The economic downturn and financial problems faced by businesses today indicate the importance of utilizing data to predict future events. But the ambiguities present in financial data can lead companies to base crucial decisions on inaccurate data and suffer the consequences. Bankers, insurers, mortgage firms, and other corporates offering financial services are not immune to the data quality nightmare. In fact, these businesses experience the highest costs resulting from poor quality of financial information.
In this blog, we will cover the meaning of data quality in financial services, how it benefits individuals and organizations, common data quality issues present in financial data, and how to improve the quality of financial information.
What is data quality in financial services?
Data quality in financial services means that financial data being captured, stored, processed, and presented by financial institutions serves its intended purpose. Any data that fails to fulfil its purpose is known to be of poor quality and must be tested and verified before it can be used effectively.
Financial institutions – such as banks, insurance companies, mortgage or brokerage firms, investors, creditors, or lenders – utilize data in almost every business process. Financial data is used to:
- Prepare financial statements and reports for internal use and customers,
- Approve loans and complete the underwriting process,
- Catch or prevent fraudulent activities such as stolen details or fake applications,
- Identify individuals that are more likely to default on their loan payments,
- Assess risks associated with financial decisions, such as operational or credit risk, etc.
It is obvious that poor data quality can badly impact the execution and outcomes of these processes. Feeding accurate and clean data to these processes is detrimental for protecting the credibility of financial institutions.
Why is data quality important in financial services?
Since data is tightly integrated in the financial services industry, it is very important that the data is free from error. High quality, clean, and error-free data allows customers to trust their investment banks and insurance companies. Let’s take a look at the importance of data quality in financial services industry and the benefits you can reap by ensuring the quality of your financial data.
1. Assess, plan, and mitigate risk
Risk is inevitable with certain financial activities – whether you want to invest in a venture, lend money to a borrower, or approve loans or mortgage requests. But intelligent risk planning is crucial to survive in the financial world. With careful data analysis and risk assessment, you can mitigate the risk and make better decisions about expected returns, profitability, and other alternatives. But for that, you need correct, accurate, and relevant data that helps you to dodge financial risks and potential loss that may exist.
2. Catch and prevent fraudulent activities
Banks, insurance companies, and investors that have poor data quality are more susceptible to fraudulent behavior and casualties. This is because the loopholes in data quality allow fraudsters to steal identity, make fake applications, bypass reapplication checks, and perform malicious attacks on sensitive data stored by financial organizations. Clean, accurate, and consolidated data allows you to catch anomalies in time and prevent fraudulent activities.
3. Enable digitization of financial processes
Digital banking, online payments, and online credit requests are revolutionizing the financial industry. But successful implementation and execution of these digital services is only possible with high quality data. Many bankers and investors still maintain physical files, since the data is scattered across different sources and requires manual intervention to be comprehended as needed. Managed data quality enables financial institutions to digitize any aspect of their business or service offerings.
4. Ensure customer loyalty
When customer records are matched, merged, and consolidated to represent a complete 360 view, it becomes easier to leverage personalized customer experiences as well as ensure customer privacy and security. When data is scattered across different sources – including local and physical files, third-party applications, and webform submissions – it becomes impossible to provide a connected experience to your customers and build trust and loyalty.
5. Allow accurate credit scoring for loan approvals
When it comes to lending money to borrowers, it is crucial for investors and bankers to understand the liability of their decisions. They must validate the applicant’s identity and credit score, as well as calculate the value and interest rate to be used for the loan. Good data quality can eliminate any discrepancies or delays that may come up in the underwriting process and make sure that you are investing in the right individual at the right time.
6. Comply with regulatory standards
Compliance standards, such as Anti Money Laundering (AML) and Counter Financing of Terrorism (CFT), compel financial institutions to revisit and revise their data management in financial services. To comply with these standards, these businesses must monitor their client transactions to catch financial crimes, such as money laundering and financing of terrorist activities. With inaccurate and poor quality of information, financial institutions fail to report abnormal or unusual activities to relevant authorities in time.
7. Facilitate predictive analytics
Data science has evolved to enable real-time predictions and insights in the world of finance and potential risks associated with financing activities. Investors predict the feasibility of investing in a certain market, or which stocks will be more profitable in the longer term. These calculations will not be accurate and relevant if the data used for these statistics is of poor quality. Hence, another great advantage of data quality is to allow data analysts and data scientists to make accurate predictions about financial profit.
