Deep learning for enhanced risk management: a novel approach to analyzing financial reports
Risk management is a critical component of today’s financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic mo...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2025-01-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2661.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832582498746368000 |
|---|---|
| author | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang |
| author_facet | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang |
| author_sort | Xiangting Shi |
| collection | DOAJ |
| description | Risk management is a critical component of today’s financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic models are inadequate to address the complex structures of the latest data. This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. A combination of quantitative and qualitative ratings derived from the analysis of financial texts results in high accuracy and stability compared with the HFRP model. Evaluating key findings, the quantity of training & testing loss decreased considerably and they have their final value as 0.0013 and 0.003, respectively. According to the hypothesis, the selected HFRP model demonstrates the values of the revenue, net income, and earnings per share (EPS), and are closely similar to the actual values. The model achieves substantial risk mitigation: credit risk lowered from 0.75 to 0.20, liquidity risk from 0.70 to 0.25, market risk from 0.65 to 0.30, while operational risk is at 0.80 to 0.35. By analyzing the results of the HFRP model, it can be stated that the proposal promotes improved financial stability and presents a reliable model for the contemporary financial markets, which in turn helps in making sound decisions and improve the assessment of risks. |
| format | Article |
| id | doaj-art-212216f5e2824d7fb9eb65f76b9b8f9b |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-212216f5e2824d7fb9eb65f76b9b8f9b2025-01-29T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e266110.7717/peerj-cs.2661Deep learning for enhanced risk management: a novel approach to analyzing financial reportsXiangting Shi0Yakang Zhang1Manning Yu2Lihao Zhang3Industrial Engineering and Operations Research Department, Columbia University, New York, United StatesIndustrial Engineering and Operations Research Department, Columbia University, New York, United StatesDepartment of Statistics, Amsterdam Avenue New York, Columbia University, New York, United StatesDepartment of Information Engineering, Chinese University of Hong Kong, Ho Sin Hang Engineering Building, Hong KongRisk management is a critical component of today’s financial environment because of the enormity and complexity of data contained in financial statements. Business situations, plans, and schedule risk assessment with the help of conventional ways which involve analytical, technical, and heuristic models are inadequate to address the complex structures of the latest data. This research brings out the Hybrid Financial Risk Predictor (HFRP) model, using the convolutional neural networks (CNN) and long-short term memory (LSTM) networks to improve financial risk prediction. A combination of quantitative and qualitative ratings derived from the analysis of financial texts results in high accuracy and stability compared with the HFRP model. Evaluating key findings, the quantity of training & testing loss decreased considerably and they have their final value as 0.0013 and 0.003, respectively. According to the hypothesis, the selected HFRP model demonstrates the values of the revenue, net income, and earnings per share (EPS), and are closely similar to the actual values. The model achieves substantial risk mitigation: credit risk lowered from 0.75 to 0.20, liquidity risk from 0.70 to 0.25, market risk from 0.65 to 0.30, while operational risk is at 0.80 to 0.35. By analyzing the results of the HFRP model, it can be stated that the proposal promotes improved financial stability and presents a reliable model for the contemporary financial markets, which in turn helps in making sound decisions and improve the assessment of risks.https://peerj.com/articles/cs-2661.pdfDeep learningRisk managementFinancial analysisNeural networksNatural language processing |
| spellingShingle | Xiangting Shi Yakang Zhang Manning Yu Lihao Zhang Deep learning for enhanced risk management: a novel approach to analyzing financial reports PeerJ Computer Science Deep learning Risk management Financial analysis Neural networks Natural language processing |
| title | Deep learning for enhanced risk management: a novel approach to analyzing financial reports |
| title_full | Deep learning for enhanced risk management: a novel approach to analyzing financial reports |
| title_fullStr | Deep learning for enhanced risk management: a novel approach to analyzing financial reports |
| title_full_unstemmed | Deep learning for enhanced risk management: a novel approach to analyzing financial reports |
| title_short | Deep learning for enhanced risk management: a novel approach to analyzing financial reports |
| title_sort | deep learning for enhanced risk management a novel approach to analyzing financial reports |
| topic | Deep learning Risk management Financial analysis Neural networks Natural language processing |
| url | https://peerj.com/articles/cs-2661.pdf |
| work_keys_str_mv | AT xiangtingshi deeplearningforenhancedriskmanagementanovelapproachtoanalyzingfinancialreports AT yakangzhang deeplearningforenhancedriskmanagementanovelapproachtoanalyzingfinancialreports AT manningyu deeplearningforenhancedriskmanagementanovelapproachtoanalyzingfinancialreports AT lihaozhang deeplearningforenhancedriskmanagementanovelapproachtoanalyzingfinancialreports |