As technology advances, so do the schemes employed by fraudsters, presenting a growing challenge for the financial services industry. The critical need to strengthen fraud detection and prevention mechanisms has driven financial institutions to adopt innovative tools to fight fraud more effectively.

One such innovation that has proven highly efficient is artificial intelligence. Read on to learn more about financial fraud and how AI helps detect and prevent it.

Understanding the landscape of financial fraud

Recent technology advancements and the widespread adoption of digital platforms for financial transactions led to a dramatic increase in financial fraud. Credit card fraud, identity theft, and check forgery have been augmented by phishing, ransomware, and data breaches.

These digital threats have not only targeted individuals but also financial institutions, leading to substantial financial losses, compromised customer data, and reputational damage.

Types of financial fraud in banking

1. Credit card fraud
One of the most common types of financial fraud, credit card fraud, involves fraudulent use of credit or debit card data to make unauthorised purchases or withdraw funds. Fraudsters obtain credit card information in various ways, including data breaches, phishing scams, and skimming devices. Once they acquire the necessary data, they can make purchases or cash withdrawals without the cardholder’s knowledge.

2. Identity theft
This type of fraud refers to the unauthorised use of someone else’s personal information to access their financial accounts, make transactions, or get credit in their name. Like credit card fraud, everything starts with accessing personal information, usually through phishing emails, data breaches, or physical theft of documents. Once the fraudsters have the necessary information, they can open new accounts, apply for loans, or make fraudulent transactions.

3. Account takeover
Account takeover involves gaining unauthorised access to a victim’s bank account or online banking profile, usually by obtaining login credentials through phishing attacks or malware. Account takeovers can result in unauthorised fund transfers, unauthorised changes to user account information, or draining of the account balance.

4. Phishing attacks
A phishing attack is a practice of tricking individuals into revealing their sensitive data, such as login credentials or financial details, through deceptive emails, messages, or websites. A phishing message prompts the recipient to open a malicious link or provide confidential information, which is used to carry out various fraudulent activities.

The cost of fraud: impact on banks and customers

According to Statista, in 2022, fraudulent payments and bank transfer fraud resulted in $1.59 billion in losses in the U.S. Another research revealed that 96% of financial organisations suffered losses to fraud, with 24% of total respondents having fallen victim to breaches that resulted in seven-figure losses, and 3% of respondents having lost over $10 million in the same year.

However, the cost of fraud extends beyond financial losses. For banks, fraudulent activities lead to losing customer trust and damaging their reputation. Moreover, fraud usually results in increased operational costs, legal fees, and regulatory fines, further straining the financial stability of the institution.

And how about customers? Besides direct financial losses, they suffer from their personal information being compromised. For customers, fraud can also result in damaged credit scores, financial instability, and the time-consuming process of resolving fraudulent transactions, adversely affecting their trust and confidence in the banking system.

The traditional approach to fraud prevention

The traditional approach to fraud prevention involves a combination of manual and technology-driven steps. Some of the most common traditional methods of fraud prevention include:

• Manual verification: This involves human intervention to verify the authenticity of financial transactions and customer identities. Manual verification often includes reviewing documents, conducting in-person identity verifications, and cross-referencing information.
• Transaction monitoring: Banks and other financial institutions closely monitor customer transactions for any unusual or suspicious activity. This involves analysing transaction patterns, amounts, and frequencies to spot deviations from normal behaviour.
• Rule-based methodology: This approach involves setting rules and parameters to identify the likelihood of fraud. The key elements of the rule-based approach include geographic restrictions, behaviour analysis, time-based rules, and transaction limits.
• Security cameras: Surveillance cameras are used to detect fraudulent activities at bank branches and ATMs, helping identify unauthorised or suspicious behaviour.
• Multi-factor authentication: This involves employing multiple verification methods to authenticate the identity of a user before giving them access to a system or approving a transaction, typically including a combination of something the user knows (a PIN or password), something the user has (a security token or a one-time passcode), or something the user is (biometric authentication).
• Fraud training: Providing comprehensive training to bank employees about various fraud schemes can help them recognise potential fraud indicators and take necessary preventive actions. Educating customers can also contribute to fraud prevention efforts.

While the traditional approach has been effective to some extent, the ever-growing amount of online transactions and the rapid evolution of digital technologies call for more advanced fraud prevention strategies. And this is where artificial intelligence comes into play.

The rise of AI in banking

AI (artificial intelligence) refers to simulating human brain processes with the help of computer systems, including learning, reasoning, and self-correction. Artificial intelligence is a broad term covering a wide range of technologies, such as robotics, computer vision, natural language processing (NLP), machine learning (ML), deep learning (DL), expert systems, and others. 

In recent years, the banking industry has significantly shifted towards integrating AI-driven solutions to streamline operations and optimise customer-centric services. By adopting AI, banks can considerably improve customer service, risk management, financial fraud detection, and more. 

Why use AI in banking fraud detection?

1. Real-time detection

AI-powered fraud detection systems can detect and flag suspicious activities in real-time, enabling banks to promptly respond to potential fraud threats. By continuously monitoring transactions and user behaviours, AI can immediately identify anomalies and deviations from normal patterns, minimising the fraud risk going undetected.

