Intelligent financial management of company based on neural network and fuzzy volatility evaluation

3.1 Financial data flow reconstitution

In order to realize the anomaly analysis of financial data, information flow model of financial data needs to be constructed to extract the abnormal characteristics of financial data, analyzing the change and increasing situation of financial data through the data mining technology, to provide the reference index for financial statistics and auditing department. The paper analyzes the statistical characteristics of financial data through constructing a mining model for financial data and journal trend effectively and synthesizing financial data feature distribution, adopting certain mathematical model construction method to construct information flow model of sampling financial data, combining economic sequence analysis method to make financial data flow reconstitution and feature reorganization.

The financial data is a set of nonlinear economic sequences. The nonlinear economic sequence analysis method can be used to mine the abnormal characteristics of financial data. Through statistics and sampling of apriori information of financial department, inversion economic sequence of univariate financial data can be constructed as {x _n }. High-dimensional phase space reconstitution technology is used for financial data correlation feature reorganization. The abnormal characteristics of financial data is analyzed in the phase space. The information flow model of financial data high-dimensional characteristics is { x i } i = 1 N . Optimal correlation decomposition of the data is done. The phase space vector model of financial data obtained in the reconstituted dimension space is described as: L = [ x 1 T x 2 T ⋮ x N T ] = [ x 1 x 1 + τ … x 1 + ( m - 1 ) τ x 2 x 2 + τ … x 2 + ( m - 1 ) τ ⋮ ⋮ ⋱ ⋮ x N - 1 x N - 1 + τ … x N - 1 + ( m - 1 ) τ ](1)

In the formula, the embedding dimension of financial data in the phase space is m. The sampling time delay of financial data is τ. In the m dimension space of reconstituted data map, the attribute characteristics reflecting financial data category is extracted. The test and statistic model of financial data L is constructed to generate a Gaussian time series y(n) and a discriminant statistical model of financial data is constructed to generate the corresponding alternative sequence: x n = φ 0 + ∑ i = 1 p φ i x n - i + ∑ j = 0 q θ j η n - j(2)

In the equation, {η _i } is financial data redundancy interference item with mean value of 0, and variance of σ², φ₀, φ₁, φ₂, ⋯ φ _p is known as randomization coefficient, θ₁, θ₂, ⋯ θ _q is known as time window. The singular value decomposition method is used to obtain the inversion irreversible value of financial data anomaly feature mining. The singular value decomposition process is L  =  U * S * C , U and C is a phase randomized matrix, and C = ( c 1 , c 2 , ⋯ , c n )(3)

Fourier conversion is carried out for the statistical series of original financial data. Sigma test criterion is used to obtain the third order autocorrelation value of financial data to get the generalized inverse positive solution of financial data distributed in the phase space. Here, S is the singular value of L . The main component vector of financial data redundancy information is: S = diag ( σ 1 , σ 2 , ⋯ , σ n ) , σ 1 ≥ σ 2 ≥ ⋯ ≥ σ n ≥ 0(4)

For any orthogonal matrix, in the linear subspace, phase space reconstitution track matrix L and self-organizing map functional is used to get the phase space control matrix of N × m dimensions through balance point control: X = [ x 1 x 2 ⋮ x 3 ] = [ a 1 T c 1 a 1 T c 2 … a 1 T c m a 2 T c 1 a 2 T c 2 … a 2 T c m ⋮ ⋮ ⋱ ⋮ a N T c 1 a N T c 2 … a N T c m ](5)

In this paper, J x ( N ) is obtained through singular value decomposition of the ith eigenvector J x i ( N ) , combining the non-linear anomaly feature mining method for the maximum Lyapunove index functional of financial data. Set up a separation coefficient J x ( 1 ) for the separation processing of redundant information of financial data according to the number of training samples of financial data and abundance of redundant data. Blind source separation technology is adopted to get the blind source separation covariance matrix C of financial data: C = 1 N [ X - X l ¯ ] [ X - X l ¯ ] T(6)

In the equation: l = [ 1 , 1 , ⋯ 1 ] 1 × N(7)x i ¯ = 1 N ∑ k = 1 N x ik(8)X = [ X 1 , X 2 , ⋯ , X m ](9)

