Virtually all existing risk assessment and monitoring systems are
built upon relational databases, which store information such as
counterparty, account, transaction, stakeholders, financial
instruments and derivatives in separate tables, one for each type of
business entity. The relational databases are great tools for
indexing and searching for data, as well as for supporting
transactions and performing basic statistical analysis; however, the
relational databases are poorly-equipped to connect across the
tables or business entities and identify hidden relationships and
risks from those relationships going across as many as 10 or more
layers of transactions, accounts, and persons.
Show more Using the relational
databases, in order to find potential connections, analysts need to
join a number of large tables to run the queries. Such queries could
take hours or even days to run, rendering any meaningful analysis of
linkages among parties and transactions practically impossible.
Assessing and monitoring risk also requires going beyond the
internal data for the individual account or person and connect
it up with the information from third party sources such as
OpenCorporates and the World-Check database from
Thomson Reuters containing information on Political Exposed
Persons (PEP) and the government-sanctioned entities. In
case of credit risk assessment, this means integrating non-
traditional data sources such as mobile wallet and
eCommerce transactions as well as microloan repayments.
The relational databases with their rigid schema are not well
suited for marrying the internal data with multiple external
data sources with ease.