Risk Intelligence databases, used by thousands of financial institutions as part of AML, fraud investigation and anti-corruption compliance programs, are believed to be the best way to identify public negative news or adverse media information. Unfortunately, that is not true.
What are “risk intelligence” databases and how are they constructed?
Risk intelligence databases gather negative news data from public information sources like media articles and government enforcement and regulatory publications. Database companies attempt to identify and organize this adverse media information related to financial crime risk into structured data (imagine a spreadsheet of names) lists of individuals, businesses, and government entities alleged or convicted of engaging in some sort of unlawful activity such as money laundering, fraud, drug trafficking or hundreds of other highly risky endeavors.
The Risk Intelligence database typically provides its users with what is called a “profile” – a brief summary explaining the reason a database vendor believes the person or entity poses financial crime risk.
Information in these databases must be pulled together, sifted through and selected for inclusion by people whose job it is, is read piles of publications and pick out names they believe may be of interest to AML and financial crime compliance professionals. These employees then go through a manual process of typing information, creating URL links, and categorizing the reported offenses.
Herein lies the problem.
Risk Intelligence databases are constrained by the limits of human effort, attention, and consistency.
The amount of negative news information about people and entities involved in financial crime is too large to fathom. So, database vendors must selectively choose which negative news information to review and they must rely on humans to read, record, input and create profiles. Such reliance on employees, who are scattered around the world, means error and omission occur every day (as is depicted in this article). Risk data is missed because these database companies cannot hire enough people to read the new information entering the public every day. And even in those cases were a person can read an article about an alleged financial crime, how certain can you be that all of the relevant information was identified and entered into the database accurately.
Moreover, the leading risk intelligence databases market that they have more than 2,500,000 “profiles” (i.e. list of names) but this is a claim with little meaning. Sure, 2,500,000 is better than 1,000,000, but what if the public information domain contains enough data where there should be 10 million or 20 million or 50 million such profiles?
A far better approach is for AML compliance professionals to use an application that finds all relevant negative news in real time enabling analyst to make better decisions faster and with much stronger supporting evidence.