# Deep Learning-Driven Financial Management Innovation Upgrade for Universities

Authors: Yu Guan; Chen Chen
Journal: Mobile Information Systems
 (2022)

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## Abstract

This study aims to improve the quality of college financial management and reduce the risk of college financial management, and a college financial system based on multiscale deep learning is designed in this paper. This paper designs a university financial system based on multiscale deep learning. In the hardware design, the system adds multiple sensors and scans all the information in the financial database using a coordinator. In the software design, the weights that can connect the financial information of the same attribute are set by establishing a database form; according to the multilayer perceptual network topology, a full interconnection model based on multiscale deep learning is designed to realize the system’s deep extraction of data. The experimental results show that the financial risk is based on the risk warning capability for university finance, and compared with the system under the traditional design, the university finance system designed at this time has the most categories of financial information parameters extracted.


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## Body

## 1. Introduction

Financial management of colleges and universities is an important part of public financial management; therefore, the quality of financial management work of colleges and universities will be directly related to the public financial security [1]. The application of modern science and universities and the continuous deepening of the reform supported by the Internet technology are specifically manifested in the following aspects [2–4].Firstly, it strengthens the construction of financial management information standard system of colleges and universities under the background of the Internet [5]. Given the complexity of financial management work of colleges and universities at the present stage, the financial management department of colleges and universities should systematically sort out its work content and establish the information standard system of college financial management, so as to significantly improve the quality of college financial management work [6–8].While optimizing the financial management path, we will also implement and improve the corresponding financial management mechanism [9, 10]. At present, the network service platform becomes an important support for the financial management work of colleges and universities. Based on the many advantages of the Internet, the financial management work of colleges and universities should be adjusted with reference to the information service platform, and it should optimize the management mechanism, simplify the management process, and improve the efficiency of the financial management information service platform [11].Taking advantage of Internet information transmission can effectively improve the quality of financial management information service [12]. For example, the management of the scientific research budget in colleges and universities needs to consider the use of fixed assets, labor cost expenditure, and other related funds, and in order to strengthen the management of the scientific research budget, it is necessary to rely on the information service platform, and the scientific researchers should report their demands through the financial management information service platform and financial management The financial data will be changed after approval by the financial management department, thus making the financial management mechanism more flexible and efficient [13].The financial management concept cannot keep up with the development of the current situation. (1) The financial management of colleges and universities lacks the necessary modern management concept, financial risk prevention consciousness, and cost management consciousness. At present, compared with enterprises, the financial management of colleges and universities in China not only has great shortcomings in the management content, means and methods, but also lacks the awareness of financial risk prevention and cost management in the management concept. Under the condition of the market economy, the connotation of college financial management is increasingly rich, the function is expanding, and college financial management personnel, if only stay in simple bookkeeping, accounting and other affairs, will not be able to cope with the complex economic situation and the rapid development of college needs. (2) The financial management system of universities is not smooth enough. Although in the early 1990s, China began to explore the establishment of a chief accountant system in colleges and universities, there are not many colleges and universities that established the position of chief accountants. At present, it is more and more difficult to adapt to the requirements of financial management in schools where “the president of the school (college) in charge of financial work acts as the chief accountant.” The head of the school (college) in charge of financial work should be very familiar with all aspects of teaching management in the school. However, due to the limitations of the profession, they are not familiar with the information on the fund operation of the whole university, the legal compliance of economic activities, and the efficiency effect and so on. Therefore, it is urgent for colleges and universities to set up proficient chief accountants in economic business to assist the president of colleges and universities to comprehensively lead the financial management of the university, improve the management mechanism of colleges and universities, strengthen the economic management of colleges and universities, and improve the efficiency and benefit of economic operation.Optimizing the financial risk management mechanism of colleges and universities and building financial risk models are important [14, 15]. The innovation of financial management of colleges and universities in the background of the Internet can use the network data for reasonable control of financial risks and establish a financial risk model, and with the increase of financial data of colleges and universities on the Internet, the prediction accuracy of the financial risk model will be higher, so as to ensure the orderly development of financial management of colleges and universities [16].


