As per recent reports, the U.S. retail sector lost $60 billion to payments fraud. This is a big loss suffered by retailers in the USA alone in just one year. However, these frauds could be avoided up to an extent with the use of artificial intelligence.
Leading retailers like Amazon, Walmart, Target, Walgreens, and Home Depot are already using AI to detect and prevent fraudulent activities. If you are also a retailer and want to know how you can use AI to fight fraud, this blog is for you!
In this blog, we have explained how AI can help in detecting suspicious activities and alerts to prevent any kind of retail fraud. So, let’s start.
How Frauds Take Place in Retail?
Frauds are very common in retail, whether it is in the USA or any other part of the world. These frauds not only affect retailers financially but also damage their reputation in the market.
Here are the following ways in which the majority of frauds take place in the retail industry:
1. Return Fraud
In this type of fraud, customers return the products (which are generally stolen, fake, or discarded) without any valid reason, using fake bills and receipts. Later, they ask for a refund or sometimes exchange it for other products.
2. Shoplifting
Shoplifting is one of the most common tactics used for fraud in retail stores. In this, customers take the products without paying for them, ultimately causing financial losses to the retail owners.
3. Price Tag Switching
Customers simply switch price tags of expensive products with cheaper ones. When the cashier scans the barcode, it shows less price and customers pay a reduced price for the expensive product.
4. Employee Theft
This is internal fraud committed by the employees of the retail store. Employees steal the products and later sell them on the black market. Some other employee thefts include stealing cash, manipulating sales data, misusing gift cards, etc.
5. Credit Card Fraud
In credit card fraud, customers use stolen or counterfeited credit cards to buy products from retailers. This not only results in financial loss for retail owners but also makes them vulnerable to legal charges for fraudulent transactions.
6. Gift Card Fraud
Sometimes, customers misuse the gift cards to fraud the retailers. They create fake gift cards by hacking into retailer’s databases and later use these cards for shopping.
7. E-Commerce Scams
Lastly, with the growth of the e-commerce industry, e-commerce scams are also increasing. E-commerce scams include people creating fake websites in the name of original retail websites, processing fraudulent transactions, phishing emails, offering heavy discounts, claiming gift cards and memberships, etc., and asking for sensitive information in return.
In e-commerce scams, retailers mostly do not face financial losses directly, but their brand image suffers a lot. Imagine, someone created a fake Walmart website and fraud people. The effect of this would be severe on Walmart’s brand image.
Now that you have understood the types of retail fraud, let’s see how AI can help in detecting and preventing these.
How AI Detects and Prevents Fraud in Retail
Artificial intelligence plays a vital role in detecting and preventing retail fraud. The machine learning models powered by advanced algorithms process data and detect suspicious factors in real-time, helping retailers detect and prevent fraud.
Let’s understand it in detail.
1. Real-time Transaction Monitoring
Retailers can use artificial intelligence-powered systems to identify suspicious patterns in financial transactions, and that too in real-time.
The machine learning algorithms are trained with patterns of purchasing behavior, location, average amount spent, and more. When there are activities that seem unusual, the algorithms flag them as suspicious, and the AI systems halt the transaction and alert the retailers to investigate it.
This way, AI can help tackle transaction-related frauds, credit card frauds, and e-commerce frauds in the retail sector.
2. Image Recognition and Fake Product Detection
Another way to tackle fraud in retail is to leverage image recognition tools powered by computer vision, a subset of AI.
These tools can help in detecting counterfeit products, which customers usually exchange in return for original products. Retailers can analyze images of products, logos, and packaging to spot irregularities, proving that the product is fake.
With this, retailers can prevent the exchange of fake products, tackle return frauds, and safeguard their brand image, and financial losses.
3. Predictive Analytics for Customer Behavior
User behavior helps significantly in detecting fraud cases, especially in retail.
For instance, a customer usually shops with an average ticket size of $100 and pays through a credit card. Suddenly, the same shopper makes purchases (same items but in large quantities) worth $200,000 and chooses to pay through netbanking. Seems something fishy, isn’t it?
This is a visible case of potential fraud. However, predictive analytics, another subset of AI, analyzes the customer’s past shopping behavior and can identify indicators that not are easy to detect, and alert retailers to proactively prevent fraud before it gets too late.