2. Improved accuracy

Using AI for fraud detection has significantly enhanced the accuracy of existing mechanisms, reducing the occurrence of false positives and false negatives. Advanced machine learning models can adapt to evolving fraud patterns and continuously improve their predictive capabilities, ensuring more precise identification of fraudulent transactions and abnormal behaviours.

3. Cost reduction

Implementing AI-driven fraud detection solutions minimises the need for manual intervention and extensive human resources, optimising operational efficiency and resource allocation.

4. Enhanced customer experience

Employing AI for financial fraud detection not only strengthens the security measures of financial institutions but also helps enhance customer experience. By using AI for fraud detection, organisations can build trust and confidence with customers, fostering stronger relationships and loyalty.

Key AI technologies in fraud prevention

As it has been already mentioned, AI encompasses a broader range of technologies. Let’s look deeper at the key artificial intelligence technologies used in banking fraud detection.

Machine learning algorithms

ML algorithms, which include supervised learning, unsupervised learning, and deep learning, perform a vital role in analysing vast datasets to identify patterns and anomalies indicating fraudulent behaviour. 

• Supervised learning algorithms can detect potential fraud in real-time by leveraging historical transactional data and identifying known fraud patterns.
• On the other hand, unsupervised learning algorithms identify new patterns, helping uncover previously undetected fraudulent activities.
• Deep learning enables the analysis of complex and unstructured data, effectively identifying sophisticated fraudulent schemes.

Natural language processing

Natural language processing (NLP) is another critical AI technology in banking fraud detection. It allows for analysing and interpreting unstructured textual data from various sources, including emails, chat messages, and social media, to detect fraudulent activities and identify potential threats.

Behavioural analytics

AI-based fraud detection systems employ behavioural analytics to monitor and analyse user behaviour and transaction patterns to detect deviations and flag potentially fraudulent activities in real-time.

Predictive modelling

Predictive modelling enables financial institutions to anticipate potential fraud risks by analysing historical data and patterns to predict and prevent future fraudulent activities. By leveraging predictive models, institutions can proactively implement preventive measures and mitigate potential risks.

AI in fraud prevention: real-world examples

Many leading financial institutions are already using AI-powered systems for fraud detection to enhance their security measures. Here are some real-life examples of utilising AI in fraud prevention:

• JPMorgan Chase: JPMorgan Chase has integrated AI and machine learning algorithms to spot and prevent fraudulent activities within their banking operations. The bank has developed an AI system called COiN, which can analyse legal documents and extract relevant information in seconds. The bank also uses AI to monitor transactions and flag suspicious activities, for example, money laundering or identity theft.
• PayPal: PayPal uses AI-powered technologies to analyse various types of data, such as device, email, IP, phone, transaction, and behavioural user information, to help identify and stop fraud. Among other technologies, PayPal leverages machine learning tools to train its models with thousands of good and bad transactions and help spot suspicious online behaviour. In addition, the company utilises NLP to analyse customer interactions across different channels, including emails, phone calls, or chatbots, to extract relevant information or anomalies.
• Mastercard: Mastercard uses AI-driven fraud detection systems to monitor and analyse transactional patterns across its network. The company has developed a platform that employs machine learning to examine multiple factors, such as location, device, merchant, and behaviour, to determine the likelihood of fraud. The platform can also learn from each transaction and adapt to changing patterns.
• Experian: A new service from Experian, Mule Score, is aimed at helping banks and building societies detect and shut down ‘money mule’ accounts used for housing fraudulently obtained funds. As per Experian’s data, 42% of first-party current account fraud is now related to money mules, with a 13% increase in the fraud rate for current accounts in the first three months of the year. The service analyses account opening history and turnover activity, utilising bureau data and characteristics from over 200,000 confirmed mule cases to help banks identify potentially fraudulent account activity in their portfolios. In trials, the system accurately identified over 50% of the highest-risk mule accounts.
• Capital One: Capital One utilises AI to detect and prevent fraudulent activities across its banking and financial services. The bank has implemented a machine-learning model called Eno. Eno lets customers know when suspicious activity is detected on their accounts.
• Bank of America: Bank of America has adopted AI to provide personalised financial advice and security to its customers. The bank has launched an AI assistant called Erica, which can help customers with various tasks, such as checking balances, paying bills, or sending money. In addition, Erica alerts customers to potential fraud and helps them take action.

Final thoughts

It’s hard to overestimate the role of artificial intelligence in fraud detection. Compared to traditional fraud detection methods in banking, AI-based systems offer unprecedented accuracy and efficiency, reducing costs and ensuring better customer experiences. 

By leveraging machine learning algorithms, predictive modelling, natural language processing, and behavioural analytics, organisations can effectively mitigate fraud, building trust and loyalty with their clients.

At DeepInspire, we have 20+ years of experience creating complex financial products. We also deliver AI software development services, which makes us a perfect partner for building AI-powered fraud prevention systems. Contact us today to schedule a call and discuss your project.