By: Hristo Hristov | Updated: 2023-09-27 | Comments | Related: > Data Cleansing for Validation
Problem
A successful business operation with multiple clients, contractors, or vendors may end up in a situation where it is difficult to determine the legitimacy and risk profile of the counterparties. Managers often perform a cursory look-up against common and publicly open data sources such as state company registers or business organizations. In many cases, this type of check may not suffice as businesses face increasingly sophisticated fraud and financial transaction risks as well regulatory compliance. Therefore, a flexible and more universal know-your-business tool is required to perform a comprehensive and representative check of a business entity.
Solution
When it comes to data verification and validation, Melissa Data is the preferred choice for a Know Your Business (KYB) solution that can identify and verify the validity of business data in multiple countries and regions worldwide to meet regulatory compliance. Before presenting the core functionalities of Melissa’s KYB platform, let us examine why such a solution is beneficial, considering some example scenarios.
Why is a Melissa Data Solution Needed?
According to PwC’s Global Economic Crime and Fraud Survey 2022, 35% of the companies in the Technology, Media, and Telecommunications Industry have experienced customer fraud, and 26% have experienced procurement fraud. These numbers have been consistent over the past several years. However, the recent pandemic spurred the increased digitalization of various business processes, raising the fraud risk significantly. According to the same survey, fraudulent actors and perpetrators tend to be external or act in collusion with an internal actor. External fraudsters are difficult to catch at the outset of a business process (e.g., client or vendor onboarding) with manual tools or traditional research.
Further to fraud, money laundering has remained a substantial risk over the past five years. According to a 2022 United States Sentencing Commission report, money laundering offenses have increased by 12% since 2018. Additionally, the median financial loss caused by a money laundering offense has grown and now gravitates around a median of $300,000 for the pandemic years (2020 – 2022). A recent case in Florida provides a dire example of the failure to vet loan partners. While this case ended with the perpetrator being sentenced, the whole thing could have been prevented if more stringent KYB practices had been applied.
Legal Basis and Regulatory Compliance
In the United States, anti-money laundering laws (AML) have had a strong track record since the 1970s, starting with the Bank Secrecy Act. More recently, in 2004, the Intelligence Reform & Terrorism Prevention Act made provisions for reporting cross-border financial transactions. In the European Union, AMLs have also been consistently updated to incorporate the threats of the 21st century. Both the United States and EU AML frameworks provide a good legal basis for indicting perpetrators. Nevertheless, prevention and risk mitigation is still up to local actors. This is where Melissa Data can help you avoid costly mistakes by vetting your partners in the Melissa KYB database.
In addition to the Bank Secrecy Act, the U.S. Financial Crimes Enforcement Network (FinCen) has implemented the Customer Due Diligence (CDD) Final Rule. Most importantly, the CDD rule states that financial institutions must identify and verify the identity of customers and beneficiaries on a regular basis. In other words, checking your partners once at the beginning of the business relationship is insufficient. A routine and regular check must be performed throughout the relationship to ensure compliance with the CDD Final Rule. With the API integration options that Melissa Data provides, the routine check can be implemented as a new piece of software or integrated into an existing software solution, e.g., a CRM system. From there, the check can be automated and run at specific intervals.
While the benefits of a KYB solution could be quantified based on averted losses due to fraud or money-laundering cases, there is also an implicit benefit. A business that consistently checks its partners will inevitably work with a top selection of partner businesses. This result may lead to additional benefits such as increased credit and trustworthiness.
Industries Impacted
Companies operating in the financial sector, e.g., banks, credit unions, payment processors, money transmitters, and cryptocurrency exchanges, are prime candidates for Melissa Data KYB solutions. Loan or mortgage companies typically need KYB practices as part of their compliance policy. Such companies are financial institutions that provide loans to individuals or businesses for purchasing or refinancing real estate properties. They play a crucial role in the real estate market, and as financial entities, they are subject to various regulations, including AML and counter-terrorism financing (CTF) laws. The Melissa KYB solution provides the foundational layer for automating the administrative burden of the required regular checks.
Also, eCommerce, such as marketplace, online resellers, and online shops, should consider using the Melissa KYB mechanisms. Certain vendors, agents, or sellers of rare items or collectibles (e.g., art, collectible books, numismatics, etc.) may need to vet their clients to prevent potential fraud or misrepresentation as well.
Real estate actors such as realtors, developers, or property managers may choose to integrate the Melissa platform, too. From money laundering in high-end real estate to stealing personal identifying information, the risks posed in the real estate sector are multiple and traditionally high. Nonetheless, Melissa Data can mitigate all these risks and shed light on potential partners.
Finally, there are also business challenges in the legal and accounting domain. Legal firms and accounting consultants have been the target of fraudsters or "simply" subjects of impersonation. These types of businesses may also consider the capabilities of the Melissa Data KYB database.
The Melissa Data KYB Solution
How does Melissa Data solve the problems in the listed scenarios, considering that various actors are involved? The proposed solution is a product called Melissa Personator Identity Global. This is a comprehensive cloud-based service exposed through an industry-standard REST API interface and has Know Your Business (KYB) capabilities. The service provides continuously updated data on millions of entities, including addresses and names. This KYB tool enables you to:
- Identify and verify businesses for any current and future B2B relationships.