4. Behavioral Biometrics
In continuation of analyzing customer behavior in e-commerce, AI systems powered by predictive analysis can also create unique behavioral profiles for each customer using information like typing speed, mouse movement, and the way they browse the website.
If there is an unexpected suspicious change in this behavior, the system flags it as potential fraud and alerts the retailer. This is how fraud can be detected and prevented in e-commerce by analyzing customer behavior.
5. Enhanced Identity Verification
AI can also detect and prevent credit card fraud and gift card fraud in retail through identity verification processes, such as facial recognition, voice biometrics, and document verification.
AI algorithms can verify the customer making purchases online through the former identity verification processes.
Moreover, retailers can empower identity verification by adding blockchain technology to make their e-commerce more secure and reduce the risk of identity theft and account takeovers.
6. Natural Language Processing to Identify Fraudulent Reviews
Fake reviews are not direct fraud but affect the sales of retail businesses significantly. Almost every user makes the purchase decision by getting influenced by the product or the retail outlet reviews.
Some competitors hire experts to put fake reviews on the profiles of the retail businesses so that customers do not buy from them, and the total sales and brand image are affected.
To tackle this, retailers can leverage AI systems powered by natural language processing (NLP) to detect and filter out fake reviews that mislead customers.
NLP can analyze patterns in review language, the sentiment behind the review, and the frequency of reviews from the same online ID. If the system flags it fake, the fraudulent content gets removed which might be harming the brand’s reputation.
7. Supply Chain Fraud Detection
Supply chain frauds affect retailers financially. Some suppliers and retail employees send fake consignments, manipulate inventories, and modify invoices to commit fraud.
Therefore, retailers must leverage AI development services to build custom systems to monitor and analyze data from the supply chain to identify these types of fraud. AI can significantly help maintain a smooth supply chain and protect retailers from fraud and employee theft.
8. Self-Learning Algorithms
Last but not least, the AI models can continuously learn from previous fraud attempts in the retail industry.
With the power of adaptive AI, retailers can empower AI models to gain insights from the patterns and techniques of past frauds and detect other retail frauds, like shoplifting and price tag switching more quickly, effectively, and accurately.
Limitations of AI in Detecting and Preventing Fraud
AI is one of the best technologies to detect and prevent fraud in the retail sector. However, it also has some limitations that retailers must take into consideration if they are implementing AI to meet the same objectives (detecting and preventing fraud).
Following are the AI’s limitations in detecting fraud in the retail industry:
1. Data Quality Issues
AI models heavily depend on the data to detect fraud in retail. So, a common challenge that arises is the quality of data.
If the data inconsistent, outdated, or inaccurate, the AI model cannot be able to detect fraud accurately, and ultimately it will lead to false positives or missed fraudulent activities.
2. False Positives and Negatives
In many situations, AI may flag legitimate transactions as fraudulent. This scenario is called false positives. Moreover, it would also fail to detect actual fraud, commonly known as false negatives.
Both of these scenarios can affect the retail businesses. False positives can lead to rejected transactions, resulting in poor customer experiences, whereas false negatives allow actual fraudulent activities to occur, without being noticed.
3. Bias in Algorithms
In a few cases, AI can reflect biases present in the data it is trained on. So, if some customer demographics are misrepresented in the data, it may inaccurately flag their transactions as potential fraud.
In conclusion, this will be seen as discrimination and may lead to customer dissatisfaction.
4. Privacy Concerns
AI systems deal with a large amount of data that includes both personal and transactional data. Therefore, in many cases, there are concerns regarding privacy and data protection.
Cases of data misuse, either intentionally or accidentally, can ruin the customers’ trust in the retail brand. The brand may also face legal charges like violation of data protection regulations like GDPR.
5. Dependency on Humans
No matter how advanced fraud detection and prevention AI systems are leveraged in retail, it still requires human intervention to study flagged fraudulent transactions and activities.
However, in some cases, humans may sometimes miss the fraudulent activities, which can lead to fraud, resulting in losses to the retail businesses.
Conclusion
With artificial intelligence, retailers can tackle different types of fraud and prevent them by timely intervention. However, it is crucial to train AI models on high-quality data and frequently upgrade their datasets and algorithms to get complete benefits of AI in fraud detection and prevention.
To implement AI in your retail business, reach out to Quytech, the top AI development company. We have helped numerous retailers tackle frauds with our top-notch AI models and solutions, crafted to meet their specific requirements. For more information, visit www.quytech.com