## 2. Related Work

Many scholars have made breakthroughs in the research of financial intelligence in recent years, and intelligent financial technology has been applied to some extent in large and medium-sized universities. From system construction to management changes, promising results have been achieved. Literature [17] proposed the realization path of the model, literature [18] proposed the realization and application of financial sharing based on a cloud platform and made a more in-depth study on tax management optimization, and literature [19] proposed corresponding countermeasures to the difficulties of financial data sharing. Tan Qing proposed the construction model of a dynamic accounting information platform. Literature [12] studies the financial analysis system of large universities and points out that business, financial, and management information systems within universities need to be organically integrated. Realizing the unification of bill processing is conducive to the centralization of work and personnel in colleges and universities. Literature [13] based on realizing the business-financial integration between marketing and finance departments through financial intelligence proposes an integrated control scheme to improve the efficiency of financial control and to improve the experience of business departments. Literature [14] explains the application of API technology to realize information interaction and settlement internally, with partners, and with open unspecified objects and demonstrates the great convenience of this technological advancement for the financial management of universities. Literature [15] examines the implementation of financial intelligence in the manufacturing industry from procurement, manufacturing, warehousing, and cost management and proposes a cost management platform based on target costs, using scenario simulation for cost adjustment until the target needs of cost management are met. It also makes flexible budgeting more feasible.In [16], more technical exploration of the evolution of financial technology upgrade from information technology to automation, intelligence, and digitalization is conducted. It is considered that financial management intelligence is a financial change driven by both information technology development and quantitative multiplication of university scale. Technological advancement is the external driving force, and the scale multiplication of universities is the internal motivation. Literature [17] analyzed the application scenarios of the more mature automation and intelligent technologies at present. It is pointed out that a good IT foundation is a necessary basis for AI implementation. It is considered that the current intelligent technology has been able to do more in the automatic processing of internal processes, automatic data exchange between universities and partners, automatic processing of accounts, intelligent audit, intelligent audit, supplier management, and risk prevention and control. Several intelligent scenarios and application processes are designed. The issues to be considered by universities in choosing RPA and the reference path for implementing RPA are proposed. It is also the fundamental criterion to evaluate the success of intelligence.Literature [20] conducted more research on the change of financial management under the intelligent scenario and believed that the focus of finance people will gradually shift to management, and control will become the core of financial management [18] and put forward the theory of control mechanism [19]. Combining the wisdom of finance people with artificial intelligence to achieve the integration of human-computer intelligence improves the unstructured data processing capability of AI. With the powerful computing ability of AI, it can simulate a variety of scenarios and input emotional parameters for scientific calculation, thus adjusting the influence of financiers and college decision makers due to financial psychological accounts and helping rational decisions made by colleges and universities. The financier assumes the role of a basic knowledge inputter of colleges and universities and promotes the evolution of financial intelligence, which is more in line with the personalized needs of colleges and universities. At the same time, the focus of finance people shifts from information collection to management control.Literature [19] studied the case of establishing the financial information system by combining financial outsourcing and a self-built financial center. It was pointed out that direct promotion from the top, adequate research and preparation in advance, and gradual implementation in phases according to the actual situation of each ministry are the guarantees of successful implementation. The establishment of the professional financial management subcenter, which is dedicated to promote, is the key to achieving the change in each business. In terms of talent, team building needs to be cultivated and introduced at the same time. Emphasis on the exchange of relevant departments is as important as providing professional training for continuous superior talent allocation and for establishing a dynamic financial organization system.This paper introduces the composition of the financial management risk system of colleges and universities in detail and proposes a scientific construction plan for the financial risk early warning system of colleges and universities according to the special characteristics of financial management work of colleges and universities. For example, the risk of fixed assets management of colleges and universities is mainly directly related to the frequency of use, curriculum arrangement setting, and scientific research project declaration. Taking relevant data as the influencing factor of the risk model of fixed assets management of colleges and universities, it can improve the quality of fixed assets management and financial risks caused by improper management of fixed assets. 3 Hardware design of the university financial system is based on multiscale deep learning. The main problems of the current system are as follows.The internal control system is not sound and becomes a mere formality. Budget management is not perfect. The standard of asset allocation is unreasonable. The overall quality of financial management personnel is not high, which restricts the improvement of financial management levels in colleges and universities, and there is lack of an effective cost accounting system. To meet the multiscale deep learning requirements of the university financial system, the system hardware needs to be redesigned so that the hardware meets the fully interconnected mode of the multilayer perceptron network under multiscale deep learning. It is known that the most critical hardware for the financial system to achieve the fully interconnected mode is the sensor and the coordinator. We can use the sensor to improve the transmission, processing, storage, display, recording, and control of electrical signals, and we can use the coordinator to scan the financial data in the system and select the appropriate parameters to build a multilayer perceptual network that can be used for multiscale deep learning. The block diagram of the hardware design of the financial system of the university is shown in Figure1.Figure 1
Hardware design framework of the financial system in higher education.According to Figure1, the coordinator ZigBee is connected to each node in the acquisition unit, processing unit, and control unit in order to obtain the parameters suitable for the whole network and to establish a network with complete coverage of the system. The newly selected sensor model is MGS-2483-H24, and the hardware is connected to the data collection unit for searching the financial data in the university financial system. According to the block diagram, one sensor is connected to each collection node to ensure that all collected data can be uploaded to the data processing center of the system. According to the hardware design block diagram shown in Figure 1, the hardware of the financial system is reconnected to realize the hardware design of the university financial system that satisfies multiscale deep learning.