Common data quality issues in financial services
We discussed how data quality offers great value to financial institutions. In this section, we will see what poor data quality looks like for different financial institutions, such as the data quality issues in banking or the data quality issues in insurance companies. You can read more about the most common data quality issues and where do they come from.
|Data quality issue||Explanation||Example of poor data quality in financial services|
|Inaccurate data||Data does not depict reality or truth.||The full legal name of a customer is spelled incorrectly in the lending agreement.|
|Missing data||Data is not as comprehensive as needed.||2 out of 15 covenants in a loan agreement are left blank.|
|Duplicate records||Data contains duplicates and does not represent unique identities.||The presence of duplicate customer records allows multiple loan applications.|
|Variable measuring units||Data is stored in varying measuring units.||International transactions store the monetary values in local currencies, rather than a standard trading unit, such as the US dollar.|
|Variable formats and patterns||Data is stored in varying formats and patterns.||Phone numbers of customers are stored in different patterns – some have international codes, while others do not even have area codes.|
|Outdated information||Data is not up-to-date or as current as possible.||Transactions take a little too long to show up in customer records, causing the system processes to be susceptible to incorrect computation.|
|Incorrect domain||Data does not belong to a domain of correct values.||The currency codes used do not belong to the ISO domain.|
|Inconsistency||Data is not the same across different sources.||Different exchange rates are being used for different customer segments across the organization.|
|Irrelevance||Data offers no value to its users.||Employees get the required information after applying multiple filters, sorting, and prioritization rules.|
How to improve the quality of financial information?
Data quality issues can cost you a lot of bucks – especially if you are situated in the financial industry. Corporates offering financial services need to test and verify their data before it is fed to critical business processes. Calculated steps must be taken to prevent data quality issues from occurring in the system as well as remediating the issues that already exist. Below, we will look at the most important initiatives that financial organizations can take to ensure data quality.
1. Get buy-in from leadership and management
The first step to enable data quality culture in any organization is to involve business leaders and other managerial personnel. You can start by bringing their attention to the data quality issues that are present in the datasets. Data quality reports generated through data profiling can be useful for educating upper management and other staff members about the kind of data quality issues your institution is facing.
Moreover, you can get a sample of data from recent financial activities and calculate the cost of poor data quality using the Friday Afternoon Measurement method. This will help you to build a case against poor data quality and get the approvals and buy-ins necessary to execute data quality measures.
2. Implement three levels of data quality control
Data quality control is getting more advanced as new techniques and technologies emerge. This helps banks and insurance companies to enable multiple levels of data quality control. For example, in the first and initial level, you can start off by doing a quick fact checking and fixing the data quality issues that may be present. At this level, you want to make sure that the dataset is complete, accurate, and standardized.
At the second level, you want to implement a deeper statistical analysis of your dataset. This will help you to compute standard variations of numerical values and catch anomalies that may occur. Data profiling is a good technique to perform such statistical analysis on your data. At the third and final level, you can use complex machine learning and AI tools that can predict possible data quality issues at runtime that your sources are prone to have.
3. Reconcile and consolidate duplicate records
Data duplication is one of the biggest data quality issues faced by banks and insurance companies. They should employ a data quality framework that matches duplicates and consolidates them into one. Records can be matched at runtime with every update or processed in batches at regular intervals. Read more about batch processing versus real-time data quality validation.
Record reconciliation or data deduplication process consists of the following steps:
- Profiling data to highlight errors,
- Running data parsing, cleansing, and standardization techniques to achieve a consistent view,
- Matching records that belong to the same entity (exactly on a unique identifier or fuzzy matching on a combination of fields),
- Merging records together to remove unnecessary information and achieve a single source of truth.
4. Utilize technology for data quality management
Utilizing technology for attaining a sustainable data quality management lifecycle is at the core of improving data quality in any financial institution. No process is promised to perform well, and give the best ROI – if it is not automated and optimized using technology. Invest in adopting a technological system that comes with all functionalities that you need to ensure data quality across datasets.
No matter how skilled your data quality team is, they will still struggle to sustain acceptable levels of data quality until they are provided with the right tools. This is where a data quality management tool can come in handy. An all-in-one, self-service tool that profiles data, performs various data cleansing activities, matches duplicates, and outputs a single source of truth can become a big differentiator in the performance of data stewards as well as data analysts.
Understanding the data quality issues in your financial data and choosing an appropriate framework to rectify these errors is a difficult task. In many situations, one technique is not enough, and a combination of techniques is used to accurately fix data quality issues. For this reason, the need for digital tools is increasing. Tools that not only optimize time and effort, but also intelligently select data quality techniques depending on the nature of your data structure and values.
DataMatch Enterprise is one such tool that helps you to clean and match your data to enable accurate analysis and comprehensive insights. It offers a range of modules that support data coming in from different sources, clean and standardize values, enable field mapping, suggest a combination of match definitions that are specific to your data, and merge data to get a complete 360 view of your finances.