Have you ever been a victim of financial fraud? It’s an unfortunate experience that can make you feel violated and helpless. But what if I told you that fraud detection through AI and computer vision (CV) technologies could help prevent such occurrences?
Computer vision refers to the capacity of a computer or machine to replicate the human visual ability. Precisely, AI fraud detection technologies based on CV analyze enormous financial data. Then, they identify potentially fraudulent activity before it occurs.
In this blog, we’ll explore how AI and computer vision revolutionize fraud detection in financial institutions.
Fraud is deliberate deception that fraudsters intentionally use to gain profit. It’s a severe crime affecting many people and businesses worldwide. For example, e-commerce losses to online payment fraud made up $41 billion worldwide in 2022, according to Statista.
That’s why banking, insurance, medical, government, law enforcement agencies, and public sectors actively use fraud detection systems to prevent fraud. For instance, fraud detection in banking involves identifying scams and preventing fraudsters from obtaining money through criminal activity.
More precisely, financial fraud detection relies on data mining, machine learning (ML), statistical data analysis, and artificial intelligence (AI) based techniques. For instance, as a field of artificial intelligence, AI computer vision helps people and businesses with fraud detection, aka fraud identification.
Let’s take a closer look at what computer vision, ML, deep learning, data mining, and statistical analytics mean.
Computer vision enables machines to interpret, analyze, and understand surrounding visual information. How? CV uses algorithms and deep learning models to train computers to identify and recognize image and video-related objects, patterns, and features. So, it can automatically identify potential fraudsters and scammers at ATMs and other physical locations.
Machine learning is a field of AI that uses data and algorithms to imitate the way humans learn. As a result, ML can improve its accuracy without human intervention. For instance, fintech companies use ML for financial risk assessment to determine whether someone qualifies for a loan.
Deep learning is an ML technique that teaches computers to learn by example, as humans do. For instance, trained with large data sets, deep learning helps banks find customers who want to become customers and utilize their credit cards. Specifically, deep learning helps banks pick the right questions to ask credit card applicants to identify the right customers.
Data mining involves analyzing and sorting through massive data sets to identify patterns and connections to solve problems. For instance, deep mining helps banks build credit scoring models to accept or reject a client’s credit.
Statistical analytics is the process of collecting and interpreting data to uncover trends. It helps remove bias from data evaluation. For example, banks rely on statistical analytics to estimate how many people would make deposits compared to those requesting loans.
Now, let’s get down to the most common types of fraud to stay informed and vigilant to avoid fraud and protect your finances.
Friendly fraud (aka first-party or accidental fraud) occurs most often. It happens when customers use their credit or debit cards to complete a legitimate purchase and then dispute the charge with their card issuer.
Thankfully, computer vision can pinpoint fraudulent transactions more accurately than fraud detection analysts to help catch more fraud and flag fewer false alarms.
This technology can analyze vast amounts of transaction data to detect behavior patterns associated with friendly fraud. These patterns can include a customer making a large purchase and disputing the charge shortly after. The technology can also identify unusual activities, such as a customer using a different device or location than usual.
AI computer vision can monitor transactions in real-time to detect suspicious activities. These activities can include a customer making a large purchase with a new card or from an unusual location.
This technology can also analyze customers’ interaction with a website or mobile app to create a unique customer profile. Then, the technology can use this profile to detect changes in behavior that may indicate friendly fraud.
CV can analyze chargeback data to identify trends that may indicate friendly fraud. Namely, the technology can examine and verify customer-related information, such as name and phone number, before accepting payment.
Thanks to biometric authentication, such as facial recognition, computer vision can verify the customer’s identity before allowing them to dispute a charge. As a result, only authorized individuals can dispute charges, preventing friendly fraud.
In 2022, Visa’s chief risk officer Paul Fabara said friendly fraud incidents had increased between 20% and 30% globally.
Identity theft occurs when a fraudster steals someone’s personal information and uses it for fraudulent purposes. Such information can include name, date of birth, Social Security number, and credit card data. Identity theft is a serious crime that results in fraud or deception, mainly for economic gain.
