AUTOMATION IN AML

1. AUTOMATION IN AML/CFT AND
ANTI –MONEY LAUNDERING
Presented By:Team 3

2. INTRODUCTION
• The war between the various financial sectors and money
laundering has led financial institutions to set up
technologically intelligent weapons.
• Thus, these institutions aim to use sophisticated analytical
technologies such as machine learning to combat various
financial crimes effectively.
• Furthermore, financial institutions are confronted with
criminals and regulators who are imposing increasingly
severe sanctions for offenses.
• Branches, which fear supervisor sanctions and damage to
their reputation, are more dedicated than ever in their
efforts to combat money laundering and the financing of
terrorism (AML / CFT).

3. MACHINE LEARNING IN ANTI-MONEY
LAUNDERING
• The compliance teams who are under all this pressure from
regulators believe that "machine learning" is the miracle
solution for the AML. Its usefulness is particularly
important for reducing the number of false positives (false
alerts) that are generated through traditional AML devices
• Thus, new technologies will play a potentially huge role in
the field of AML / CFT, but they cannot entirely replace
human judgment, which remains very important in these
processes. Therefore, the aim will be to enrich the existing
AML / CFT devices with the various technologies of artificial
intelligence (AI).

4. APPLICATIONS OF ARTIFICIAL INTELLIGENCE
TECHNOLOGIES FOR AML / CFT COMPLIANCE
New technologies that can be supported in AML devices
include:
i. AI and machine learning
ii. Robotic process automation (RPA) and intelligent
automation
iii. Analysis of unstructured data
iv. Natural language generation
v. Cloud-based analysis tools

5. ROBOTIC PROCESS AUTOMATION
(RPA) IN AML AND KYC (INTRO)
• According to a new market report published by Transparency
Market Research1, “ the market for robotic automation globally
is displaying a phenomenal growth of 60.50 percent compound
annual growth rate (CAGR) from 2014 to 2020, with the
valuation of the market predicted to reach US$5 billion by 2020,
increasing from US$0.18 billion in 2013.”
• Strategic predictions also talks about robotic automation having
an expanding role in the years to come2. Some of these project
the following by 2020:
 Robots will participate in five percent of all financial
transactions.
 Smart agents will facilitate 40 percent of mobile interactions.

6. ROBOTIC PROCESS AUTOMATION
(RPA) ADOPTION
• Financial institutions (FIs) are considering new technology tools to address challenges such as heightened
regulatory scrutiny and the increasing cost pressures that are affecting their anti-money laundering
(AML) and know your customer (KYC) processes.
• RPA is an easy-to-deploy solution that can help reduce manual tasks, streamline workflows,
improve compliance, and reduce costs. It is an ideal candidate for a field like compliance that is
predominantly rule-based and evolves constantly.
There are different styles of automation that an FI can adopt:
• Semi-automated processes: Automate a part of the process by adopting a model of humans and
bots working together to complete end-to-end tasks efficiently. For example: customer service
desks in any FI have service agents navigating through multiple systems to view customer
information whilst interacting with the customer. Bots can be used to help collect and access
information from multiple source systems to support human agents servicing client calls.
Another………… example would be processing a change of address on multiple systems. While the
initial checks / data can be entered by humans, bots can be used to process customer information
changes across multiple source systems.
• Fully automated processes: This style of automation would ensure that there are no human
touchpoints in the process. For example: updating customer contracts and multi-format message
creation.

7. PRESENT CHALLENGES IN AML/KYC

8. OPPOURTUNITIES OF RPA
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