- Verify against government sources in the United States, Western Europe, China, and Australia.
- Verify Company Name, Address, and Business ID, among other attributes.
- Enrich business information for select countries.
How Do You Use the Melissa Data KYB Solution?
The Melissa KYB knowledge base is exposed via a single, modern, fast, secure REST API endpoint. The service accepts company data attributes, e.g., names and addresses. The response comes in a JSON format, making it easy to consume by third-party applications such as client or vendor onboarding applications or ERP/CRM products that allow integrations. The KYB service provides a structured response and a list of result codes that help the user interpret the result. Here is an example input:
Company: Melissa Data National ID: 123456ABC Address: 22382 Avenida Empresa Locality: Rancho Santa Margarita Administrative Area: CA Postal Code: 92688 Country: US
This is the corresponding output based on the provided data:
Organization Matched Address Matched Locality Matched Administrative Area Matched Postal Code Matched National ID Matched Confidence: High
It is recommended that a KYB check is performed regularly throughout the life of the business relationship. With the easy-to-use Melissa API endpoint and the structured response payload, the results can also be stored in a special KYB database specific to your business. This database can enable your business to track the changes in your partner’s data in case such a change occurs.
Product Demonstration
To illustrate the full capabilities of the Melissa KYB tool (Melissa Personator Identity Global), we can examine a simple Python web app built with Flask. The app has several pages, one for each test company. Each company page displays a sort of business card, i.e., a summary of the KYB check, with the information that Melissa provided via its API in real-time. The input data are saved in a static JSON file. In a real-life scenario, they may originate from another web service or a database. Below is an image of the input (first two companies):
Every company has a name, address, locality (city), administrative area (state), postal code, country name, and national identification number. Based on the id for each entry, we have created a dedicated page for each company in the Python web app. Upon loading, the page calls the Melissa API and returns the validated information from the KYB database. Let us examine some examples and the output we get.
Austria
For Austria, we have the company "Test Business AT." The KYB API tells us the company is active, and the legal form is GmbH. In the lower half of the card, the KYB results are listed, showing the list of matches performed. The address and ID have been matched, too. Finally, we see a confidence score of "High," meaning the company has been determined to be a realistic entity.
Australia
Australia has a similar result. Additionally, the locality has been matched, which comes from the original address format that includes the state or province name. Again, this company has a high confidence score.
Belgium
What about the Belgian test company? While the name, address, and entity status were validated, we got a confidence score of "Low." This indicates that we have provided insufficient input data, so the KYB database could not perform a complete match.
In this case, we have omitted the national id:
Brazil
Next, we have a test company from Brazil. Here, we see the company type is different, in this case, "corporation." It is also "active." The company details have been provided and fully matched. The confidence score is "High."
Canada
For the Canadian test company, the results look much the same. Again, we have an extra match for the Locality, like other countries with a state or province as part of the address.
China
The Melissa KYB database is also available for validating Chinese entities. In this example, while the address was partially matched, the national id was also matched. Therefore, the confidence score for the entity is determined to be "High."
Spain
Most countries have different national abbreviations for the local company type corresponding to a limited liability company. So far, we have seen "GmbH" in the Austrian and "Private Limited" in the Australian examples. The Spanish example illustrates how the Melissa KYB database correctly returns the national legal form according to the input data. Here, we have a company type "SA," full address matches, and a high confidence score.
Great Britain
The Melissa KYB database works for European and non-EU entities, too. Here is a test company from Great Britain. There is a complete match.
Switzerland
Another example of a non-EU entity is Switzerland. The company type could not be determined; however, the score remains "High."
United States
Finally, we have an example corporation from the United States. It also produces a full match and a credible confidence score.
Conclusion
Throughout this article, we explored the capabilities of the Melissa Data KYB tool. While national governments are taking steps to mitigate and pursue fraud, misrepresentation, or money laundering cases, it is still up to local actors to vet and validate their business partners. This is where the Melissa KYB solution proves to be a powerful integration functionality to programmatically check companies based on registration data such as name, complete address, and national id. The Melissa KYB tool (Melissa Personator Identity Global) implements a proprietary matching algorithm, among other technologies, to determine a plausible match and a confidence score. The confidence score is a single unified indicator intended to summarize the extent of the company’s credibility based on the provided input data. With the Melissa Data KYB tool, businesses can validate their partners quickly and reliably. Furthermore, the validation can occur automatically over regular intervals to guarantee overall trustworthiness and compliance with different legal requirements.
Next Steps
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About the author
Hristo Hristov is a Data Scientist and Power Platform engineer with more than 12 years of experience. Between 2009 and 2016 he was a web engineering consultant working on projects for local and international clients. Since 2017, he has been working for Atlas Copco Airpower in Flanders, Belgium where he has tackled successfully multiple end-to-end digital transformation challenges. His focus is delivering advanced solutions in the analytics domain with predominantly Azure cloud technologies and Python. Hristo's real passion is predictive analytics and statistical analysis. He holds a masters degree in Data Science and multiple Microsoft certifications covering SQL Server, Power BI, Azure Data Factory and related technologies.This author pledges the content of this article is based on professional experience and not AI generated.
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Article Last Updated: 2023-09-27