Identity thieves usually steal mail or trash, hack into computer systems, or use phishing scams to trick people into giving away their sensitive information. They can use this information for different purposes, such as opening new credit accounts, receiving loans, and filing fraudulent tax returns.
Thankfully, identity-theft detection algorithms of CV can determine customers’ trustworthiness based on their previous transaction behavior, geolocations, and IP addresses.
This technology can use biometric authentication based on facial recognition to verify the customer’s identity. After recognizing customers’ faces, it allows them to access their accounts.
AI-powered computer vision can boost the speed of fraud detection in real-time, which can’t be said about fraud analysts. Namely, it can monitor customer behavior in real-time to detect unusual login attempts or transactions and sudden increases in the frequency or number of transactions.
This technology can analyze customer data to detect a sudden change in their location or usual spending pattern.
It can verify identification documents, such as passports or driver’s licenses. In addition, it can compare document information to government databases to determine information accuracy.
AI computer vision can analyze customer data to identify potential vulnerabilities or risk factors that might lead to identity theft before it occurs. These factors can include having large debt; being denied loans, mortgages, and employment; or being unable to open a bank account.
The Federal Trade Commission’s (FTC) Consumer Sentinel Network received over 5.1 million reports in 2022. 46% of those reports related to fraud and 22% to identity theft. Specifically, credit card fraud represented 43.7% of identity thefts.
Investment fraud refers to criminals contacting people and convincing them to invest in nonexistent or worthless schemes or products. And when criminals receive payment, they cease contacting the victim.
AI computer vision algorithms can compare new investment opportunities with known schemes to identify fake strategies.
This technology can monitor investment activity and detect suspicious behavior. Namely, AI can flag fraud by analyzing patterns of investment activity and identifying anomalies, such as quick, astronomical profits.
Specifically, fraudsters try to trick people into investing in fake investment opportunities. For example, they might want their victims to invest in unreal stocks, bonds, notes, commodities, currency, or even real estate.
CV can identify fraudulent documents, such as fake financial statements or forged investment contracts. Specifically, the technology analyzes the documents for inconsistencies or irregularities, such as an unclear description of investment specifics.
AI computer vision can analyze social media posts, online conversations, and activity by comparing data and behaviors to known scam techniques. As a result, it can spot and distinguish investment fraud from regular transactions.
Specifically, certain behavior patterns can indicate fraud. For instance, investment criminals leave questions unanswered and create fake accounts and email addresses. Also, they link their posts to websites, videos, or photos that make the investment look legitimate.
CV can also predict potential investment fraud before it happens. Namely, the technology analyzes historical data, such as unrealistic high investment returns associated with known fraudsters, to indicate future fraudulent activity.
The Federal Trade Commission reports consumers lost the most money to investment scams in 2022. Specifically, the loss accounted for over $3.8 billion.
Credit card fraud happens when criminals steal and use credit card information for unauthorized purchases. Thankfully, AI computer vision algorithms can fight credit card fraud by detecting real-time fraudulent transactions.
CV can detect a sudden large transaction or a transaction in an unusual location. These anomalies can indicate possible fraud.
This technology can analyze transaction-related metadata, including time and amount, to identify fraud. For example, it can be suspicious if someone simultaneously completes multiple transactions from different locations.
CV can monitor user behavior, such as how long it takes someone to enter a PIN. Suspicious behavior like longer time spent on guessing and trying several PINs can require further investigation.
According to Statista, fraudulent transactions made with payment cards worldwide are forecast to increase to 38.5 billion by 2027.
Insurance fraud occurs when someone makes a false claim to receive an insurance payout. It may refer to individuals committing fraud against consumers and individuals committing fraud against insurance companies.
Specifically, insurance fraud against consumers is when insurance companies or agents commit deliberate deception, such as a false claim or promise, to obtain an illegitimate financial gain. And this can happen during the buying, using, selling, or underwriting insurance processes.
Insurance fraud against insurance companies involves deceptive acts against insurance companies by a person, usually for financial gain. For example, a policyholder may exaggerate a claim for a larger payout, resulting in fraud against the insurance company.
