Intelligent evaluation method of bank digital transformation credibility based on big data analysis

Abstract

In order to measure the quality and level of digital transformation of banks, we evaluate its credibility. Due to the backwardness of data processing technology, the existing credibility evaluation methods have the defects of low validity of evaluation results, which indicates that the evaluation results are one-sided and can not meet the needs of today’s digital transformation of banks. Therefore, this paper proposes an intelligent evaluation method for the credibility of digital transformation of banks based on big data analysis. This paper analyzes the digital transformation strategy of banks in detail. On this basis, it introduces big data analysis technology, selects credibility intelligent evaluation index, constructs credibility intelligent evaluation model, formulates credibility intelligent evaluation steps of digital transformation of banks according to the construction model, and implements the steps to realize the intelligent evaluation of digital transformation credibility of banks. The experimental data show that: compared with the existing methods, the validity data of the evaluation results of the proposed method is larger, which fully confirms the effectiveness and feasibility of the proposed method and is suitable for vigorously promoting and applying.

Keywords

Big data analysis bank digital transformation credibility intelligent evaluation

1. Introduction

In recent years, the government report has lowered the GDP growth target to 6%–6.5% in China, and the GDP growth target has been lowered for three consecutive years. Facing the policy environment of “New Normal” of economy turning into low growth rate, accelerating interest rate marketization reform, promoting financial disintermediation, and policy environment of “Strong Supervision, Deleveraging and Tight Restriction”, the profit space of banking industry has been reduced and roe has been declining for many years. At this time, retail business which integrates assets, liabilities, intermediate and other businesses become a new source of profit for banks and an important business structure for the development of banking industry [1, 2, 3]. With the rapid development of Internet finance, a new round of information technology and technological revolution has come. Mobile Internet, big data, cloud computing, AI, blockchain, Internet of Things, which represent financial technology, have entered a rapid development stage. At the same time, the customer needs are changing, and the integrated service scheme with the view of more customization, convenience and end-to-end has become a new demand. According to the report on the development of China banking industry, it is pointed out that: the scope and depth of the application of financial technology are continuously expanded. Financial technology promotes payment from single business attribute to link scenario, and builds ecological comprehensive financial service platform, promotes the transformation from traditional retail to new retail, helps push the development of inclusive financial business to digital inclusive financial business, and promote the company business to transfer to online trading bank The type upgrading, financial technology gives the interbank business a new strategic connotation, banks need to further grasp the opportunities and challenges, and accelerate their own transformation and development. Moreover, Accenture consulting company predicts that with the deepening of the influence of financial technology, nearly 30% of the banking revenue will be affected by this [4, 5]. Based on the above analysis, the banking industry has increased investment in financial technology, and the digital transformation of retail business of commercial banks has become an inevitable trend.

At present, most commercial banks have invested in the digital transformation of retail business. The new thinking of data enables business innovation and development. The new operation mode of data realizes the refinement of management and optimizes the way of serving customers. Retail business transactions have changed from offline to online and offline integration. Customers pay more attention to convenient digital experience. Digital marketing has become the main way to serve customers. Digital, intelligent and scene “new retail” is developing rapidly. Taking the number of mobile banking customers as an example, since 2019, the overall number of mobile banking users has maintained a steady growth. The number of mobile banking customers of six large commercial banks has exceeded 1.3 billion, and the number of mobile banking customers of joint-stock commercial banks has reached 400 million. According to the semi annual report of China Merchants Bank and Ping An Bank in 2019, the number of APP users of China Merchants Bank reached 92.758 million, an increase of 18.51% compared with 78.27 million at the end of 2018, and the number of APP users of Ping An pocket bank reached 74.3193 million, an increase of 19.4% compared with the end of 2018. The two banks have achieved the leading transformation in the digital retail business, but some banks have just begun the trial stage of digital retail business transformation, and some are still in the wait-and-see stage. With the advent of the digital era, some financial technology companies have grasped the opportunity of the digital era in time, and completed the transformation of the operation mode with customer service as the center, big data as the drive, and Internet finance as the main. The absolute dominant position of banks is shaking. Mobile payment, small and micro credit are constantly impacting the traditional business of banks. The current situation makes the retail business of commercial banks have to accelerate the transformation and realize the digitization of retail business as soon as possible [6].

