A Survey of Research on Data Analytics-Based Legal Tech

Empirical mode decomposition using deep learning model for financial market forecasting

Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, optimized using PSO, and predicted. Combining the nonlinear and non-stationary financial time series can improve prediction accuracy. The predictive model of deep learning, based on the analysis of the massive financial trading data, can forecast the future trend of financial market price, forming a trading signal when particular confidence is satisfied. The empirical results show that the EMD-based deep learning model has an excellent predicting performance.

Evaluating Human versus Machine Learning Performance in a LegalTech Problem

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process.