As global spending in e-commerce explodes, so does the scope of fraud. The global E-Commerce market is expected to reach $4.9 trillion by 2021. With such large stakes, it’s hardly surprising that the criminals will target the money trail criss-crossing across the globe. Private organizations along with Federal, local, and state law enforcement agencies reported a staggering 3 million cases of identity theft in 2019 with financial losses reported in a quarter of the cases.
This resulted in almost 500,000 complaints with the IC3 (Internet Crime Complaint Center), and 2019 saw some of the heaviest financial losses caused by data fraud. Recent data from a survey indicates that the respondents lost a cumulative US $42bn with nearly 13% losing more than US$50 million. Despite the huge financial losses, only about a half of the victims conducted an investigation. Online Fraud detection is an increasingly persistent requirement for e-commerce businesses. For support, you can find extensive resources at IT Support Vermont.
What is fraud detection?
Defrauding people is a practice nearly as old as humanity. However with transactions limited between a handful of people the scope of fraud was generally limited, unless one got embroiled in a huge Ponzi scheme or criminal activities at a larger scale. However the scale of e-commerce connects people globally in transactions that amount to millions per second.
Fraudsters now see the advantage of leveraging digital tools and technologies to perpetrate fraud at a global scale using the e-commerce platform and raking in millions for very little effort. With the mushrooming of e-shopping, online banking, and online insurance, fraudsters now simply monitor the systems for any chance of vulnerability that they can exploit. The scale of these systems have also proved to be their Achilles’ heel.
Most often criminals manage to get away with full exploitation of a loophole before the organization can manage to patch up the vulnerability. In a matter of seconds companies can stand to lose sensitive data worth millions of dollars as well as sustain financial losses. Needless to say, fraud has turned into a major headache for e-commerce retailers and financial organizations across the globe.
How to build a defense system against fraud?
Preventing, detecting, and eliminating fraud requires intelligent implementation of the right defensive tools and technologies. This requires more sophisticated mechanisms than say combating malware. One of the best ways to implement a rock solid defense mechanism against fraud is through machine learning development services. IT Consulting Vermont can help you develop and implement the same in your business.
Machine learning is already being leveraged successfully in use cases such as detection of email spam and accurate product recommendations for billions. In conjunction with big data, rapid advances in statistical modeling, increased processing power and more technological advances, Machine learning is poised for a great leap in development and significant improvements shortly. Many businesses are betting big on Machine learning for effective fraud detection and mitigation. In this article we will explore how AI and machine learning can be leveraged for fraud detection and mitigation.
Keys to Using AI and Machine Learning in Fraud Detection
Understand the context clearly
Machine learning and big data analytics for fraud detection are not necessarily plug and play solutions. So it is up to your businesses IT security teams to understand the context of data collection before you set out to collect and analyze data. You need to clearly spell out the exact use case of each technology in fraud detection, the results you hope to achieve, the requisite resource allocation and the ROI you seek. Even if you have a vague idea of all of these, it is advisable to conduct a Discovery Phase that lets you validate your ideas and provide proof for your assumptions.
Invest in developing a big data engineering ecosystem
Having the right big data engineering ecosystem in place will allow you to collect, integrate, store, and process data from (usually) siloed data sources. To this end, you could make use of tools like Dataflow, Apache Beam, AWS Glue, or Spark. While starting with big data analysis is the first big step, you need to move quickly in order to build your data lake and data warehouse solutions.
Leveraging Data modeling with large data sets
The data leveraged for fraud analysis comes from a variety of sources including web proxies, firewalls, authentication systems, transaction processing systems, payment and billing systems, databases, business applications, and more. This means that you will need to implement data engineering in order to convert the raw data into prepared data. This prepared data will also need feature engineering to tune it and create the features necessary for building the models to feed the learning algorithm. The new data ecosystem needs to be monitored closely for data quality and quantity, running ETL procedures (extract, transform, and load) etc.
Always remember that the accuracy of results achieved from machine learning in fraud detection and cybercrime prevention will depend on the size and finetuning of your training dataset. Apart from the necessary data parsing and technical clean-up, your data scientists should also employ further refining techniques as per your unique business requirements.
Select a robust machine learning solution to fit your business needs
You could invest time and money in building a scalable and efficient machine learning solution. The alternative is to rely on reputed third-party ML solutions, such as the ML SaaS solutions offered by Google, Microsoft, Amazon, and IBM. The creation and deployment of ML models can be effectively handled with the help of packages like Amazon SageMaker, Amazon ML, Google Cloud AI Platform, Azure Machine Learning Studio, or IBM Watson Machine Learning.
Make sure you select appropriate algorithms and models
There is no one-size-fits-all in different business use cases when it comes to machine learning algorithms. It is up to each company to re-train models with new data sets and continuously tweak them for better results. When it comes to fraud prevention, companies can choose to opt for either unsupervised or supervised machine learning.
These distinct formats can be used as a standalone or even be combined for the construction of more sophisticated algorithms. At a basic level, unsupervised learning can be distinguished as the kind of learning enabled by unlabeled data where you only use input data with no output variables. Examples of reputed algorithms in unsupervised learning include K-Means clustering, Singular value decomposition (SVD), Apriori etc.
Supervised learning, on the other hand, makes use of “labeled” training data for predicting the output. Popular algorithms in supervised learning fraud detection include XGBoost, k-nearest neighbors (KNN), decision trees, random forests etc. Managed IT Services Vermont can help you develop the right algorithm for your specific business case.