Remove the dimension of original financial data to get the slack variable of financial data in the phase space: δ x i + 1 ≈ J x i ( 1 ) δ x i(10)

In the equation δ x _i  =  x _j - x _i , x _j is the vector quantization information of the original data sample sequence in phase space, and the corresponding m principal component feature subset is x _j  ∈  R ^m . So let x_n+1 = μx _n  (1-x _n ) be the state information parameter of abnormal distribution of financial data. When the detection statistic meets δ x _i+1 =  x _j+1- x _i+1, autoregression ARMA estimation is carried out through financial economic sample sequence of the original statistics [8] to get δ x _i and δ x _i+1. And when the number of principal components satisfies the constraint condition U = {  u (t) |u (t) ∈ X , ∥ u ∥ ≤ d, t ∈ I } , cumulative variance of financial data is calculated in the reconstituted phase space, and the Euler distance of financial data flow distribution track in high-dimensional phase space meets: ∥ x j - x i ∥ ≤ ɛ(11)

In the formula, (ɛ is a small positive number), thus the relevance mapping vector x _j of financial data is determined, thus forming a ontology model writing matrix form of financial data parallel mining: B x i = ( δ x i ( j 1 ) , δ x i ( j 2 ) , … , δ x i ( j N b ) ) T(12)

Through the above phase processing, phase space reconstitution method is used to carry out high dimensional feature expansion of financial data, providing the original data input basis for the abnormal analysis of financial data.

Based on high-dimensional feature expansion of financial data by using phase space reconstitution method, the principal component analysis method is used for auto-correlation feature matching of financial data, to improve the accuracy for mining of financial data anomaly. Time-frequency analysis method is used to obtain the correlation dimension of financial data as: x ( t ) = Re { a n ( t ) e - j 2 π f c τ n ( t ) s l ( t - τ n ( t ) ) e - j 2 π f c t }(13)

In the formula, on the time scale of information flow distribution of financial data, multiple wavelets are decomposed to get the transient disturbance of financial data as: c ( τ , t ) = ∑ n a n ( t ) e - j 2 π f c τ n ( t ) δ ( t - τ n ( t ) )(14)

In the equation above, a _n (t) is thee inversion integral function of abnormal feature performed on the nth phase space distribution trajectory. τ _n  (t) is the time delay of financial empirical sampling. f _c is the sampling frequency of main feature quantity of financial data, and s _l (t) is the transitional information of simple component.

The grid segmentation method is used for transient state disturbance optimization of financial data, and the optimal correlation distribution function of financial data is obtained as follows: h ( t ) = ∑ i = 1 P a i p ( t - τ i )(15)

In the equation, a _i and τ _i are the similarity coefficient and disturbance amplitude respectively. The relevance mapping of financial data is carried out in the reconstituted high-dimensional feature space of financial data, and the attribute characteristics reflecting financial data category are extracted, to get the autocorrelation feature distribution function of financial data as: { y ( t ) = x ( t - t 0 ) ⇒ W y ( t , v ) = W x ( t - t 0 , v ) y ( t ) = x ( t ) e j 2 π v 0 t ⇒ W y ( t , v ) = W x ( t , v - v 0 )(16)

Through the time-frequency scaling, spectral analysis technology is used for the abnormal feature clustering process of financial data. The data clustering center for the construction of financial data abnormal feature mining is described as: y ( t ) = k x ( kt ) , k > 0(17)W y ( t , v ) = W x ( kt , v / k )(18)

Here, k refers to the disturbance amplitude of data clustering, v is expressed as the sampling spectrum value of financial data, W _x is the time window function. Set the amplitude of financial data anomaly feature mining information flow as A. Main feature quantity N _k (x) of optimal correlation data of financial data is obtained. The alternative data analysis method is used to get the information classification error of financial data: W x ( t , v ) = ∫ - ∞ + ∞ x ( t + τ / 2 ) x * ( t - τ / 2 ) e - j 2 π v τ d τ(19)

In the finite domain, the frequency domain distribution of abnormal characteristics of financial data is expressed as: W x ( t , v ) = ∫ - ∞ + ∞ X ( v + ξ / p - 2 ) X * ( v - ξ / 2 ) e j 2 π ξ t d ξ(20)

In the above equation, ξ is the attenuation coefficient in the clustering process of financial data abnormal features, and X is the bifurcation distance of financial data classification vector. X^* refers to looking for the conjugate of the financial data. Hyperbolic frequency modulation is carried out for financial data anomaly detection system. According to autocorrelation r feature matching technique, the matching result of abnormal feature is: p ( Q s ) = a k 1 2 π σ s exp [ - ( Q s - 〈 Q s 〉 ) 2 2 σ s 2 ](21)

Here, a _k is recognition coefficient of financial data attribute. If a _k  = 0, it means the kth abnormal data clustering center tends to zero. If a _k  = 1, it means there is a disturbance in the clustering space, it indicates that there is abnormal data.