The good news is that computer vision helps with insurance fraud detection. How? Let’s read below.
This technology can analyze data and identify suspicious claims based on location, injury type, and previous claims history. So, insurance companies can investigate such doubtful cases early on to avoid paying out fraudulent claims.
CV can analyze images and videos submitted as part of a claim. As a result, it can ensure these images haven’t been digitally altered or videos haven’t been staged. Besides, it can analyze images to identify fraud-related anomalies, such as mismatched vehicle damage, inconsistent lighting, or irregular physical injuries.
This technology can monitor live video feeds from public areas, such as parking lots, highways, and intersections, to identify potential fraud. Specifically, it can analyze the footage in real-time and flag suspicious activity, such as staged accidents.
AI computer vision can use predictive analytics to identify fraud patterns based on historical data, such as exaggerated or unrealistic payout amounts. Namely, the technology can spot possible fraud by analyzing past claims data. As a result, insurance companies can adjust their underwriting practices and detect potential fraud before it occurs.
The Coalition Against Insurance Fraud reports that American consumers lose at least $308.6 billion annually to insurance fraud.
A phishing scam occurs when someone sends an email or text message as if it’s from a legitimate company. The aim is to trick victims into giving away sensitive information, such as usernames, passwords, and credit card details. Thankfully, AI computer vision helps fight phishing scams.
This technology can analyze images and logos to detect whether they’re authentic. It can spot even minor cha
nges in logos or images and flag them as potentially fake.
CV can analyze the language used in phishing emails. As a result, the technology can flag these emails as potential phishing scams.
Such analysis involves integrating computer vision and natural language processing (NLP). NLP uses AI and computer algorithms to enable computers to recognize, comprehend, generate, and respond to human language.
For instance, some devices clip onto glasses and use an optical sensor to combine computer vision and NLP. As a result, they take in the wearer’s surroundings and use language-modeling algorithms to describe what they see audibly.
CV can monitor user behavior to detect unusual behavior, such as a user suddenly clicking on suspicious links or visiting unfamiliar websites. The CV system will flag this activity as potentially malicious.
Computer vision can analyze website design and layout to detect irregularities. The point is that scammers often use fake websites that seem legitimate. Moreover, this technology can compare websites to known legitimate ones to determine their authenticity.
According to the 2022 Internet Crime Report by the Federal Bureau of Investigation (FBI), phishing schemes were the top crime type among 300,497 complaints.
Computer vision in AI is a field that applies machine learning to images to enable computers to classify objects and respond.
AI helps computers think, while CV helps them perceive and understand the environment. More precisely, computer vision enables systems and computers to derive resourceful information from various visual inputs, such as videos and digital images.
Machine learning computer vision can recognize features and data patterns, such as faces and behavior changes, to help detect fraud in various fields, such as finance. For instance, your smartphone uses this technology to unlock the screen when recognizing your face.
Namely, CV’s neural network imitates the human eye and can train models to function with the help of cameras, algorithms, and data. For instance, computer vision technology can analyze financial documents, digitize, classify, structure, and enter them into the system faster and more efficiently. As a result, companies can deploy computer vision applications more intensively for improved performance and fraud detection.
For example, CV helps with Know Your Customer (KYC) verification. As a result, more fintech can use facial recognition software to accurately confirm the customer’s identity when they submit IDs or communicate via video.
The most successful fraud detection models are based on historical data. Namely, they analyze large amounts of data regarding known fraudsters and their past actions to identify suspicious patterns and detect possible fraud.
Here are AI and computer vision tools that help financial institutions detect and prevent fraud, protecting both their customers and themselves:
Optical character recognition (text recognition) is a CV tool that extracts text from images. It can read and analyze financial documents like bank statements, invoices, and receipts. When used for fraud detection, OCR compares fraudulent document data with the original data to reveal false information.
Image analysis can also help detect fraudulent signatures, altered documents, or false identities.
