Cross-matching is part of the identity verification process and involves filling in the gaps with customer contact data to include missing names, phone numbers, email addresses, postal addresses and more. Cross-matching fosters customer centricity, giving businesses a more complete customer view by adding missing components to their contact data.
When cross-matching is used in the identity verification process, contact records are validated, updated and completed, allowing businesses to leverage the data for improved analytics and targeted marketing efforts. Identity verification is an important process in Anti-Money Laundering (AML) and Know Your Customer (KYC) initiatives, which help protect businesses against fraud.
Financial Institutions often face new-customer identity verification challenges while trying to maintain a seamless customer experience. When dealing with customer data to complete the identity verification process, these organizations may find they are dealing with “bad data”. Bad or inaccurate data can happen for a variety of reasons, from typos and misspellings to outdated addresses, name changes or formatting issues. Cross-matching ensures customer data is accurate and consistent across many different attributes, giving businesses and financial institutions the security and confidence of knowing their customers are who they say they are.
With identity verification, centricity refers to determining which elements of an individual’s identity should be “centric” to your equation. Once this element is identified, centricity can be used to fill in the gaps and append additional information to your records.
Melissa’s identity verification solutions help businesses reduce risk, ensure compliance and keep customers happy with tools that verify identities, IDs/documents, authenticate age and perform watchlist screening. Personator, Melissa’s real-time identity verification service, matches empty contact fields to provide a single customer view. With just one piece of data, such as an email address, Melissa can find and verify additional information about that person including name, phone number, current mailing address, and more. Melissa’s advanced matching algorithms help to resolve additional missing data, reduce false positives and corroborate the customer’s identity. By combining unmatched global reference data with deep domain knowledge, Melissa provides fast identity verification and data validation at the point of entry to prevent bad data from entering your system.