Before the process reengineering of the financial intelligent management center, first analyze the business processes, connect with the business processes of each department, define and confirm the scope of business processes that need to be adjusted due to the construction of the financial intelligent management center, and then redesign and transform it. To fully analyze the existing constraints between departments and the interrelationship between business processes, it is necessary to cooperate with information system in the process of business process design and reengineering to optimize business processes to the greatest extent.

The company’s new financial process reengineering framework has been transformed into transferring the financial data of each branch’s business department directly to the financial Intelligent Management Center for processing, which has changed the three-level management mode under the traditional mode. This process has realized the standardization of standards, reduced the processing level and the total amount of work, and improved the work efficiency. Through the business types transferred to the financial intelligent management center to confirm the scope of business process, and through standardized processing to reengineer each process, the important link of the construction of financial intelligent management is realized. The company follows the principle of business process reengineering, optimizes the business process, and improves the efficiency of financial processing by formulating corresponding standards. It mainly reengineers the corresponding purchase and payment business, cost and expense management, sales and collection business, asset management, general ledger and report business.

Procurement and accounts payable business is the earliest and most basic content of financial intelligent management application. Under the traditional financial management mode, each secondary organization has the financial autonomy, and each unit is responsible for the procurement and accounts payable business. The financial department of the headquarters does not interfere, which is not conducive to the understanding and management of the headquarters on the business situation. However, in the financial intelligent management mode, it is necessary to redesign the process of procurement and accounts payable business, and no longer use the previous three-level management system, and concentrate the procurement and accounts payable business to the Financial Sharing Center for processing. The headquarters of the company can monitor the operation status of each branch in real time, and connect the procurement and accounts payable business with the budget system, so as to fully develop them We should play the role of budget management, strengthen cost control and reduce financial risks. The company’s procurement business mainly refers to the cost control of the construction unit through budget management control and the procurement of large-scale production equipment, engineering production materials and other bulk businesses according to the needs of the project schedule during the construction process. There are a few sporadic purchases with high frequency and less funds involved, which can be classified into expense management for business processing according to the situation.

In the process of procurement and accounts payable business process design, it is necessary to combine risk prevention and control, and divide the business process design into three important links. First, budget generation and review is to make corresponding budget according to the construction plan, which is reviewed by relevant personnel. The basic basis of business data information is to complete the approved data, and to calculate the cost and budget data Comparison can strengthen cost control and risk prevention, and strictly control budget to prevent over expenditure. Then the business is preliminarily reviewed. According to the budget entry contract, the application is submitted through the scanning data of the image system and is waiting for review. The main purpose is to preliminarily review the authenticity of the business. After passing the preliminary review, it will enter the business review of the sharing center, control the business compliance and whether it is within the budget through the financial intelligent management center, and feed back to the performance department and relevant business departments, so as to control the problems in time, and pay for the approved online banking, and the financial intelligent management center will feed back in time in case of failure for special reasons.

4.1 Randomization and de-redundancy processing of financial data

Based on the information flow reconstitution and autocorrelation feature matching of financial data performed above for abnormal feature mining, this paper presents a method to analyze the abnormal financial data based on the correlation data feature mining technology. Alternative data method is used for the randomization of financial data processing. Alternative data method is from the modern statistics Bootstrap theory, and the realization process is described as:

Phase-randomize the original financial data to generate a set of Gaussian economic sequences y(n) to obtain a linearly related Gaussian process;

In the association rule mapping system, the quaternary group of financial data attribute classification is constructed;

{ r ( n ) } n = 1 N is made Fourier transformation, and optimal matching of financial data is carried out to get a new sequence y′ (n), and the association rules and autoregressive eigenvectors are the same;