## 3. Software Design of the University Financial System Based on Multiscale Deep Learning

### 3.1. Creating Database Form to Set Connection Weights

In the financial system of higher education, the financial information covered is of various types, complex contents and refined subjects may appear at the third or fourth levels, and the system may carry out multiscale deep learning by designing a database form with a sharp, clear objective and distinct type division and by setting a weight value to connect the same attribute information. The basic information about college users and college workers is shown in Table1 and Table 2.Table 1
Basic information of users.
Yuduan nameData typeLengthRemarksCruxEIDVarchar10Customer numberYesNameVarchar10Full nameNoTelVarchar10Contact informationYesDIDVarchar20Affiliated unitNoAreaVarchar50Place of residenceYesDNameVarchar10Unit typeNoTable 2
Basic information of employees.
Yuduan nameData typeLengthRemarksCruxEIDVarchar10Employee numberYesNameVarchar10Employee nameYesSexVarchar10GenderNoBirthdayDate10Date of birthNoHometownVarchar10Native placeNoAddressVarchar50Current residenceYesTelVarchar10Contact numberYesID_numberVarchar10ID numberYesDepartmentVarchar20DepartmentYesWith Table1 and Table 2 as the two major categories for financial information retrieval, the financial information of universities in terms of customers and employees is recorded and updated in real time. Set up information such as customer payment items, drug categories, and charge numbers is shown in Table 1; and set up information such as basic salary, job salary, seniority salary, employee benefits, incentive salary, and social insurance is displayed in Table 2. According to all the financial information of the university, we set the weights of the connected financial information, and the equation of the change of the weights is shown in the following equation:(1)Δqij=−μ∂D∂qij=−μ∂∂qij∑m=1mDm=∑m=1m−μ∂Dm∂qij,where qij denotes the connection weights between financial information; Δqij denotes the change of weights; i and j denote two random financial information; μ denotes the learning rate of the system; m denotes the same attribute of financial information; Dm denotes the global error value. According to the information of the established database form, the weights that can connect the financial information of universities are set.


