Fuzzy Matching Uses & Applications

Duplicate patient records occur when there are multiple identities or records associated with a single customer. For example, John White and John Wyatt may be two different people but may accidentally be recorded as one. In other instances, John’s records may be duplicated if his primary identifying number has changed, such as his phone number or his email address. 

The implications of duplicate records in healthcare is life-threatening. For example, it’s not uncommon to see laboratories sending results to the wrong individual. While there is little empirical evidence available on the potential clinical harm of duplicate records, it cannot be underestimated. 

With the increasing digitization of healthcare records, the amount of patient data being collected and stored has grown exponentially, making it difficult for organizations to manage this data effectively. This is where code-free fuzzy data matching can change the game for healthcare organizations. 

One real-world example of the power of fuzzy matching is Centura Health, a non-profit care organization that struggled to create a single view of their donors and remove duplicates. Using WinPure’s codeless fuzzy data matching solution, they were able to merge, and purge data saving hundreds of hours in manual effort. 

Here is an example of a before and after fuzzy matching of patient data. 

In this table, Maria Garcia and Maria G. Garcia are the same people with a variation in their record. The address is different, because it is quite possible, Maria now lives with Mary Smith who shares the same address data! These kind of errors cause significant challenges in maintaining an accurate patient record. When fuzzy matching is applied, the variation is identified as a potential match. A data analyst will have to verify this information manually, then consolidate the data to ensure accuracy. 

Healthcare data is particularly vulnerable to dirty data challenges such as duplicates and missing information. Yet, it is not easy to resolve. It could take months to clean and deduplicate years of obsolete record. Hence, it is necessary to invest in code-free solutions like WinPure that allows for large scale data matching with no additional programming skills required. 

Fuzzy Matching in Finance: Enhancing Fraud Detection and Customer Profiling

Finance organizations such as banks and credit agencies are not oblivious to fraud. However, one of the main challenges in detecting and preventing fraud in the finance industry is the large volume of financial transactions that take place, which can make it difficult to identify anomalies or inconsistencies.

Other than fraud, poor and duplicate data in the finance industry can cause: 

Money Laundering: Financial institutions are required to monitor customer transactions for signs of money laundering or terrorist financing. Duplicate customer records or incomplete data can make it difficult to accurately identify transactions that may be suspicious, allowing fraudsters to exploit these weaknesses and launder money through the financial system.

False Claims: Insurance companies can also be vulnerable to fraud when dealing with claims. Duplicate or inaccurate data can lead to false claims or duplicate claims, which can result in significant financial losses for the company.

Identity Theft: Identity theft is a common form of fraud in the finance industry, where fraudsters steal personal information to open bank accounts, obtain credit cards or loans, or make fraudulent transactions. Poor data quality can make it easier for fraudsters to access personal information, creating significant vulnerabilities for both individuals and financial institutions.

Insider Fraud: Insider fraud is another form of fraud that can be enabled by poor data quality. This occurs when employees or contractors abuse their position to commit fraud against their employer. Duplicate or incomplete data can make it difficult to identify and monitor employee activity, making it easier for insiders to cover their tracks and evade detection.

Fuzzy data matching in the finance industry can be used to fix dirty data, verify validates, which in turn will help with: 

Identifying and merging duplicate customer records: Duplicate customer records can make it difficult for financial institutions to accurately track customer activity and identify potential fraud. Fuzzy matching algorithms can be used to identify variations in customer data, and merge duplicate records, reducing the risk of false claims or suspicious transactions. Opting for a codeless fuzzy matching solution can accelerate this process, enabling a faster resolution of fraudulent identities. 

For example, a large US bank used a fuzzy matching algorithm to identify and merge duplicate customer records. The bank was able to merge over 12 million duplicate records, resulting in an estimated annual savings of $100 million in operational costs and a reduction in fraudulent activity.

Monitoring transactions for suspicious activity: Fuzzy matching algorithms can be used to identify patterns of suspicious activity and flag potential fraudulent transactions. For example, a large European bank used a fuzzy matching algorithm to identify multiple transactions involving the same payee name or address, which helped to uncover a fraudulent scheme involving multiple accounts.

