51 research outputs found
Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model
With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models
Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model
With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In the modern e-commerce, the behaviors of customers contain rich
information, e.g., consumption habits, the dynamics of preferences. Recently,
session-based recommendations are becoming popular to explore the temporal
characteristics of customers' interactive behaviors. However, existing works
mainly exploit the short-term behaviors without fully taking the customers'
long-term stable preferences and evolutions into account. In this paper, we
propose a novel Behavior-Intensive Neural Network (BINN) for next-item
recommendation by incorporating both users' historical stable preferences and
present consumption motivations. Specifically, BINN contains two main
components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning.
Firstly, a novel item embedding method based on user interactions is developed
for obtaining an unified representation for each item. Then, with the embedded
items and the interactive behaviors over item sequences, BINN discriminatively
learns the historical preferences and present motivations of the target users.
Thus, BINN could better perform recommendations of the next items for the
target users. Finally, for evaluating the performances of BINN, we conduct
extensive experiments on two real-world datasets, i.e., Tianchi and JD. The
experimental results clearly demonstrate the effectiveness of BINN compared
with several state-of-the-art methods.Comment: 10 pages, 7 figures, KDD 201
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams
Knowing which factors are significant in credit rating assignment leads to
better decision-making. However, the focus of the literature thus far has been
mostly on structured data, and fewer studies have addressed unstructured or
multi-modal datasets. In this paper, we present an analysis of the most
effective architectures for the fusion of deep learning models for the
prediction of company credit rating classes, by using structured and
unstructured datasets of different types. In these models, we tested different
combinations of fusion strategies with different deep learning models,
including CNN, LSTM, GRU, and BERT. We studied data fusion strategies in terms
of level (including early and intermediate fusion) and techniques (including
concatenation and cross-attention). Our results show that a CNN-based
multi-modal model with two fusion strategies outperformed other multi-modal
techniques. In addition, by comparing simple architectures with more complex
ones, we found that more sophisticated deep learning models do not necessarily
produce the highest performance; however, if attention-based models are
producing the best results, cross-attention is necessary as a fusion strategy.
Finally, our comparison of rating agencies on short-, medium-, and long-term
performance shows that Moody's credit ratings outperform those of other
agencies like Standard & Poor's and Fitch Ratings
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods
Machine learning and deep learning have become increasingly prevalent in
financial prediction and forecasting tasks, offering advantages such as
enhanced customer experience, democratising financial services, improving
consumer protection, and enhancing risk management. However, these complex
models often lack transparency and interpretability, making them challenging to
use in sensitive domains like finance. This has led to the rise of eXplainable
Artificial Intelligence (XAI) methods aimed at creating models that are easily
understood by humans. Classical XAI methods, such as LIME and SHAP, have been
developed to provide explanations for complex models. While these methods have
made significant contributions, they also have limitations, including
computational complexity, inherent model bias, sensitivity to data sampling,
and challenges in dealing with feature dependence. In this context, this paper
explores good practices for deploying explainability in AI-based systems for
finance, emphasising the importance of data quality, audience-specific methods,
consideration of data properties, and the stability of explanations. These
practices aim to address the unique challenges and requirements of the
financial industry and guide the development of effective XAI tools.Comment: 11 pages, 1 figur
Tree-Based Approaches for Predicting Financial Performance
The lending industry commonly relied on assessing borrowers’ repayment performance to make lending decisions. This is to safeguard their assets and maintain their profitability. With the rise of Artificial Intelligence, lenders resorted to Machine Learning (ML) algorithms to solve this problem.
In this study, the novelty introduced is applying ML’s Tree-based methods to a large dataset and accurately predicting financial repayment performance without using any repayment history, which was utilized in all literature reviewed. Instead, the attributes used were demographics and psychographics of applicants, only. The study’s proprietary US-based dataset comprises an anonymous population whose owner does not wish to be disclosed and it contains the information of about half a million beneficiaries with a very balanced bimodal binary target distribution.
An Area Under the Curve of Receiver Characteristic Operator (ROC-AUC) of 85% was achieved with a binary classification target using CatBoost API. The study also experimented with a given tri-class target. Furthermore, this research used ML to gain insight into which attributes contribute the most to the repayment prediction. The study also tested whether similar results can be achieved with fewer attributes for the sake of the practicality of application by the data owner. The best model was applied to one of the biggest publicly available financial datasets for verification. The original research of said dataset had an accuracy score of 82%, this study achieved 79% using 5-fold Cross-Validation (CV). This result was achieved with Tree-Based models with a complexity of O(log n) compared to O(2n) in the original research, which is a significant efficiency enhancement
- …