Cyber Defense eMagazine February Edition for 2023

Cloud vendors' features can be deployed quickly, the algorithms used are highly trained and optimized,
have already proven effective, can be tailored to some extent, and are updated.For example, Google
developed a smart analytics design pattern that helps users build a scalable real-time fraud detection
solution in one hour. The serverless feature can provide fraud notifications and dashboards to monitor
the performance of the fraud detection pipeline.
Cloud-ready anti-fraud models can be very cost-efficient. However, they do have limitations. Most cloud
vendors mainly provide credit card fraud detection ML. Other types of fraud, such as internal fraud, may
require the development of custom ML models.
On the other hand, with ML-as-a-service, your company can develop a unique algorithm custom-made
for your particular business needs. Depending on the complexity of your business, these models may be
more or less expensive than ML in the cloud. The main benefit of having a company build its ML model
is ownership.
Both cloud anti-fraud ML models and ML-as-a-service, require some level of expertise. It's critical for any
company to understand the basics of anti-fraud ML before venturing into this journey.
Finally, the most challenging road, especially for startups that do not have a resourceful team of data
scientists, is creating, building, training, testing, and deploying their own ML anti-fraud model. While this
can be expensive and time-consuming, it is highly customizable, and you will own your own technology.
How to build ML anti-fraud models
How exactly do anti-fraud AI-ML technologies work? As mentioned before, the main goal of your ML
model will be to know your customers, suppliers, workers, and partners. The first step is to identify fraud
patterns in your organization.
ML is trained to recognize “normal” behavior by double-checking key data of financial transactions. ML
models analyze a wide range of different data, checking it against historical data, and can identify wether
the operation aligns with “normal” behaviour. If it does not, ML solutions will flag and shut down the
operation. For example, the ML model may check where the transaction originates and whether it
matches where the customer lives.
Most companies run fraud programs to deter, prevent, and resolve online payment fraud. However, it's a
good idea to consider expanding the models to include possible internal fraud, corporate fraud, payroll,
overbilling, or inventory fraud. These types of fraud are common and are not perpetrated by consumers
but happen from within your company.
Monitoring fraud activity for workers, partners, suppliers, and other business contacts can help you create
a holistic program that works on all levels. Once you have identified fraud patterns and defined your
program's scope, you can start defining your data sets—what data you will use in the anti-fraud ML model.
When you use your historical database, you should choose the data that best helps you understand your
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