### 3.2. Designing a Fully Interconnected Model Based on Multiscale Deep Learning

It is known that the perceptron has a single-layer computing capability and belongs to a kind of feed-forward network, and according to the set weights, bottom-up information transmission can be performed for each layer of the network. Therefore, according to this function of the perceptron, a multilayer perceptual network is constructed, and in this way, the full interconnection pattern of the system information is set; the connections between neurons in different layers are used to mine the financial information of the university.The perceptron is used as each node in the neural network, and a dynamic connection weight is set according to the result of equation (1), and then, the perceptron is used to learn this weight; the topology of the multilayer perceptual network is schematically shown in Figure 2.Figure 2
Topology of the multilayer sensing network.In Figure2, x denotes the random financial information; H denotes the input layer unit; hk denotes the hidden layer unit; K denotes the output layer unit; Y denotes the final result. According to Figure 2, neurons in the same layer are not connected to each other and neurons in two adjacent layers are fully connected to each other, and the data transmitted from the input to the output are calculated layer by layer through directional information transfer. The multilayer perceptual network setup contains not only input and output layers but also one or more hidden layers, allowing the system to extract financial data features with associated properties within the system during deep learning, which is the forward propagation system interconnection. In contrast, the reverse system interconnection of the perception network is designed using the backpropagation algorithm, which is a secondary assignment of weights, feeding the input pattern into the system from the input side and adjusting the weights of the previous layer by using the error between the output value and the target output value, thus realizing the full interconnection pattern of the multilayer perception network.It is known that the input layer of the perceptual network has a input neurons, the output layer hasb output neurons, the hidden layer has e hidden neurons, qij plus formula (1) sought is the connection weights between the output hidden layer and the output layer, and pij is the connection weights, and then, the output results of the hidden neurons and the output layer neurons under forward propagation are shown in the following equation:(2)K=f1∑i=1apijXn,Y=f2∑i=0epij−K.Using the above equation, the multilayer perception network completes the forward propagation and establishes the spatial mapping from a dimension tob dimension. Under the definition of the backpropagation algorithm, let the input data be c, denoted by x1,x2…xc, and the output value is calculated after the cth data are input into the network. Assuming that the error function is squared, the error between the cth output value and the target output value is calculated as shown in the equation.(3)ΔY=12∑c=1nXc−Yc,where ΔY is the error and Xc denotes the original input and also the desired output. Adjusting the connection weights pij between the input layer and the output hidden layer according to this result, the equation of change of pij is calculated as follows:(4)Δpij=∑i=1c∑j=1nμXc−Ycf2′SHKqijf1′SKqij,where f2′SHK is the partial differential of the hidden layer transfer function and f1′SK is the partial differential of the output layer transfer function. By adding pij to the result of equation (4) and similarly qij to the result of equation (1), we can use the connection weights of the two directions to realize the data analysis of positive and negative directions and complete the setting of the full interconnection mode of the multilayer perception network so that the design of the university financial system based on multiscale deep learning is completed [21–23].


## 3.1. Creating Database Form to Set Connection Weights

In the financial system of higher education, the financial information covered is of various types, complex contents and refined subjects may appear at the third or fourth levels, and the system may carry out multiscale deep learning by designing a database form with a sharp, clear objective and distinct type division and by setting a weight value to connect the same attribute information. The basic information about college users and college workers is shown in Table1 and Table 2.Table 1
Basic information of users.
Yuduan nameData typeLengthRemarksCruxEIDVarchar10Customer numberYesNameVarchar10Full nameNoTelVarchar10Contact informationYesDIDVarchar20Affiliated unitNoAreaVarchar50Place of residenceYesDNameVarchar10Unit typeNoTable 2
Basic information of employees.
Yuduan nameData typeLengthRemarksCruxEIDVarchar10Employee numberYesNameVarchar10Employee nameYesSexVarchar10GenderNoBirthdayDate10Date of birthNoHometownVarchar10Native placeNoAddressVarchar50Current residenceYesTelVarchar10Contact numberYesID_numberVarchar10ID numberYesDepartmentVarchar20DepartmentYesWith Table1 and Table 2 as the two major categories for financial information retrieval, the financial information of universities in terms of customers and employees is recorded and updated in real time. Set up information such as customer payment items, drug categories, and charge numbers is shown in Table 1; and set up information such as basic salary, job salary, seniority salary, employee benefits, incentive salary, and social insurance is displayed in Table 2. According to all the financial information of the university, we set the weights of the connected financial information, and the equation of the change of the weights is shown in the following equation:(1)Δqij=−μ∂D∂qij=−μ∂∂qij∑m=1mDm=∑m=1m−μ∂Dm∂qij,where qij denotes the connection weights between financial information; Δqij denotes the change of weights; i and j denote two random financial information; μ denotes the learning rate of the system; m denotes the same attribute of financial information; Dm denotes the global error value. According to the information of the established database form, the weights that can connect the financial information of universities are set.