At present, scholars in related fields have studied the credibility evaluation of bank digital transformation. Dona and Mokashi [7] proposed the service dimension evaluation of traditional banks and digital banks: a case study of Lloyds Bank in the UK. In order to collect data, customers of Lloyds Bank in the UK were selected. Two scales are developed to measure the usefulness, accessibility, ease of use, efficiency, availability, cost-effectiveness, security, responsiveness and credibility of digital banks and traditional banks. Through the research, it is found that compared with Digital Banking, the security of Digital Banking is the key aspect that customers prefer in traditional banks. Therefore, it is recommended that Lloyds Bank maintain a higher level of security in Digital Banking to improve customer satisfaction. Chen and Zhang [8] proposed a credibility evaluation method of bank digital transformation based on artificial intelligence. Based on artificial intelligence technology and the connotation of high-quality development of macro economy, enterprises and banking industry, this paper maps the high-quality development of macro, meso and micro levels to commercial banks, and explains the connotation and level of high-quality development of commercial banks. Based on the logical framework of high-quality development of commercial banks, this paper constructs the evaluation system of high-quality development of commercial banks. Research shows that this upward trend in recent years is because commercial banks have improved their efficiency and innovation ability and strengthened their common functions with society, shareholders and employees.

Compared with developed countries, bank of China’s digital transformation is still in the primary stage of development, there are great problems and defects. In order to measure the quality and level of digital transformation of banks, the feasibility evaluation is one of the key research issues in the field of banking. In terms of the existing research results, due to the backward data processing technology, the existing credibility evaluation methods have the defects of low validity of evaluation results, which indicates that the evaluation results are relatively one-sided and can not meet the needs of today’s digital transformation of banks. Therefore, this paper proposes an intelligent evaluation method for the credibility of digital transformation of banks based on big data analysis. Through the detailed analysis of the bank’s digital transformation strategy, the introduction of big data analysis technology, the selection of credibility intelligent evaluation indicators, and the construction of credibility intelligent evaluation model, so as to further improve the effectiveness and feasibility of the evaluation results. According to the construction model, formulate the steps of bank digital transformation credibility intelligent evaluation, realize the bank digital transformation credibility intelligent evaluation, provide more effective method support for the credibility evaluation of bank digital transformation, and also provide a certain theoretical basis for the research of credibility intelligent evaluation.

2. Credibility intelligent evaluation method of bank digital transformation

2.1 Digital transformation strategy of banks

The digital transformation strategy uses the thinking of bank reengineering theory to carry out around the bank culture, embracing financial technology, focusing on customer experience and reiterating the bank’s operation and management system:

