Why Data Quality Management and Cleansing Are Vital for Banking Institutions?

by | Published on Mar 29, 2021 | Data Entry Services

The banking industry is in a state of dramatic transformation with several new regulatory and technological requirements, making them far more agile and responsive in their key operations. Due to unending pressure from new players or competitors in the market, and with the proliferation of mobile devices and technology, banks have started digitizing their mode of operations and this has led to generation of huge volumes of data. In fact, one of the top challenges faced by banks today is managing data – both for regulatory requirements and to gain meaningful insights. Data quality is an important aspect in any industry and banks need quality data to understand their market and customers. Typically, bank data includes that related to large number of business transactions, and records that need to be accessed for various banking functions. When compared to other business sectors, banks have to adhere to strict regulations, which makes data quality management and cleansing for banks vital. Banking institutions must maintain an updated database for efficient contact with their customers and to ensure compliance standards. In addition, there are advanced data analysis tools that help in analyzing and verifying data to make right organizational decisions. Outsourced data cleansing services help ensure good quality data that can be utilized effectively.

Understanding the Concept of Data Cleansing and Its Core Benefits

In simple terms, data cleansing is a form of data management. Over time, banking institutions accumulate a lot of personal information about their customers and prospects. In certain cases, information can become outdated quickly from the basics of contact names and addresses through financial details and product portfolios. The process of data cleansing involves a complete review of all the data within a database to either remove or update information that is incomplete, incorrect, improperly formatted, duplicated or irrelevant. It usually focuses on cleaning up data compiled in one single area.

Where Does Unclean Data Come From?

Generally, misleading, missing, duplicate, or otherwise unclean data can come from a wide range of sources. Some of these sources include but are not limited to –

  • Interfacing and integrating with other systems and databases across the globe. As systems are set up differently, miscommunication can occur between systems.
  • Paper documents anywhere in the data chain can easily be the source of error as they require manual input into electronic systems.
  • Any specific changes that occur to the accountholder’s information that involves sharing across different applications and systems within the banking network. For instance, if an accountholder gets married but the name change is not carried over to all accounts automatically.
  • Any data from third-party partners or systems that has errors in it could get entered automatically and be incorrect. As the banking industry involves constant mergers and acquisitions, it requires reintegration of data which can lead to duplicate or missing entries and even corrupted data.

Generally, the basic objective of data cleansing is threefold – maintenance of information for existing customers (to enable relevant communication), maintaining information to support day-to-day banking functions (like collecting payments), and finally to meet the compliance requirements including data protection legislations such as GDPR. Ensuring data quality management and structured data cleansing can have wide-reaching benefits across a banking institution like –

  • Avoid Costly Errors – The accuracy of advanced analytics capabilities such as machine learning, artificial intelligence, and big data are heavily dependent on the quality of the raw material – data. Correct segmentation of data can help improve customer satisfaction, since risk profiling can be completed more accurately, and therefore customers can be offered lower interest rates and better service offerings. Data cleansing can help avoid additional costs that occur when banks are busy processing errors, correcting incorrect data or troubleshooting.
  • Help Data Work Across Different Channels – Data cleansing clears the path for the successful management of multichannel customer data. Accuracy across customer data including phone, email channels, and so on allows your contact strategies to be successfully executed across channels. For example, if the contact details for a customer is incorrect, and the customer defaults, banks will be unable to contact them, which will require significant additional expense to collect defaulted payments.
  • Improve Customer Acquisition – Banking organizations with well-maintained data are best placed to develop lists of prospects using accurate and updated data. As a result, they increase the efficiency of their acquisition and on-boarding operations.
  • Ease the Decision-making Process – Clean and accurate data is crucial for uncomplicated decision-making, and is essential for moving forward in a digital world. Accurate data supports machine intelligence and other key analytics that in turn help banks with the insights they require to make well-informed decisions.
  • Increase Productivity of Internal Teams – The cleansing process helps improve data quality and in turn ensures increased productivity. When incorrect data is removed or updated, banks are left with the highest quality information and this means that their teams do not have to use time resources to wade through irrelevant and incorrect data.
  • Anti-money Laundering (AML) Purposes – The level of data accuracy and accessibility is a crucial aspect in anti-money laundering (AML) purposes. For instance, you need to be able to verify information, trace transactions and so on, which requires accurate and accessible information. The accuracy of risk calculations must also be verifiable and impacts the amount of capital a bank must hold in reserve. Better quality risk data frees up capital to give better returns to shareholders.

How to Carry Out Data Cleansing – Key Strategies to Employ

So how do banks manage the quality of their data that pile up over a period of time? The data cleansing process is usually done all at once and can take quite a while if information has been piling up for years. That is why it’s important to regularly perform data cleansing. How often organizations should cleanse depends on a number of factors. It is also important not to cleanse too often, because this may waste resources by performing unnecessary actions. Data cleansing focuses on certain key aspects like –

  • Data Auditing – Regarded as the basic step of cleansing, data auditing is the complete reviewing of all customer databases. Auditing performed using statistical or database methods will help detect anomalies and inaccuracies. However, the process of auditing of a database should not be restricted. These methods can include additional steps such as buying external data and comparing it against internal data. The information should be used to gather characteristics and location of anomalies, which in turn helps to identify the root cause of the problem. Banking institutions that have time and staff constraints can outsource data cleansing tasks to a professional data cleansing company.
  • Locating Missing or Inaccurate Information – Updating customer database and regular removal of inaccurate or duplicate information are vital to ensure a clean customer database. The identification of duplicate records or information keeps your database organized and accurate. Locate and correct inaccurate and defective elements and values like misspellings and mistyped number values to avoid important communication being misdirected. Data cleansing should not be restricted to mere identification and removal of inaccurate data from customer database. Rather, it should be used as an opportunity to consolidate the customer data. Additional information such as email addresses, phone numbers or additional contacts should be incorporated whenever possible.
  • Handling Structural Errors –Inconsistent punctuation, typos and mislabeled classifications are the most common problems that need to be resolved.
  • Validating Existing Data – Reviewing existing data for consistency and accuracy is an important aspect in data cleansing. Maintaining your communication channels to ensure that customers are able to pay will help fulfill any legal obligations. Therefore, banking organizations should make sure to update the contact, location and other details they hold for customers.
  • Control and Feedback Mechanism – Banking institutions need to establish a well-controlled mechanism wherein any inaccurate information gets reported and updated in the database. For instance, there should be a control and feedback mechanism for emails – any email which is undelivered due to an incorrect address should be reported and the invalid email address should be cleansed from the customer data.

Maintaining accurate, timely and comprehensive data is a challenging task for banking institutions. In fact, the quality of data is a key aspect that holds processes together to deliver a superior customer experience, gain competitive advantage, and move the financial business forward. If the data captured is incorrect or inaccurate, then it is useless. Automation means that errors can be propagated more quickly and can easily become pervasive. The above-mentioned strategies and techniques can improve the efficiency of banks and prevent them from incurring huge losses. Banks that don’t have enough time and resources to perform data cleansing in-house can consider outsourcing these tasks to an experienced data entry company as this can improve data quality to a great extent. Experienced data entry service providers can perform the data cleansing task in a feasible way to ensure that data is as accurate and consistent as possible.

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