## 3.2. Designing a Fully Interconnected Model Based on Multiscale Deep Learning

It is known that the perceptron has a single-layer computing capability and belongs to a kind of feed-forward network, and according to the set weights, bottom-up information transmission can be performed for each layer of the network. Therefore, according to this function of the perceptron, a multilayer perceptual network is constructed, and in this way, the full interconnection pattern of the system information is set; the connections between neurons in different layers are used to mine the financial information of the university.The perceptron is used as each node in the neural network, and a dynamic connection weight is set according to the result of equation (1), and then, the perceptron is used to learn this weight; the topology of the multilayer perceptual network is schematically shown in Figure 2.Figure 2
Topology of the multilayer sensing network.In Figure2, x denotes the random financial information; H denotes the input layer unit; hk denotes the hidden layer unit; K denotes the output layer unit; Y denotes the final result. According to Figure 2, neurons in the same layer are not connected to each other and neurons in two adjacent layers are fully connected to each other, and the data transmitted from the input to the output are calculated layer by layer through directional information transfer. The multilayer perceptual network setup contains not only input and output layers but also one or more hidden layers, allowing the system to extract financial data features with associated properties within the system during deep learning, which is the forward propagation system interconnection. In contrast, the reverse system interconnection of the perception network is designed using the backpropagation algorithm, which is a secondary assignment of weights, feeding the input pattern into the system from the input side and adjusting the weights of the previous layer by using the error between the output value and the target output value, thus realizing the full interconnection pattern of the multilayer perception network.It is known that the input layer of the perceptual network has a input neurons, the output layer hasb output neurons, the hidden layer has e hidden neurons, qij plus formula (1) sought is the connection weights between the output hidden layer and the output layer, and pij is the connection weights, and then, the output results of the hidden neurons and the output layer neurons under forward propagation are shown in the following equation:(2)K=f1∑i=1apijXn,Y=f2∑i=0epij−K.Using the above equation, the multilayer perception network completes the forward propagation and establishes the spatial mapping from a dimension tob dimension. Under the definition of the backpropagation algorithm, let the input data be c, denoted by x1,x2…xc, and the output value is calculated after the cth data are input into the network. Assuming that the error function is squared, the error between the cth output value and the target output value is calculated as shown in the equation.(3)ΔY=12∑c=1nXc−Yc,where ΔY is the error and Xc denotes the original input and also the desired output. Adjusting the connection weights pij between the input layer and the output hidden layer according to this result, the equation of change of pij is calculated as follows:(4)Δpij=∑i=1c∑j=1nμXc−Ycf2′SHKqijf1′SKqij,where f2′SHK is the partial differential of the hidden layer transfer function and f1′SK is the partial differential of the output layer transfer function. By adding pij to the result of equation (4) and similarly qij to the result of equation (1), we can use the connection weights of the two directions to realize the data analysis of positive and negative directions and complete the setting of the full interconnection mode of the multilayer perception network so that the design of the university financial system based on multiscale deep learning is completed [21–23].


## 4. Empirical Results

### 4.1. Model Evaluation

Constructing a financial reporting fraud identification model with good out-of-sample prediction ability is crucial to the research in this paper. For both the deep learning model and the benchmark model, three types of evaluation metrics, precision, recall, and F1-score, are used to measure the classification performance of the model on the test set.The precision and recall rates can be expressed as(5)Precision=TPTP+FP,Recall=TPTP+FN.In particular, TP (true positive (TP)) represents the MD&A text that is predicted by the model to be a fraud sample and is itself a fraud sample; FP (false positive (FP)) represents the MD&A text that is predicted by the model to be a fraud sample and is itself a nonfraud sample; FN (false negative) represents the MD&A text that is predicted by the model to be a nonfraud sample and is itself a fraud sample [24–27]. The F1-score is a commonly used statistical measure of model dichotomization performance and can represent a harmonic mean of the model’s precision and recall rates, and it is expressed as(6)F1−score=2⋅Precision×RecallPrecision+Recall.The values of all three metrics are between 0 and 1, and the value closer to 1 indicates a better classification performance of the model. However, all three categories of indicators are calculated by assuming a positive sample for fraudulent texts and a negative sample for nonfraudulent texts. In order to investigate the classification performance of the model in different categories, the macroaverage method was introduced, which is the arithmetic average of the evaluation index values obtained when the fraud and nonfraud samples are positive, respectively.


