Tag Archives: FATCA
In this video, Peter Ku, director of solution marketing, Global Financial Services, Informatica, discusses the data challenges associated with FATCA compliance.
The Foreign Account Tax Compliance Act (FATCA) was signed into U.S law in March 2010 and is coming into effect on January 1, 2014. The new law will require Foreign Financial Institutions to report the names of U.S. persons and owners of companies who have bank accounts in these banks for tax reporting and withholding purposes.
Peter answers the following questions:
1) What is FATCA and what are the requirements financial services companies must comply with?
2) What must financial institutions do to successfully meet these requirements?
3) What do financial institutions need to address the data-related challenges and comply with FATCA?
While Dodd Frank received most of the media attention after the great financial crisis, during that period, the U.S. government signed into law the Foreign Account Tax Compliance Act (FATCA) back in March 2010 which will require Foreign Financial Institutions (FFIs) to report the names of U.S. persons and owners of companies who have bank accounts in foreign accounts for tax reporting and withholding purposes.
The law was set to go into effect on January 1, 2013 however on October 24, 2012, the U.S. Internal Revenue Service (IRS) announced a one year extension to January 1, 2014 to give FFIs more time implement procedures for meeting the FATCA reporting requirements. Banks who elect not to comply or fail to meet these deadlines will be tagged as a ‘non-participating FFI’ and subject to a 30% withholding tax on all U.S. sourced income paid to it by a U.S. financial institution. Ouch!!
The reasons for FATCA are fairly straight forward. The United States Internal Revenue Service (IRS) wants to collect its share of tax revenue from individuals who have financial accounts and assets in overseas banks. According to industry studies, it is estimated that of the seven million U.S. citizens and green card holders who live or work outside the U.S., less than seven percent file tax returns. Officially, the intention of FATCA is not to raise additional tax revenue but to trace its missing, non-compliant taxpayers and return them to the U.S. tax system. Once FATCA goes into effect, the IRS expects it will collect an additional $8.7 billion in tax revenue.
Satisfying FATCA reporting requirements will require banks to identify:
- Any customer who may have an existing U.S. tax status.
- Customers who hold a U.S. citizenship or green card.
- Country of birth and residency.
- U.S.-based addresses associated with accounts – incoming and outgoing payments.
- Customers who have re-occurring payments to the U.S. including electronic transfers and recipient banks located in the U.S.
- Customers who have payments coming from the U.S. to banks abroad.
- Customers with high balances across retail banking, wealth management, asset management, Investment and Commercial Banking business lines.
Although these requirements sound simple enough, there are many data challenges to overcome including:
- Access to account information from core banking systems, customer management and relationship systems, payment systems, databases and desktops across multiple lines of business which can range into the hundreds, if not thousands of individual data sources.
- Data varying in different formats and structures including unstructured documents such as scanned images, PDFs, etc.
- Data quality errors including:
- Incomplete records: Data that is missing or unusable from the source system or file yet required for FATCA identification.
- Non-conforming record types: Data that is available in a non-standard format that does not integrate with data from other systems.
- Inconsistent values: Data values that give conflicting information or have different definitions with similar values.
- Inaccuracy: Data that is incorrect or out of date.
- Duplicates: Data records or attributes are repeated.
- Lack of Integrity: Data that is missing or not referenced in any system.
Most modern core banking systems have built in data validation checks to ensure that the right values are entered. Unfortunately, many banks continue to operate 20-30 year-old systems, many of which were custom built and lack upstream validation capabilities. In many cases, these data errors arise when combining ‘like’ data and information from multiple systems. Given the number of data sources and the volume of data that banks deal with, it will be important for FFIs to have capable technology to expedite and accurately profile FATCA source data to identify errors at the source as well as errors that occur as data is being combined and transformed for reporting purposes.
Another data quality challenge facing FFI’s will be to identify unique account holders while dealing with the following data anomalies:
- Deciphering names across different language (山田太郎 vs. Taro Yamada)
- Use of Nicknames (e.g. John, Jonathan, Johnny)
- Concatenation (e.g. Mary Anne vs. Maryanne)
- Prefix / Suffix (e.g. MacDonald vs. McDonald)
- Spelling error (e.g. Potter vs. Porter)
- Typographical error (e.g. Beth vs. Beht)
- Transcription error (e.g. Hannah vs. Hamah)
- Localization (e.g. Stanislav Milosovich vs. Stan Milo)
- Phonetic variations (e.g. Edinburgh – Edinborough)
- Transliteration (e.g. Kang vs. Kwang)
Attempting to perform these intricate data validations and matching processes requires technology that is purposely built for this function. Specifically, identity matching and resolution technology that leverages proven probabilistic, deterministic and fuzzy matching algorithms against any data of any language, capable of processing large data sets in a timely manner and that is designed to be used by business analysts versus an IT developer. Most importantly, being able to deliver the end results into the bank’s FATCA reporting systems and applications where the business needs it most.
As I stated earlier, FATCA impacts both U.S. and non-U.S. banks and is as important for the U.S. tax collectors as well as to the health of the global financial and economic markets. Even with the extended deadlines, those who lack capable data quality management processes, policies, standards and enabling technologies to deal with these data quality issues must act now or face the penalties defined by Uncle Sam.