(1)
From customer to user, restate service object and business thinking: The bank has broken through the existing service boundary, expanded from the customer system with bank card account as the core to class II and III accounts, and app users without bank card account, striving to build an Internet funnel-shaped user system. The bank has changed its thinking from transaction to user experience. Guided by this, it adheres to the business philosophy of strengthening the Polaris index, that is, monthly active users [9]. From the customer’s point of view, it creates the best customer experience bank, innovates the bank’s products and services, and is committed to interaction design. Taking the user experience as the strategic commanding point, we first established a system to monitor the user experience, then recorded the customer’s feelings in real time, and finally quickly fed back and improved. We always provided support for the online service platform and customer manager through intelligent way, and fundamentally improved the customer experience.
(2)
From bank card to app, reiterate the bank service mode: With the change of customer behavior, the main position of bank and customer transaction is app. Bank card is only a product to serve customers, but app is a service platform [10]. Taking “China Merchants Bank” and “palm life” as platforms, self built consumption scenarios and external expansion scenarios, used API interface to transmit financial services, strengthened the universality of ecological alliance construction, and initially built a convenient ecosystem for travel scenarios, including public transportation, subway, parking lot, etc.
(3)
From centralization to opening up, the bank reiterated its scientific and technological strength and corporate culture: The most basic support of banks is science and technology, through financial technology investment benchmarking financial technology companies, build an open its system, and comprehensively strengthen the R&D and application of science and technology. The amount of the bank’s financial technology innovation project fund increased from 1% of the pre-tax profit in 2017 to 1% of the operating income of the previous year, and established a financial technology innovation incubation platform, established an independent operation mechanism, and increased the reserve of professional technology and data personnel [11]. In addition, the company’s articles of association were revised and it was proposed that in principle, the total budget amount of financial technology investment each year should not be less than 3.5% of the operating revenue after the audit of the previous year. Culture is at the bottom of financial technology. It launched “eggshell”, an exchange platform for employees, and launched an “open, flat and inclusive” Internet corporate culture. Through the establishment of fault tolerance mechanism, it supports all kinds of innovation and is committed to changing the traditional banking bureaucratic culture.
(4)
From organization to action, the bank organization construction and management upgrading are reiterated: Banks began to form more task-based teams, from organization to action [12]. First of all, gradually build an enabling organization, and strive to improve the service ability of the middle station. Secondly, we should use financial technology to get through all business systems. With massive data as the core, we should establish the data center, integrate internal and external data, improve the big data management system, and finally build the “general staff”. Strengthen the construction of business platform, and reduce the burden on the front line by empowering the market. In general, the middle platform enables the front desk and the front desk feedback to accelerate the upgrading of the middle desk, so as to promote the self evolution and upgrading of the organization and management.

2.2 Introduction of big data analysis technology

The existing credibility evaluation methods have the shortcomings of backward data processing technology. In order to make up for the shortcomings of the existing methods, this study introduces big data analysis technology to improve the validity of the credibility intelligent evaluation results of bank digital transformation.

There are many kinds of big data analysis technology. According to the method requirements, the introduction of online analytical processing technology is a typical data cube technology. It extracts effective information from the data cube through appropriate transformation, so as to carry out subsequent data mining and analysis work [13]. Common OLAP operations have the following forms: roll up, that is, to coarsen the data granularity in the same dimension appropriately, and gather the data to a higher concept layer; drill down, that is, to refine or supplement the data granularity in the same dimension, and provide some information, so as to disperse the data to a lower concept layer; cut block, that is, to select a part of the data cube and separate it. In order to observe and analyze the other two dimensions, we need to list the sub cubes separately; in slice, we need to set one dimension of the data cube as a fixed value, and simplify the data cube into a data square; in axis, we need to rotate the data cube or its blocks or slices by an angle, or change the relative position of some dimensions, so as to provide different perspectives for observation and analysis.

Next, take a bank as an example to analyze the data cube and OLAP technology. The three dimensions of the data cube based on the bank’s profit are time, address and service, and the value of the cube is profit. The OLAP operation of the data cube is as Fig. 1.

Figure 1.

OLAP operation diagram of data cube.

In addition, because the digital transformation of banks will produce a large amount of data, a series of simple data preprocessing technologies are needed, such as data cleaning, data integration, data specification, data transformation, etc. [14].

Data cleaning, through the preliminary browsing and analysis of the original data, the error points, omissions and noise in the data are screened out, and the impact of the poor quality of the data on data mining is minimized. There are three common methods for data cleaning: delete, modify and fit. For the data with serious errors and omissions, which contain almost no effective information, and the obvious noise and outliers, it is recommended to delete directly; however, if the data is indispensable in database construction and subsequent analysis, it is recommended to contact the first-line department to provide data again or recollect the data by means of field means. For the data with obvious errors, but still can distinguish or extract the correct information from the data, it is suggested to revise the data, such as “20020” to “2020”. If there are some errors or omissions in the data, but the trend of data change can be estimated from the data before and after, the data can be fitted by certain mathematical means. The common data fitting methods include: using center measurement, global constant to fill in the defect value, or fitting the defect value by linear function, multivariate function, Bayesian network and decision tree.