### 4.2. Empirical Results and Analysis

Tables3 and 4 summarize the prediction results of the deep learning model and the benchmark model on the out-of-sample dataset (evaluation metrics are macroaveraged) and the prediction performance of the deep learning model in different categories, using MD&A texts from the university periodic reports, respectively. The architecture of the deep learning model is based on a word embedding model and a character-level convolutional nerve network, while two types of statistical models (logistic regression and plain Bayesian models) and three types of shallow models (support vector machines, random forests, and gradient boosting decision trees) are selected for the benchmark model. Based on the evaluation metrics presented in Tables 3 and 4, the empirical results of the study can be summarized in the following four points: first, the classification performance of the models implemented in the study, both the deep learning model and the other benchmark models, is greater than 0.7, indicating that the models can effectively use the textual information in MD&A for financial reporting fraud identification; second, the classification performance of deep learning is significantly higher than that of the other benchmark models. The framework constructed in this paper can better identify fraudulent financial reports than the models used in traditional intelligent financial reporting fraud detection studies. The evaluation index values of the deep learning models on both types of MD&A text sets are greater than 0.82, indicating that the models show better fraud identification ability on different types of datasets [28–30].Table 3
Classification performance of deep learning models and benchmark models on MD&A text datasets.
ModelMacro_PrecisionMacro_RecallMacro_F1-scoreWord embedding + character-level CNN0.890.8500.851Logistic regression0.800.810.801Naive Bayes0.7950.7950.795Support vector machine0.8110.820.812Random forest0.7670.7680.768XGBoost0.7340.7340.734LightGBM0.7690.7690.769Table 4
Classification performance of deep learning models on two types of MD&A text datasets.
Sample categoryPrecisionRecallF1-scoreFraud0.870.830.84Nonfraud0.840.860.85


### 4.3. Financial System Performance Testing

Based on the experimental data obtained from above, the system functions and operation are black box tested through the system’s dynamic income statement function module and financial trend analysis, etc. After logging into the system, we enter the dynamic income statement interface. We then analyze the correctness of the detailed data in the financial multidimensional analysis table according to the test cases, check the income and expense reports, and check whether the comparison of the same period of the previous year has been shown graphically. We enter the financial trend analysis page, and first, we check the correctness of the parameter setting on the left side, set the year of salary withdrawal as a random year from January 1, 2014, to December 31, 2018, select the section department, submit the query, and check the section financial trend analysis chart and related financial index information as well as equipment yield tracking, future growth trend, and cost management trend analysis in the returned results. The efficiency of the financial system operation is tested as shown in Figure3.Figure 3
Operation efficiency under different system conditions.We compare the proposed system with the other three groups of methods. As can be seen from Figure3, the operating efficiency of the system designed in this paper is significantly higher than that of the system used in the control experiment conducted by the three groups, and the highest operating efficiency of the system designed in this paper is close to 96%, indicating that the performance of the university financial system designed in this paper is superior. Although system B outperforms systems A and C, its performance is still lower than that of our method.


## 4.1. Model Evaluation

Constructing a financial reporting fraud identification model with good out-of-sample prediction ability is crucial to the research in this paper. For both the deep learning model and the benchmark model, three types of evaluation metrics, precision, recall, and F1-score, are used to measure the classification performance of the model on the test set.The precision and recall rates can be expressed as(5)Precision=TPTP+FP,Recall=TPTP+FN.In particular, TP (true positive (TP)) represents the MD&A text that is predicted by the model to be a fraud sample and is itself a fraud sample; FP (false positive (FP)) represents the MD&A text that is predicted by the model to be a fraud sample and is itself a nonfraud sample; FN (false negative) represents the MD&A text that is predicted by the model to be a nonfraud sample and is itself a fraud sample [24–27]. The F1-score is a commonly used statistical measure of model dichotomization performance and can represent a harmonic mean of the model’s precision and recall rates, and it is expressed as(6)F1−score=2⋅Precision×RecallPrecision+Recall.The values of all three metrics are between 0 and 1, and the value closer to 1 indicates a better classification performance of the model. However, all three categories of indicators are calculated by assuming a positive sample for fraudulent texts and a negative sample for nonfraudulent texts. In order to investigate the classification performance of the model in different categories, the macroaverage method was introduced, which is the arithmetic average of the evaluation index values obtained when the fraud and nonfraud samples are positive, respectively.


