COMPLIANCE
automate significant parts of operations but
also offer superior insights through
advanced capabilities for analyzing struc-
tured and unstructured data.” 1
These dynamic models focus on patterns
rather than individual data points or trans-
actions. They detect anomalies, making it
easier to identify behavior that truly
accounts for malicious activities. Dynamic
models enable institutions to keep pace
with changing requirements while also
resolving the costly problem of reducing
false positives.
Integrating non-traditional data sources
into a data management program will
improve the effectiveness of detection and
ongoing due diligence. Non-traditional
internal and external data sources can
include documents, newsfeeds, images,
video, social media, clickstream data and
machine log data. Driving the growth of
these variable data sources are an increase
in client interactions and the digitalization
of business processes.
While these new data sources offer a
wealth of information for AML and compli-
ance purposes, traditional structured
query language-based analytic techniques
may not be well suited for these non-tradi-
tional data sources because their pre-set
schemas vary and change often. For this
reason, an alternative approach for analyz-
ing data uses programming languages
such as Java, Python and R. These coding
languages are often chosen for big data
and analytical tools for several different
reasons. For example, Python has become
a popular choice for applications because
it relies on the most cutting-edge tech-
niques, such as AI, machine learning and
natural language processing.
The ability to integrate non-traditional
internal and external data sources enables
institutions to go beyond basic analytics to
identify risk more quickly and efficiently.
When transaction data is enriched with
client/legal entity data (including names,
addresses and other identifiers), and pub-
licly available OFAC lists, banks can track
transactions to determine if they were
1
completed by known high-risk individuals or non-cooperative jurisdictions. Going one
step further, enriching this data with verbal and written communications information
can help cast a wider net when looking at potential indicators.
Tip of the iceberg
Fighting crime with big data and analytics still has a long way to go. Industry pioneers
who wish to move beyond analytic technologies are looking toward cutting-edge solu-
tions based on probability and inductive, heuristic logic that detects money launder-
ing by replicating an analyst’s thought processes. This is the future state of advanced
capabilities that institutions require to address comprehensive AML and compliance
challenges in a dynamic environment. With the right investment in the right technol-
ogy and data platforms, institutions can be confident that they have a clear view of
risk across the enterprise.
Carol Stabile, CAMS, chief sales and marketing officer, Safe Banking Systems,
Mineola, NY, USA, carol.stabile@safe-banking .com
Arin Ray and Neil Katkov, “Artificial Intelligence in KYC-AML: Enabling the Next Level of Operational Efficiency,” Celent, August 22, 2016, https://
www.celent.com/insights/567701809
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