Data integration, entity identification of multi-source heterogeneous data through relevant means, and the identified data are classified and integrated. Entity recognition is a complex and systematic project; in the digital transformation data of banks, how to extract consistent information from words or data with diverse expressions and complex and confusing formats, the common processing methods are as follows: Firstly, keyboard input instead of manual handwriting, which can largely avoid information reading errors caused by different fonts or scribbled handwriting; Secondly, keyboard input instead of manual handwriting. The information is filtered by keywords; Thirdly, the relevant information is coded uniformly. If information is to be input in the system, the system will remind the user to select the appropriate code in the drop-down menu, which can largely avoid the confusion caused by different descriptions of the same object or event [15].

Data specification reduces the number of random variables or attributes by mathematical means to simplify the data structure and retain the intrinsic information to the greatest extent [16]. The common ways of data specification are attribute deletion, attribute generalization and attribute construction. Data transformation, by merging and adjusting the quantity or format of the original data, makes the presentation of data more in line with the psychological expectations of decision makers, and provides a better perspective for data observation and analysis [17]. Common data transformation methods include normalization and discretization. Normalization, by merging the data into a unified interval, such as $[{0,1}]$ or $[{{-}1,1}]$ , to avoid the impact of dimension and value range differences on data analysis. At present, linear method is commonly used to realize data normalization:

$\displaystyle x_{ij}=\frac{x^{\prime}_{ij}-\min x^{\prime}_{j}}{\max x^{\prime% }_{j}-\min x^{\prime}_{j}}$ (1)

In Eq. (1), $x^{\prime}_{ij}$ represents the original value of the $j$ index of the $i$ object; $\max x^{\prime}_{j}$ represents the maximum original value of the $j$ index of all objects; $\min x^{\prime}_{j}$ represents the minimum original value of the $j$ index of all objects; $x_{ij}$ represents the normalized value of the $j$ index of the $i$ object.

2.3 Construction of credibility evaluation model

Based on the above analysis results of bank digital transformation strategy and the introduction of big data analysis technology, select credibility intelligent evaluation index, build credibility evaluation model, and lay a solid foundation for the realization of credibility intelligent evaluation of bank digital transformation [18].

According to the national bank data standards, credibility refers to the ability of the system to perform functions when needed, including reliability, availability, recovery, maintainability and other aspects. The credibility of an event is defined as the average of probability and necessity, and the expression is:

$\displaystyle Cr\{A\}=\frac{1}{2}({\textit{Pos}\{A\}+\textit{Nec}\{A\}})$ (2)

In Eq. (2), $Cr\{A\}$ represents the credibility measure of event $A$ , $\textit{Pos}\{A\}$ and $\textit{Nec}\{A\}$ represents the possibility space and necessity space respectively.

In addition, the credibility measurement between data sources is also a key part of the credibility intelligent evaluation of bank digital transformation. Its main content is to build the calculation model of local credibility and global credibility between data sources [19]. The credibility between data sources refers to the comprehensive credibility of the local data sources to the target data sources, which is restricted by the local credibility and the global credibility between data sources.

This chapter studies the status of local credibility and global credibility in the model, the selection of parameter values, the influence of data interaction, the measurement of similarity recognition between two data sources, and the influence of time on the model, and puts forward a new method of credibility between data sources.

In the process of credibility intelligent evaluation of bank digital transformation, when the credibility value between two data sources exceeds a certain threshold, a directed link is established between the two data sources. With the expansion of network scale, trusted virtual network is becoming more and more stable. If the data provided by a data source is found to be untrustworthy or malicious, the model can quickly impose a penalty coefficient on the provider, which will reduce the credibility of the provider in a period of time. But as time goes on, if the data source can continue to provide reliable data, its credibility will be slowly restored [20]. In credibility analysis network, if there is no new context interaction between data sources in a computing interval, a time penalty is imposed on them.

