Combating Fraud with Machine Learning
With more people than ever before transacting their business online, the team at F33 has been fielding increasingly larger numbers of inquiries from enterprises who are concerned about fraud and how it’s affecting their bottom line.
The problems are exacerbated by the fact that criminals are able to connect with companies from any location in the world, with plenty of time on their hands to look for weaknesses. Businesses are finding that they need help in machine learning, with specific capability to detect anomalies
We know that artificial intelligence is ideal for such work, since it’s proven at pattern recognition and making connections difficult for ordinary humans to process themselves.
What’s needed is an experienced team to design and then integrate a ML system with the enterprise’s existing computational setup. That’s what F33’s experts do day in and day out for our customers.
Consider that approximately $3.31 billion was paid to fraudsters in cases reported to the United States Federal Trade Commission in 2020, up from $1.9 billion in 2019, according to Statista. The stakes are extremely high.
Read on for insight into how you can harness machine learning to stop criminals from destroying your bottom line.
No Time to Waste
Speed is of the essence when it comes to countering online fraud. Enterprises do not have any time to wait, looking for signs of fraud only after it has occurred.
Once criminals have a person’s credit card or other banking details, there’s nothing in place to prevent them from conducting transactions fraudulently.
As Towards Data Science noted, the stakes are only increasing. “Banks and insurance companies need tools and techniques to detect fraud in real-time to take appropriate actions.”
A wide range of parameters go into these banking transactions, including GPS location when done with a smartphone or laptop, the time of the transaction, who the merchant is and historical background of previous transactions.
Only a machine learning system can actually monitor these activities as they are occurring. So computer science professionals develop algorithms to help monitor and process these financial transactions. As the system notices anomalies, it can put a halt to the proceedings, sending up red flags to prompt human operators to intervene.
While it’s obviously useful for a bank’s internal security team and members of law enforcement to obtain historical records of crimes, actually spotting criminals trying to steal from an institution in real time is much more effective at eating away at fraud.
Challenges to Address in Stopping Computer Fraud
Creating a system to combat online fraud involves a huge amount of data. Medium explains that “Building a fraud detection model is a machine learning task which deals with a difficult problem caused by having a high-dimensional feature space.”
One major issue is that less than 1% of financial transactions are fraudulent. That means there is a bigger potential for ML to register a higher false positive rate. That’s where machine learning professionals, such as the experts at F33, can be of assistance.
You have to improve the financial fraud detection accuracy rate, which takes into consideration data labeling, assigning confidence levels to information and conducting complex comparisons of real-time behavior with previous trends.
If the system isn’t accurate enough, you run the risk of bothering and alienating honest customers who get dinged with false hits of potential fraud. Per Medium, “Deep learning models could help to reduce false positive rate (which means less customer insult) and reduce chargebacks and fraud, when compared to traditional machine learning methods.”
The Human Factor
We won’t rely entirely on computers to handle fraud detection autonomously. Manual review is called for, or ordinary people will not be able to feel they can trust how the ML system is operating. Business 2 Community notes that “Significant reduction of human effort is the main aim of data scientists in implementing ML. Even with modern analytics tools, it takes a lot of time for humans to read, collect, categorize and analyze the data.”
Machine learning here has to do with machines figuring out how to identify patterns and determine their true relevance/importance on their own, taking on the rote work humans can’t accomplish fast enough themselves.
Motivations for Fraud
Business experts refer to the Fraud Triangle, created by Donald R. Cressey, as noted by Forbes. The triangle consists of three elements that make fraud possible:
* Opportunity to commit fraud
* Pressure of a problem or other motivation to commit fraud
* Rationalization to seize on a reason that the rewards of fraud are higher than the potential for being caught and punished
With online criminals having plenty of time and ability to penetrate systems and motivation to earn money quickly with as little effort as possible, the fraud triangle becomes substantial. Forbes comments that in the aftermath of the global coronavirus and the economic chaos that resulted from shutdowns, the motivation to commit fraud only grows.
Fraud can spring up from within. Currently, “many organizations are preparing plants, sites, stores or office spaces for employees to come back to work, which may mean an uptick in the number of vendors or transactions taking place,” Forbes reminds us.
That poses a new kind of problem. “An employee might see an opportunity to commit fraud by hiring a vendor who’s a friend and taking a kickback in return, thinking it would go undiscovered in the commotion surrounding reopening.”
This type of fraud has been occurring since companies first began, but COVID-19 financial pressure means enterprises need to be particularly vigilant. ML fraud detection could be what stands between profitability and having to lay off people if payroll can’t support them.
Anomaly detection algorithms, enhanced with ongoing testing and refinement, can raise red flags about possible employee fraud just as they can help counter fraud conducted by criminals impersonating customers.
Cutting Down on Damage From Fraud Using Machine Learning
As long as people are using login credentials to set appointments, buy items, and otherwise conduct business online, it’s safe to assume bad actors will be out there, trying to test systems for weaknesses. The issue of fraud will be with us, but enterprises can take steps to fight back. To learn more about artificial intelligence and machine learning in combating fraudsters, get in touch with F33 today.