According to the DigitalOcean report, 37% of participants raised their cybersecurity budgets in order to purchase higher-quality security software. In the battle against digital fraud, AI fraud detection is clearly a significant breakthrough. So, What is AI fraud detection?
What makes many companies take it? How does it work? This article already provides all the necessary answers.
In addition to these, TechDictionary also offers some tips on setting up the most effective fraud detection AI system. Let’s find out now!
What is AI Fraud Detection?
Artificial intelligence (AI) fraud detection means using a group of algorithms to find and prevent fraudulent threats before they actually happen. These algorithms are trained with large volumes of data to identify suspicious patterns or anomalies indicating fraud.
As Artificial intelligence (AI) in fraud detection keeps learning and updating, its accuracy rate heightens to overcome the most recent fraudulent tactics. Since this defense mechanism is useful in maintaining transaction integrity and security, you can find AI fraud detection in banking, finance, insurance, and other industries.
How Does AI Work In Fraud Detection?
As mentioned above, fraud detection AI works by using various machine learning algorithms to detect fraudulent behaviors. Below are the detailed steps for you to understand this process more clearly:
Step 1: Collecting Data
The procedure starts by gathering different behavioral and transactional data for the AI models to process. This consists of information about both legitimate and fraudulent actions.
Step 2: Categorizing Data
From the data, AI models start selecting relevant fraudulent symptoms. The training goes on until the AI fraud detection systems become agile and versatile enough for the business.
Step 3: Anomaly detection
With every new transaction entering the system, AI fraud detectors use statistical techniques to detect behaviors different from standard legitimate patterns.
Step 4: Flagging and Reporting
When detecting fraudulent behaviors, fraud detection AI quickly flags them as suspicious and reports them for further investigation.
Step 5: Continuous Learning
AI used in fraud detection needs continuous updating with new data to keep up with the ever-changing fraud environment and new fraudulent forms. This also helps fine-tune these AI algorithms’ parameters, making their detection more accurate and effective.
The Pros & Cons Of AI in Fraud Detection
From its definition and mechanism, you may grasp one or two things about AI fraud detection’s strength. Still, everything has two sides. Let’s see which benefits this defense mechanism has and what challenges await it.
Pros
Using AI in fraud detection offers many advantages for all kinds of businesses. Here are the 6 biggest benefits:
- Real-time monitor: AI fraud detection systems work 24/7, immediately detecting and preventing any suspicious activity. Therefore, this defense mechanism stops any fraud attempts before they affect the business, minimizing potential losses.
- High accuracy rate: Analyzing a huge amount of data and identifying anomalies is challenging if manually done. But AI models do it with lower chances of errors. Moreover, these algorithms can detect complex fraudulent behaviors that humans may miss, like fraud involving multiple accounts or different channels.
- Increased efficiency: Instead of spending hours doing repetitive tasks manually, let fraud detection AI automate them all, from reviewing transactions to verifying identities. Then, you can focus on other strategic tasks requiring human expertise.
- Cost reduction: Besides saving long-term costs from fraud losses or reputational damage, this AI model even helps you reduce the financial need for manual review teams. The savings can go to other necessary areas like investments.
- Scalability: AI in fraud detection can easily expand its monitoring capacities without increasing the number of staff. Moreover, as these algorithms evolve, your business will remain protected without additional costs.
- Customer trust: AI-powered fraud management systems guarantee a safe environment for customers, enhancing their trust and loyalty in your company. A reputation for security also helps attract new customers who want to protect their personal information.
Cons
As AI models are still evolving, researchers encounter several obstacles along the way. TechDictionary has listed 6 disadvantages fraud detection AI must overcome:
- Evolving threats: Fraudsters keep developing new tactics to pass through AI fraud detection, especially social frauds like phishing and social engineering. These social engineering threats aren’t automated, AI models struggle when combating them. So, besides regularly updating your system, you also need to train your employees to avoid these frauds.
- False positives: This is a common mistake with AI fraud detection, as the detector can flag legitimate behaviors as fraudulent, especially when customers use uncommon browsers and VPNs.
- Lack of transparency: Since AI in fraud detection has to process lots of data, it’s hard to understand its process, especially when it’s combined with machine learning or neural networks.
- Improper data quality: Training fraud detection AI requires high-quality data since inaccurate, biased, or outdated data can negatively affect the system’s performance, causing unfair treatment to certain customer segments.
