Artificial intelligence has emerged as a powerful tool, prompting financial services companies to leverage it for fraud detection in the foreign exchange market. Is it as effective as they believe it to be?
The Forex Market Is a Target for Fraud
The foreign exchange (forex) market is the largest global financial market. It is decentralized, meaning no physical trades take place and no centralized regulatory agency oversees trading. This responsibility falls to banks, forex dealers, companies and investment management firms.
The forex market outdoes some of the largest stock exchanges in the world. In fact, its daily trading volume is approximately $6.6 trillion — over 200 times greater than the New York Stock Exchange’s $2–$6 billion daily trading volume.
A market as large and profitable as forex is a natural breeding ground for risk. The nature of forex means traders are at the behest of market fluctuations across various nations’ economies. The potentially tumultuous nature of forex trading means companies must be even more vigilant against fraudulent activities.
Examples of Forex Trading Fraud
The forex market’s low barrier to entry makes fraud more likely. Since it is open 24 hours per day, five days per week, it is more accessible than other exchanges. While front-end fraud tactics involve social engineering and deceit, back-end fraud leverages scripts and bots.
Money Laundering
The forex market acts as an intermediary for making illegally obtained currency appear to have come from a legitimate source. Regulators hold businesses accountable for this type of fraud if it occurs on their systems, meaning they are liable to be shut down.
Chargeback Fraud
If fraudsters use stolen credit cards and the cardholder requests the funds back, the intermediary has to pay out. Alternatively, someone can engage in chargeback fraud after using their own credit card to trade legitimately.
Account Takeover
Fraudsters frequently leverage social engineering tactics to take over existing accounts and engage in fraudulent activity unnoticed. Often, identifying them is challenging because the account activity initially appears legitimate.
Promotional Abuse Fraud
Individuals creating new accounts en masse — manually or with the help of bots — to repeatedly take advantage of signup-related promotional offers are engaging in a form of forex fraud that can be damaging to the intermediary.
How Has AI Emerged as a Fraud Detection Tool?
Since forex is the largest global financial market, it generates tremendous data. Processing and analyzing all relevant information was practically impossible — at least until the introduction of artificial intelligence.
One survey of financial services companies revealed the emerging trend of AI use. When asked which use case they were investing in, three in 10 respondents said they were using it to detect transaction and payment fraud — a 310% year-over-year increase.
According to the same survey, two in 10 respondents said they were investing in AI for anti-money laundering and know-your-customer procedures. These are measures for detecting illegally obtained currency and verifying traders’ identities to prevent fraud.
The Integration of AI into the Forex Market
There are multiple ways financial services companies can leverage AI to combat trading fraud.
- Fraud Detection
One of the reasons AI has emerged as a leading detection tool is because of its speed. Machine learning models can rapidly analyze large data sets for hidden patterns — mainly fraud indicators — at high speeds, outpacing any manual alternative.
- Threat Simulation
Financial services firms can simulate fraud or anomalous activity to develop an effective incident response to likely scenarios. This approach also helps them prepare for unforeseen market conditions and respond to unlikely fraudulent activity.
- Anomaly Detection
AI can detect abnormal activity for manual review. For example, it can flag when a trader begins behaving differently or alert professionals to a suspiciously large sum of currency being traded rapidly. These instances of unusual activity often indicate fraud.
- Threat Identification
Unlike other fraud detection tools, AI enables predictive analytics. It can anticipate when previously discovered and unknown threats will appear by analyzing historical and current data. This helps financial services brands proactively combat trading fraud.
- Identity Verification
Account takeover, impersonation, promotional abuse and chargeback fraud all relate to customers’ activity patterns. Financial services companies can use machine learning models to identify hidden behavioral trends, streamlining fraud detection.
The Benefits of Using AI to Detect Fraud
Financial services companies using AI for forex fraud detection will benefit from a significantly improved response speed in regard to suspicious transactions. The algorithm can rapidly interpret data, meaning professionals get a near-immediate alert.
AI also substantially improves detection accuracy since it’s incapable of human error. It constantly references a massive data set during analysis — unlike humans, who can operate on memory alone.
Another significant benefit of AI-driven fraud detection is timeliness. Since algorithms operate without manual intervention or oversight, they can continue functioning 24 hours per day, seven days per week, to detect threats sooner than any manual approach.
Considerations for AI Implementation
While AI is an effective tool, its use comes with drawbacks. Before implementation, financial services institutions should account for security, privacy and functional considerations.
- Continual Data Collection
As the forex market sees new traders daily and its currency exchange rate fluctuates, accurate fraud detection is challenging without a constant data stream. In these circumstances, algorithms can only remain accurate if they continually receive relevant, high-quality information.
- Overreliance on AI
Many financial services businesses mistakenly assume AI can operate independently of governance. While human intervention and oversight are largely unnecessary, machine learning models must be routinely audited, as their data intake may eventually affect their performance.
- Data Privacy Concerns
When financial services firms collect, analyze and store customers’ details, they become obligated to regulatory oversight. As such, not addressing data security and privacy concerns is a mistake.
A generative model may function unexpectedly, divulging personal information in its output. Alternatively, poorly secured storage systems are prone to breaches and data exfiltration. There are numerous ways the addition of AI technology can open organizations up to cyber threats.
The Importance of Implementation Considerations
Financial services enterprises, investment management firms and forex dealers should ensure they have enough data scientists, proper technology infrastructure, and well-trained security professionals to safeguard their systems and combat trading fraud. Otherwise, they risk regulatory, financial and reputational damages.
Devin Partida is an industrial tech writer and the Editor-in-Chief of ReHack.com, a digital magazine for all things technology, big data, cryptocurrency, and more. To read more from Devin, please check out the site.
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