For example, facial recognition software can compare a person’s image with a database of known secure identities to verify the document’s authenticity. Forensic document examination also uses AI to review signatures and handwriting. Forensic document examination is a subcategory that deals with document authenticity.
Data visualization is the process of representing data through charts, plots, infographics, and animations to help communicate complex data easily. These charts and graphs can help financial institutions quickly identify unusual transactions, identify potential fraudsters, and mitigate risk. Since human perception is fast at decoding shapes, colors, positions, and movement, detecting data linked with fraudulent cards becomes more effortless.
Machine learning algorithms can detect fraud by analyzing historical data and identifying fraud patterns. For example, these may refer to users changing their passwords and contact details and transactions falling outside the expected range.
Fraud detection is also associated with anomaly detection algorithms. The latter algorithms can flag unusual behavior patterns that could indicate fraudulent activity. For example, they can spot significantly more expensive transactions, more often, or completed outside of regular business hours.
Using AI for fraud detection means identifying patterns and anomalies in large datasets. This can help detect fraudulent activities in real-time.
The Federal Trade Commission reports consumers lost around $8.8 billion to fraud in 2022, over a 30% increase from 2021. Thankfully, computer vision and machine learning successfully help with fraud transaction detection.
For example, in banking, AI algorithms can identify transactions larger than a customer usually makes. This type of transaction can be indicative of fraud. Then, these algorithms can alert bank personnel or automatically prevent further fraud by blocking payments. Moreover, AI can analyze exaggerated or false claims or claims that don’t require registration to help insurance companies save millions of dollars.
As you read above, machine learning refers to teaching computers to learn and improve independently. For example, in finance, fraud detection machine learning helps identify patterns, such as transfers from unusual locations, which may flag as fraud.
As a subset of ML, computer vision application in fraud detection refers to analyzing visual data and identifying anomalies that may indicate fraud. These anomalies can include unusual money transfers.
Specifically, CV stands behind facial recognition, object recognition, anomaly or strange activity detection, and video monitoring or analytics. As a result, it helps the financial industry prevent fraudulent activities and protect consumers from financial harm. You can read the examples below.
Here is how ML helps with fraud detection:
The ability of computer vision to analyze images and videos enables companies and institutions in the financial industry to fight fraud successfully. Let’s look at the examples of computer vision in fraud detection.
CV can verify customer identity and document authenticities, such as IDs, passports, driver’s licenses, invoices, and expense receipts. The technology scans the document and compares it to known legitimate records in the database to determine its authenticity.
CV can verify customer identity using biometric data, such as faces or fingerprints. Namely, the technology compares the biometric data to known secure examples in the database for identity verification.
For example, some fintechs use facial recognition for account access. Computer vision compares the customer’s face to a stored image to ensure it’s the same person. The same applies to online transactions or transactions at physical points of sale (POS).
CVs used in fraud detection can recognize objects, such as currencies, IDs, credit cards, checks, and banknotes, to determine if they’re counterfeit. Specifically, the technology compares the object to known legitimate examples in the database to determine if it’s fake or not.
Computer vision can detect unusual activity. This can refer to someone accessing an ATM outside of regular business hours, covering the camera, using a skimmer, or making transactions at an unusual rate.
A skimmer is a card reader designed to look like part of an ATM. The device can fit over an ATM to collect card numbers and PIN codes so they can be replicated into counterfeit cards.
Precisely, cameras with video analytics are connected to computer vision algorithms. They use video surveillance to monitor activities, analyze real-time video feeds, and flag suspicious activities for further review.
AI and computer vision are transforming financial fraud detection by improving accuracy and efficiency.
Implementing AI computer vision in financial fraud detection can help save money by reducing false positives and minimizing losses from fraudulent activity. As a result, it can help fintech stay ahead of increasingly sophisticated fraudsters.
Artificial intelligence allows computers to “think,” and computer vision to “see.” Specifically, artificial intelligence computer vision enables computers to obtain meaningful data from visual inputs, such as photos and videos.
The insights gained from CV help fintech with fraud detection by closing suspicious accounts, revealing fraudsters, and verifying customer identity.
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