Page 58 - CMA Journal (July-August 2025)

Machine Learning for

Focus Section
Financial Fraud Detection

History shows that financial fraud remains a significant account networks by
and growing problem in today’s world, causing billions of examining connec-
dollars in losses for businesses, governments, and tions between vari-
individuals every year. The complicated structure of ous entities, includ-
modern financial systems, combined with increasingly ing shared contact
sophisticated fraud tactics, means that traditional information, IP
methods of detecting fraud through simple rules are no addresses, or tele-
longer enough and require out-of-the-box solutions to phone numbers. This
detect financial fraud and enable remedial actions. approach is referred
to as network or
Machine Learning (ML) stands out as a revolutionary
graph analysis.
approach, offering the capability to identify concealed
patterns, adapt to emerging fraud techniques, and (2) Immediate
provide immediate analysis. This discussion examines Processing and
how ML is utilized in financial fraud detection, Muhammad Sarfraz
emphasizing important methodologies, obstacles, and Action Arshad, FCMA
the future of advanced fraud prevention systems. The rapid pace of finan- Chief Financial Officer
Machine learning functions as a very powerful method cial transactions dem- Craft Industrial Co., Saudi Arabia
for addressing monetary fraud in recent times. Its ands fraud detection
capability to process huge volumes of data and recognize systems capable of real-time operation. ML models can
complex patterns provides significant advantages over evaluate hundreds of thousands of transactions within
traditional rule-based approaches. milliseconds, assigning risk ratings to each. This enables
Machine learning in finance is a type of artificial financial organizations to:
intelligence (AI) that has recently gained popularity. It a) Prompt Transaction Obstructing/Blocking: When
allows computing algorithms to work with vast amounts transactions receive elevated financial risk ratings,
of data efficiently and at low cost. (Source: the systems can automatically prevent completion
https://unthinkable.fm/how-does-machine-learning-guide before fraudulent activities occur. This blocks the
-in-finance/)
transaction in real time and prevents losses of
How Machine Learning Contributes to billions of dollars.
Reducing Financial Fraud
b) Request Additional Authentication: For
(1) Detecting Patterns and Unusual Activity
moderately risky transactions, systems can initiate
Conventional fraud detection relies on fixed rules, additional verification requests, including one-time
including “alert for transactions exceeding $1,000” or codes or confirmation messages to cardholders.
“prevent purchases from international locations.” However,
criminals continuously modify their approaches. Machine (3) Continuous Learning for Changing Threats
learning addresses this constraint through:
Fraudulent methods constantly evolve. ML models are
a) Unusual Activity Detection: ML systems learn from capable of continuously learning and adjusting to new
extensive historical data to establish typical behavior schemes by automatically processing fresh data. This
patterns for individuals or organizations. This represents a significant benefit over static rule-based
encompasses purchasing behaviors, transaction systems requiring manual modifications. Through
locations, timing, and purchase categories. When
reinforcement or adaptive learning processes, ML models
transactions significantly differ from established can modify their algorithms for improved effectiveness
patterns—such as a minor purchase in one location
over time, even against previously unknown threats.
immediately followed by a major purchase across the
globe— the system identifies it as questionable. This (4) Minimized Incorrect Alerts
represents unsupervised learning, where models It is generally observed that traditional fraud detection
detect irregular patterns without pre-categorized
methods face the most significant challenges with
information. “incorrect alerts,” where legitimate transactions are
b) Pattern Identification: ML systems can discover very mistakenly identified as fraudulent. This creates
subtle and complex relationships that remain hidden frustrating customer experiences and business losses.
from human observation. They can identify fraudulent Machine learning addresses this through:
56 ICMA’s Chartered Management Accountant, Jul-Aug 2025