## 4.2. Empirical Results and Analysis

Tables3 and 4 summarize the prediction results of the deep learning model and the benchmark model on the out-of-sample dataset (evaluation metrics are macroaveraged) and the prediction performance of the deep learning model in different categories, using MD&A texts from the university periodic reports, respectively. The architecture of the deep learning model is based on a word embedding model and a character-level convolutional nerve network, while two types of statistical models (logistic regression and plain Bayesian models) and three types of shallow models (support vector machines, random forests, and gradient boosting decision trees) are selected for the benchmark model. Based on the evaluation metrics presented in Tables 3 and 4, the empirical results of the study can be summarized in the following four points: first, the classification performance of the models implemented in the study, both the deep learning model and the other benchmark models, is greater than 0.7, indicating that the models can effectively use the textual information in MD&A for financial reporting fraud identification; second, the classification performance of deep learning is significantly higher than that of the other benchmark models. The framework constructed in this paper can better identify fraudulent financial reports than the models used in traditional intelligent financial reporting fraud detection studies. The evaluation index values of the deep learning models on both types of MD&A text sets are greater than 0.82, indicating that the models show better fraud identification ability on different types of datasets [28–30].Table 3
Classification performance of deep learning models and benchmark models on MD&A text datasets.
ModelMacro_PrecisionMacro_RecallMacro_F1-scoreWord embedding + character-level CNN0.890.8500.851Logistic regression0.800.810.801Naive Bayes0.7950.7950.795Support vector machine0.8110.820.812Random forest0.7670.7680.768XGBoost0.7340.7340.734LightGBM0.7690.7690.769Table 4
Classification performance of deep learning models on two types of MD&A text datasets.
Sample categoryPrecisionRecallF1-scoreFraud0.870.830.84Nonfraud0.840.860.85


## 4.3. Financial System Performance Testing

Based on the experimental data obtained from above, the system functions and operation are black box tested through the system’s dynamic income statement function module and financial trend analysis, etc. After logging into the system, we enter the dynamic income statement interface. We then analyze the correctness of the detailed data in the financial multidimensional analysis table according to the test cases, check the income and expense reports, and check whether the comparison of the same period of the previous year has been shown graphically. We enter the financial trend analysis page, and first, we check the correctness of the parameter setting on the left side, set the year of salary withdrawal as a random year from January 1, 2014, to December 31, 2018, select the section department, submit the query, and check the section financial trend analysis chart and related financial index information as well as equipment yield tracking, future growth trend, and cost management trend analysis in the returned results. The efficiency of the financial system operation is tested as shown in Figure3.Figure 3
Operation efficiency under different system conditions.We compare the proposed system with the other three groups of methods. As can be seen from Figure3, the operating efficiency of the system designed in this paper is significantly higher than that of the system used in the control experiment conducted by the three groups, and the highest operating efficiency of the system designed in this paper is close to 96%, indicating that the performance of the university financial system designed in this paper is superior. Although system B outperforms systems A and C, its performance is still lower than that of our method.


## 5. Conclusions

Financial reports, as publicly disclosed information, directly reflect the operation of universities and therefore become an important medium for universities to commit fraudulent acts. Effective identification of financial reporting fraud has become one of the important tools to regulate the operation order of the financial market. This paper constructs a character-level CNN model for identifying fraudulent financial reports of colleges and universities by taking advantage of the breakthroughs in NLP in deep learning technology and uses the MD&A text in financial reports as the analysis sample. The results show that the character-level CNN model can still show better classification performance on a small sample dataset where the shallow model has obvious advantages, without complex textual feature directed extraction, and this result also provides direct evidence for using deep learning techniques to improve existing financial reporting fraud identification methods. In addition, the study not only demonstrates the advantages of deep learning models in identifying financial reporting fraud but also the models constructed show better prediction performance on MD&A texts, which reflects the utility and information value of public texts disclosed by universities. This reflects the usefulness and information value of the public text disclosed by universities. At the same time, the textual disclosure of financial reports, as a reliable and easily available data source, can provide good data support for related studies.



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*Source: 9328712-2022-09-07.xml*