Improving identity verification: Fuzzy matching algorithms can also be used to improve identity verification by matching customer information against public records or other data sources. This can help to reduce the risk of identity theft and prevent fraudsters from opening accounts using false identities.

For example, a large UK bank used a fuzzy matching algorithm to match customer data against public records, resulting in a 30% increase in the accuracy of identity verification and a significant reduction in fraudulent account openings.

Fuzzy matching can be a powerful tool for finance institutions to improve their data quality and reduce their risk of fraud. By implementing fuzzy matching algorithms in their data management processes, financial institutions can identify and merge duplicate data, monitor transactions for suspicious activity, and improve their identity verification processes, ultimately resulting in better decision-making and increased profitability. 

Marketers struggle the most to make sense of data. An organization’s marketing department is the custodian of customer data, which it uses to amplify growth, drive revenue, and build campaigns that maximizes profit with minimum spend. To achieve these goals, marketers need access to clean, accurate, updated data. Unfortunately, over 80% of marketers we’ve worked with have had to rely on IT teams to get access to data. This creates an unnecessary dependency and causes conflicts in multiple situations where marketers and IT users disagree on data validity. 

In this case, a fuzzy matching solution like WinPure can help marketers overcome dependencies and inefficiencies. 

Here’s an example of how this can be achieved: 

Let’s say you work for an e-commerce company that wants to improve its email marketing campaigns. You have a customer database that includes customer names, email addresses, and purchase history. You want to segment your customer data based on their purchase history to create more targeted email campaigns. The problem is you will have to connect with IT teams to provide you with the most recent data in the form of spreadsheets, which you will have to verify further to ensure accuracy. This means unnecessary hours spent collecting, fixing, and verifying data. 

With WinPure you could easily: 

Import your customer data into the fuzzy matching software. Most fuzzy matching solutions offer a user-friendly interface that allows you to easily upload your data in various formats such as Excel or CSV.

Identify the fields you want to match. In this case, you want to match purchase history, so you’ll select that field in the software.

Set the matching threshold. The matching threshold determines how closely the records need to match before they are considered a match. For example, you might set a threshold of 90% to ensure that only highly similar records are matched.

Run the matching process. The software will then run the fuzzy matching process and identify matching records based on the matching threshold you set.

Export the results. Once the matching process is complete, you can export the results in a new file or directly integrate it into your email marketing platform. You now have a segmented customer list based on purchase history that you can use to create targeted email campaigns.

Watch how this is done in a series of tutorial videos here. 

With this simple process, marketers, too can use fuzzy matching algorithms to match customer data with demographic, psychographic, or other external data sources without relying on IT teams. They can truly become the owners of their data. With user-friendly interfaces and tools, fuzzy matching software can be accessible to non-technical marketing users and can significantly enhance their marketing efforts.

The HR department of large organizations are constantly challenged with employee data, recruitment data, and payroll data. 

For example, an HR department may have candidate data stored in an applicant tracking system, which includes information such as the candidate’s name, email address, and job title. Once a candidate is hired, their information is transferred to the company’s payroll system, which includes additional information such as their start date, salary, and benefits. If the data in the two systems is not properly matched and merged, there may be duplicate records for the same employee, or incomplete data for employees who were previously candidates. 

Moreover, HR departments deal with data streaming in from job sites, social media sites such as Facebook & LinkedIn and from their own websites. If a candidate applies via a job site and then also uses LinkedIn to send in an application, a duplicate record is created. Most organizations ignore this duplication only to end up with inaccurate recruitment insights! 

Fuzzy data matching can help to resolve these issues by identifying and merging duplicate records based on common fields such as name, email address, or social security number. 

An HR employee can use a fuzzy data matching solution to compare data points such as employee names, addresses, phone numbers, email addresses, and job titles, among others, to identify potential matches or duplicates – without having to depend on IT teams for technical assistance. 

As demonstrated in this article, fuzzy data matching can now be used by even business users to treat, maintain, and ensure the integrity of their data. They do not need to go through multiple platforms, or connect with IT users to maintain their records. Most importantly, they can do all this without having to rely on hundreds of Excel sheets!