Through the above process, the construction of credibility evaluation model is completed, which provides the basis for the following credibility evaluation steps, and also prepares for the final credibility evaluation [21, 22].

2.4 Establishment of credibility evaluation steps

According to the above-mentioned credibility intelligent evaluation model, the specific implementation steps of the credibility intelligent evaluation are formulated as follows:

Step 1: Data aggregation and information extraction.

After the original data of digital transformation of banks is preprocessed in the data dictionary format described in Section 2.2, the data will be stored in the form of items with a fine granularity. In order to observe and evaluate the digital state of banks, we must aggregate and improve the data items in order to extract the feature information from them. The common data aggregation methods include the on-line analysis and data protocol technology in data preprocessing. In volume, the data of the same dimension will rise along the conceptual level, so that the data can be aggregated to a higher level. Data specification, that is, through the technology of attribute deletion, attribute generalization and attribute construction, it simplifies the data structure and retains the feature information. The two technologies are in a sense identical and can be used in combination [23].

For the aggregation of bank digital transformation data, the first thing to consider is the volume up in the space-time dimension. After repeated debugging and demonstration, the location information should be rolled up to the line level, and the time information should be rolled up to the quarter level. In other words, the credibility evaluation of OCS should take the bank digital transformation of each line every quarter as the evaluation object [24].

For the quality evaluation index, it is suggested to extract it through the dictionary of OCS fault parameters. The extraction method is as follows: set the fault parameter importance code to “2”, then the bank digital transformation important parameters can be screened out. It should be noted that there are 10 important parameters specified in the fault parameter dictionary, while only 7 secondary indexes are specified in the quality evaluation, so the above 10 parameters need to be integrated to make them correspond to the 7 indexes of the quality evaluation.

After the preliminary extraction of quality evaluation indicators, the indicators should be rolled up to the line level along the two dimensions of time and space. As for how to realize the aggregation of parameter values in the process of roll up, it is suggested to keep the parameter extremum without considering the average value [25, 26]. This is because the quality evaluation mainly focuses on the health status of the bank’s digital transformation under extreme parameters. If the bank’s digital transformation can withstand the influence of extreme parameters, there will be no problems under moderate parameters; if the evaluation is based on the mean value of parameters, even if the evaluation results are good, it can not guarantee the normal operation of the bank’s digital transformation under extreme parameters.

For the quality appraisal index, availability index and maintainability index, because they cannot be directly obtained from the data entry, it is necessary to first roll up the data to the line level, and then construct the attribute according to the definition. For example, after the fault items are rolled up, the fault frequency of a certain line in a certain quarter can be counted, and then divided by the total length of the line to get the fault intensity [27]. Other attributes are constructed in this way and so on.

Step 2: Normalization of index parameters.

The linear normalization method is recommended, but it is only suitable for the situation that the larger the index value, the better. According to the essential characteristics and evaluation requirements of bank digital transformation data indicators, this paper makes appropriate improvements to the method. The improved method divides the indicators in the digital transformation data of banks into the following major types: very large indicators, that is, the larger the index value is, the better; very small indicators, that is, the smaller the index value is, the better; interval indicators, that is, the index value should fall into a certain interval [28]. The interval classification of each index should be carried out in strict accordance with the standard.

Step 3: Determining the index weight.

Considering that the current digital transformation data management system of China’s banks needs to be optimized and improved, there are a large number of errors and omissions in the data collected by the existing technology, we still need to learn from the existing experience of bank digital transformation operation and maintenance to correct and supplement the errors and deficiencies of objective data. Therefore, in the use of AHP, the experience driven AHP should be the main method, and the data driven AHP should be the auxiliary method.

Step 4: Score calculation and grading.

It is recommended to use the weight obtained by AHP moisture weight method to directly sum the normalized values of each index to obtain the overall credibility score of the digital transformation object of the bank.

