financial data. In [48], an AI-based stock market prediction model for financial trade called CLEAR-Trade is proposed, which is based on CLEAR (Class Enhanced Attentive Response). It identifies the regions with high importance/activations and their impact on the decision as well as the categories that are highly related to the important activations. The objective is to visualise the decisions for better interpretability. The results on using S&P 500 Stock Index data show that the model can give helpful information about the decision made which can help a company while adopting AI-based systems for addressing requirements from regulatory authorities. Their model uses a CNN architecture with a convolution layer, leaky ReLu, and Global average pooling layer, followed by the SoftMax layer to classify into two categories i.e., the market going up or down. The visualisation shows that in the correct cases, the model weighs the past 4 days of data heavily, whereas in the incorrect cases, it considers data from previous weeks as important. Secondly, in the correct decisions, it considers open, high, and low values for making a decision. Whereas in the incorrect cases, the model considers trade volumes but it is not a strong indicator of correctly predicting the model future. Thirdly, it can/may show that in the correct cases, the probability or output values are high compared to when the model incorrectly predicts. **XAI in Text data:** DL has shown good performance over text data for application areas such as text classification, text generation, natural language processing for chat-dots, etc. Similar to vision, financial, and time-series data, several works have been done on text data to explain what and how the text is classified or sentence is generated [36; 49]. A bi-LSTM is used to classify each sentence in five classes of sentiment. LRP is used to visualise the important word in the sentence. The LRP relevance values are being examined qualitatively and quantitatively. It showed better results than a gradient-based approach.