# Financial Data Quality Evaluation Method Based on Multiple Linear Regression
## 1 Introduction  
Quality evaluation commonly pertains to the methodologies that organizations employ to ensure the attainment of strategic objectives, establish regulatory compliance, and monitor data integrity [1]. In the past, data collected from various transaction systems within companies or institutions were often regarded as a mere by-product of business operations, possessing limited value beyond the transactions [2]. Today, as the complexity and volume of data continue to escalate, numerous companies and businesses are compelled to adapt their business models accordingly. Customers need personalized products, and service products must be industrialized. These factors inevitably influence business processes and organizational strategies, and high-quality data are a prerequisite for meeting these changing business demands and achieving corporate agility objectives [3]. A single data management solution is insufficient to meet the demands of the business. Therefore, different approaches to integrating solutions to data problems must be employed [4]. Financial institutions must ensure the completeness and accuracy of their data quality so that regulatory bodies can better understand the operation of the institutions, predict risks, and take corresponding measures [5]. In the era of rapid big data technology advancement, financial institutions have been compelled to enhance their management, processing, and analysis of vast volumes of personal customer information. This imperative arises from the goals to enhance business efficiency and strengthen risk management capabilities. Institutions are increasingly utilizing machine learning technologies to analyze customer behavior, predict market trends, and identify fraudulent behaviors. These technologies alsomust be able to support a large amount of high-quality data; therefore, financial institutions must establish effective data governance and evaluation mechanisms to ensure data quality. [PERSON] [6] introduced a methodical yet adaptable framework consisting of ten steps to address an organization's business needs and data quality issues. Within this framework, data quality dimensions are employed to precisely define, measure, and effectively manage the quality of data. [PERSON] et al. [7] presented a comprehensive compilation of prevalent data quality dimensions, encompassing accuracy, completeness, consistency, and timeliness. Furthermore, they proposed a novel approach to gauge row completeness utilizing a data mining model developed on neural networks. [PERSON] et al. [8] measured data quality from a systems perspective with more dimensions and considered customer data quality to facilitate the development of personalized interactive marketing initiatives. [PERSON] et al. [9] developed a scheme to evaluate data quality on large datasets using sampling strategies, which adopted some data quality measurement metrics tailored to specific data quality dimensions. [PERSON] [10] conducted an investigation on a variety of data quality metrics, with a specific focus on the measurement and quantification of particular dimensions like completeness and consistency. Recently, the analytic hierarchy process (AHP) was used to integrate various data quality dimensions as well as expert preferences for quantifying a comprehensive score of data quality [11], and has been applied to healthcare [12], financial statements [13] and cloud computing [14]. For the data quality evaluation system, most experts only consider qualitative or quantitative situations, citing a single data quality evaluation method, such as fault tree analysis, AHP, gray relational degree analysis, etc. Some scholars combine commonly used engineering evaluation methods to build evaluation models on the basis of combining qualitative and quantitative methods. Through research and combining, this paper finds that many scholars have not unified the selection of data quality indicators, and most of them use more traditional methods, which lack certain innovation.