Step 5: Validity test of evaluation results.

It is recommended to combine Kendell harmony coefficient with Spearman rank correlation coefficient.

Through the above process, the intelligent evaluation of the credibility of the digital transformation of banks is realized, which provides accurate data support and help for the digital transformation of banks, and also provides some reference for the research of the credibility intelligent evaluation.

3. Experiment and result analysis

In order to verify the difference of application performance between the proposed method and the existing methods, the simulation experiment is designed by using MATLAB software, the experimental process is as follows:

3.1 Selection of subjects

In order to simplify the experimental process, we select a local bank as the object to collect its digital transformation data.

3.2 Determination of evaluation validity test method

The validity test of evaluation results mainly uses two methods: Kendell harmony coefficient method and Spearman rank correlation coefficient method. Among them, Kendell harmony coefficient method can calculate the similarity of the series generated by two or more evaluation methods, which is suitable for the preliminary test of the evaluation results; Spearman rank correlation coefficient method can be used to generate the benchmark grade series, and then the rank correlation coefficient can be obtained from the evaluation results of several methods and the benchmark grade series. The higher the rank correlation coefficient is, the higher the validity of the evaluation method is, which is suitable for the preliminary test of the evaluation results. The results were further tested. According to the experimental requirements, Kendell harmony coefficient method is selected.

Kendell harmony coefficient method is to add different ranking values of the same object in three ranking sequences generated by AHP method, grey clustering method and TOPSIS method to obtain the sum of ranking values $R_{i}$ and the square $R_{i}^{2}$ of the sum of ranking values. The Kendell harmony coefficient can be calculated by substituting the calculation results into the following formula:

$\displaystyle W=\frac{12\left[{n\sum\limits_{i=1}^{n}{R_{i}^{2}}-\left({\sum% \limits_{i=1}^{n}{R_{i}}}\right)^{2}}\right]}{k^{2}n^{2}({n+1})({n-1})}$ (3)

In Eq. (3), $k$ is the number of evaluation methods; $n$ is the number of evaluation objects.

3.3 Experimental results

According to the selected experimental objects, the evaluation validity test method is determined to evaluate the credibility of the digital transformation of banks. The validity of the evaluation results shows the application performance of the method. Under normal circumstances, the higher the validity of the evaluation results, the better the comprehensiveness of the evaluation, that is, the more accurate the evaluation results. The validity data of the evaluation results are as Table 1.

Table 1
Validity data table of evaluation results

Number of experiments	Existing methods	Proposed method
1	0.5612	0.9459
2	0.5010	0.8991
3	0.4587	0.8912
4	0.4123	0.7451
5	0.4103	0.7010
6	0.3012	0.6945
7	0.3000	0.7892

As shown in Table 1, compared with the existing methods, the validity of the evaluation results of the proposed method is greater, indicating that the proposed method is more comprehensive and accurate, which fully confirms the effectiveness and feasibility of the proposed method.

4. Conclusions

This study introduces big data analysis technology and proposes a new credibility intelligent evaluation method of bank digital transformation. This paper analyzes the bank’s digital transformation strategy, introduces big data analysis technology, selects the reliability intelligent evaluation index, and constructs the reliability intelligent evaluation model. According to the construction model, formulate the intelligent evaluation steps of bank digital transformation credibility, and realize the intelligent evaluation of bank digital transformation credibility. Which greatly improves the validity of evaluation results, provides more effective method support for credibility evaluation of bank digital transformation, and also provides a certain theoretical basis for credibility intelligent evaluation research.

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Intelligent evaluation method of bank digital transformation credibility based on big data analysis

Abstract

Keywords

1. Introduction

2. Credibility intelligent evaluation method of bank digital transformation

2.1 Digital transformation strategy of banks

3. Experiment and result analysis

3.1 Selection of subjects

3.2 Determination of evaluation validity test method

Table 1 Validity data table of evaluation results

References

Table 1
Validity data table of evaluation results