The attribute characteristics reflecting abnormal information of financial data are extracted, and elimination of redundancy is made for the substitute data x(k). The redundant vector of the financial data is: W y ( t , v ) = ∫ - ∞ + ∞ W h ( t - s , v ) W x ( s , v ) ds(23)

In the equation, t is data sampling time, s is correlation coupling coefficient of financial data. In the data clustering center, the self-adaption weighting W_e = (ω_j^(e), 0) of financial data classification is carried out non-correlation redundancy removal process by feature compression method. K-L transformation is used to obtain characteristic compression results:

| ∫ - ∞ + ∞ x ( t ) y * ( t ) dt | 2 = ∫ - ∞ + ∞ ∫ - ∞ + ∞ W x ( t , v ) W y * ( t , v ) dtdv(24)

Set the error of abnormal information mining of financial data as p ¯ = ∑ p i ∈ N k ( x ) p i θ p i ( x ) . The nonlinear abnormal feature mining is used to get the non-correlation redundancy blind source separation results of the financial data as: f x ( t ) = ∫ - ∞ + ∞ v W x α ( t , v ) dv ∫ - ∞ + ∞ W x α ( t , v ) dv(25)

In the equation, x _α is the number of main components reflecting abnormal information in the financial data information flow.

In the nonlinear economic sequence of financial data, high-dimensional multivariate statistical analysis method is used to map the financial data. Assume that the financial data is generated by the linearly related nonlinear economic sequence, and the following ARMA model is used: x n = a 0 + ∑ i = 1 M AR a i x n - i + ∑ j = 0 M MA b j η n - j(26)

In the equation, a₀ is the sampling amplitude of initial financial data, x_n-i is the financial data scalar economic sequence with the same mean, variance, b _j is the oscillation amplitude of financial data. Third-order autocorrelation statistic is used as the inspection statistical data of financial data abnormal analysis: C or 3 = 〈 ( x n - x ¯ ) ( x n - d - x ¯ ) ( x n - D - x ¯ ) 〉 〈 ( x n - x ¯ ) 3 〉(27)

In the equation, x _n refers to non-linear economic sequence of financial data, d refers to the time interval for sampling financial data, D = 2d, x ¯ represents the mean, 〈x (n)〉 represents taking the mean of the financial data sample x(n): 〈 x ( n ) 〉 = 1 / N ∑ n = 1 N x ( n )(28)

The trajectory of non-linear economic sequence and vector feature economic sequence of financial data in the high-dimensional phase space is {x (t₀ + iΔt)}, i = 0, 1, ⋯ , N-1. Decision tree model is used for the anomaly of financial data. The basic idea is to measure the time-varying characteristics of economic data and randomness characteristics x and x _{n  +τ}. Through the linear correlation processing, the average mutual information C(τ) of financial data is defined as: C ( τ ) = lim T → ∞ 1 T ∫ - T 2 T 2 x ( t ) x ( t + τ ) d τ(29)

In the equation, τ is the time delay of financial data in the reconstituted phase space, representing the correlation degree of financial data change at the time of t and t+τ. The abnormal behavior trend and abnormal feature of financial data are mined based on the correlation degree to get the correlation dimension information in vector space of financial data economic sequence as: I ( τ ) = - ∑ ij p ij ( τ ) ln p ij ( τ ) p i p j(30)

A point in the economic sequence phase space of reconstituted financial data is expressed as X _n , and the nearest neighbor point in the set of stochastic phases of financial data is expressed as X _η(n), thus the economic sequence exported by financial data anomaly feature mining is: X ( n ) = { x ( n ) , x ( n + τ ) , ⋯ , x ( n + ( m - 1 ) τ ) }(31)

Finally, Sigma test method is used to construct the test criterion of abnormal feature mining of financial data. According to the significant difference of discriminant statistic, the accuracy of abnormal feature mining of financial data is tested. The test criteria is: p ( Q s ) = 1 2 π σ s exp [ - ( Q s - 〈 Q s 〉 ) 2 2 σ s 2 ](32)∫ - ∞ ∞ p ( Q s ) d Q s = 1(33)

In the equation, p (Q _s ) ∼ (Q _s ) curve is shown as Fig 1.