- Incompatibility with current systems: Sometimes, AI fraud detection systems may not be compatible with your company’s existing infrastructure. In that case, you’ll have to upgrade or completely overhaul the current system, costing lots of time, effort, and resources.
- Regulatory compliance: Any AI fraud detector has to comply with regulations and laws regarding data and privacy protection, such as the General Data Protection Regulation (GDPR).
Real Scenario of Fraud Detection AI
With all the pros and cons above, AI models really change the insight in combating fraud. Below, we will look at how 4 big industries use AI in fraud detection:
Banking & Financial Services
AI fraud detection in banking and financial services usually verifies customers’ identities. The algorithms continuously monitor accounts and analyze all transactions to detect even the smallest anomalies, like sudden overseas transactions or abnormal money withdrawals.
One example is the AI-powered behavioral analytics in Paypal, a famous financial technology company. Its AI models analyze users’ behaviors, transaction history, and other relevant information to build a unique profile for each customer.
From there, if a transaction deviates from regular behaviors in the profile, the system will mark it as an anomaly, preventing unauthorized transactions.
E-Commerce
In e-commerce, AI fraud detection protects the shopping experience by monitoring customers’ purchase history, like transaction frequency and size. This way, it can prevent return-and-refund fraud and minimize card-not-present fraud.
Rakuten France, a large e-commerce marketplace in France, uses fraud detection AI to protect its website from real-time attacks and preserve its bandwidth.
Online Gaming
Recently, the rising online gaming industry has faced various fraudulent activities, like identity theft, cheating through bots or hacks, and payment fraud. Using AI in fraud detection can track the transactions’ frequency or original location, detecting uncommon patterns that indicate account takeovers or money laundering.
Aside from enhancing players’ trust and the game’s revenue, this method also guarantees all game assets are legitimately used, resulting in an equal gaming environment.
Carpooling Community
The carpooling community is also a target for online fraud due to its rich database. BlaBlaCar once faced the same situation when numerous fraudsters tried to get in and steal personal data from its members.
AI fraud detection helps BlaBlaCar by ensuring no user information is sent from incoming BlaBlaCar traffic. Not to mention, this system doesn’t worsen the user experience or the stability of the website or mobile apps.
How To Set Up AI in Fraud Detection
AI fraud detection only reaches its maximum efficiency with an effective methodical approach. TechDictionary has 7 tips for a strong operational framework to overcome all challenges:
1. Establish A Fraud Management Team
While fraud detection AI runs automatically, you still need a dedicated team to oversee its implementation and maintenance. Including members from all departments, like compliance, data science, IT, legal, and operations, helps you have a wider understanding of how the system aligns with your business’s objectives and results.
2. Build a Fraud Detection Strategy
Although effective, using AI in fraud detection shouldn’t be your only defense layer. A complex strategy, including AI models and other measures, like anomaly detection, multi-factor authentication, and encryption, will heighten your defense against fraud.
3. Choose the Right Model
An AI fraud detection system compatible with your current systems will save you a lot of time and money. Make sure the system is also scalable and well-supported with suitable platforms.
4. Test with Simulate Attacks
Before employing AI in fraud detection, test the system with several simulated attack scenarios, like red team exercises or penetration testing (a simulated attack to identifies system defensive weaknesses that attackers could exploit). This way, you’ll uncover its weaknesses and fix them in time.
5. Ensure Ethical Data Usage
Many customers nowadays are concerned about privacy protection, so make sure your fraud detection AI aligns with their ethical standards. For example, your system must comply with laws and regulations of data protection.
You should also maintain security in storing and handling private information and other practices concerning data collection.
6. Regularly Monitor and Update
You should construct a maintenance routine to keep your AI fraud detection accurate, effective, and relevant. The routine consists of regularly monitoring, updating, and training with new data.
7. Provide Security Trainings
Since several social frauds can pass through fraud detection AI, it’s necessary to train your employees on detecting fraudulent behaviors, like phishing attempts or abnormal financial requests. When your employees understand how their role relates to the company’s system, your security defense will become much stronger.
Conclusion
Online engineering threats become more and more dangerous over time. To counter them, you need to understand what is AI fraud detection and other related matters. This way, no fraudsters can penetrate your defense.
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