OPEN IN VIEWER

Fig. 1

p(Q _s )∼(Q _s ) curve distribution of test criterion.

According to Fig. 1, financial data abnormal feature mining distribution meets the standard normal distribution, and if the difference between Q₀ and 〈Q _s 〉 exceeds a certain threshold Q _c , making:

p ( | Q 0 - 〈 Q s 〉 | > Q c ) ≤ 0.05(34)

At this point the confidence of financial data mining is 95%, because the normal distribution is symmetrical on both sides of 〈Q _s 〉, there should be: 0.025 = ∫ - ∞ z 2 p ( Q s ) d Q s = 1 - ∫ - ∞ z 1 p ( Q s ) d Q s(35)

In the equation, z₂ =-z₁, when S ≥ 2.00, the abnormal distribution of financial data is not established with 95% probability, accepting the original hypothesis, the financial data anomaly mining results meet the convergence conditions.

6.1 Improve the effectiveness of business process

Based on the construction of the information system platform, the company realizes the main link of the construction of the financial intelligent management service center through the design and adjustment of the business process. The smooth connection between the business process and the financial intelligent management platform determines the effectiveness of the operation. When building the financial intelligent management center, the design and improvement of the business process should be carried out at any time according to the strategic objectives and development needs Appropriate adjustment, try to make each business process to achieve effective use and high-speed operation. For the management of business process, we can take the way of analysis and discussion to find solutions to the problems in the use process. At the same time, the optimization of business processes should be investigated in-depth by the business departments, the business processes necessary for the implementation of the operation of the financial intelligent management center should be preserved, and the business processes with certain restrictions and low use frequency should be eliminated and refined to ensure the effectiveness of all business processes and achieve the goal of making full use of resources. The construction of branch functions as show in Table 2.

To adjust and improve the business process, it is necessary to run through the whole business process. It needs to be carried out continuously for a long time. According to the development of the enterprise and the internal and external environment, the company needs to make corresponding adjustments in time. To improve the business process, the company needs a professional process management team to be responsible for the confirmation and management of the business process scope, and must have professional technology Only in this way can we find out the problems in the business process and optimize the plan in time. There should be strict requirements and standards for the selection of professional business process management personnel, as well as the ability of good communication and coordination, and be able to achieve information exchange between the financial personnel and the staff of each business department, excavate the information related to the business process from the exchange and conduct in-depth analysis to find solutions to the problems. At the same time, the participants of business process should be encouraged to put forward more use feelings and suggestions related to business process, cultivate the participation consciousness of all employees, publicize the importance of business process management, advocate the staff team to actively make suggestions and suggestions, and the opinions and suggestions from the practice of grass-roots staff team are more practical. Therefore, some incentive measures should be given to the employees who put forward valuable suggestions To mobilize all employees to contribute to the goal of the financial intelligent management center.

6.3 Adjust risk control measures in time

In the process of transformation of financial management mode, resources will be re integrated with big data and other information technologies. The corresponding organizational structure and business process will be redesigned and adjusted. The concentration of financial accounting also brings the concentration of risk. The change of management mode will have a certain impact on the original internal control management. Therefore, when building the financial intelligent management center, the It is necessary to strengthen the management of internal control, formulate internal control standards more suitable for the new management mode of financial intelligent management, and provide guarantee for the company to achieve the new goal of overall development. Individuals in the top 10 as show in Table 3, and risk evaluation shows in Table 4.

Strengthening the control activities of the financial intelligent management center and paying attention to the risk management of receivables can promote the expansion of income scale. The company’s financial intelligent management center has the characteristics of large-scale project and long construction time due to the particularity of its industry, which leads to the long recovery time of accounts receivable. For a / R and a / P management, the financial intelligent management center should set up a collection department and a credit rating approval management department to audit and supervise the business, do a good job in risk assessment, select a reputable enterprise cooperation to reduce the possibility of bad debts and dead accounts, and set up a feedback agency to control and monitor the situation of receivables. By tracking the payment, we try to avoid the improper behavior of intentionally defaulting payment, and strengthen the cooperation between departments to strengthen the management of collection and payment. At the same time, the financial department should provide the corresponding basis for the internal control management according to the real-time situation of accounts receivable, and adjust the risk control measures in time according to the dynamic situation.