Forecast 2023: Creating a multi-pronged approach to fight cyber fraud through AI and ML

Forecast 2023: Creating a multi-pronged approach to fight cyber fraud through AI and ML

Bureau Team
Bureau Team

January 11, 2023

Bureau Team

February 20, 2023

India’s shift towards a digital economy has been rapid in recent years. The onset of Covid-19 was a catalyst in this accelerated shift, particularly in the financial industry. As the world moved increasingly digital -- for some overnight -- cyber fraud inevitably became real and unavoidable threat. The increase in digital banking transactions and e-payments were directly proportional to the growth in fraudulent activity across digital channels.

Cyber fraud affects businesses of all sizes and is prevalent across industries. It can erode customer trust, damage reputation, and result in significant financial losses. According to PwC’s Global Economic Crime and Fraud Survey 2022, cybercrime is the biggest fraud threat facing most businesses today, especially from hackers and organized cybercrime syndicates. With the emergence of new-age technologies, most cybercriminals are armed to develop new, innovative, and untraceable ways to commit fraud.  

Cyberspace is vast, and while threats are analyzed daily, their evolving nature ensures that they remain a daunting concern for businesses. A need for new, faster, and more efficient technology becomes critical as the human capacity to respond to emerging threats is limited. One potential solution lies in the world of artificial intelligence. Businesses must adopt a proactive, holistic approach in leveraging customer account life cycle data and advanced algorithms to identify and nip known and unknown fraud patterns in real time. Hence, creating a multi-pronged approach using Artificial Intelligence (AI) & Machine learning (ML) turns out to be more efficient than traditional fraud detection methods, especially for businesses dealing with large amounts of data.

Behavioral Biometrics for frictionless authentication

Every person has unique traits that are difficult to capture or replicate. The way they type is as distinctive as their fingerprints. Behavioral biometrics uses these unique traits to authenticate users and protect them from fraud. Behavioral biometrics leverages machine learning to analyze patterns in human activity and identify whether someone is who they claim to be when interacting online or whether a bot drives the activity. It assesses the identity of online users based on their behaviors, such as how they type, swipe, and keystroke patterns. The data is then analyzed using AI and ML technologies and algorithms. When the user attempts to log in, they are assigned a risk score depending on how closely their behavior matches their earlier ones.  

Behavioral biometrics analyses a user's digital, physical and cognitive behavior to distinguish between fraudulent activity and legitimate customers. As soon as the system detects 'abnormal behaviors', responses are triggered in the background to conduct additional investigations and protect the legitimate user's account. This approach is generally used for detecting malicious activities, identifying compromised accounts, and flagging unusual activity that could be indicative of fraud. Behavioral biometrics can also provide a better user experience by eliminating the need for users to remember multiple passwords or go through security measures that can be intrusive, at times.

ML for faster and more accurate fraud detection

Despite the high incidence of fraudulent transactions, many businesses still resort to reviewing fraudulent transactions with legacy solutions. Rule-based linear processes are not only time-consuming but can also lead to high false positives in a bid to prevent fraud. This traditional approach towards identifying and authenticating customers makes the review process tedious and can lead to customer drop-offs. ML algorithms' ability to learn from historical fraud patterns and recognize them in future transactions makes them viable. They appear more effective than humans when it comes to processing information. Also, ML algorithms can find sophisticated fraud traits that a human cannot detect easily. They are very fast in adapting to changes in that normal behavior and can quickly identify patterns of fraudulent transactions. In most instances, they can identify potential fraudsters even when there hasn't been a fraudulent transaction yet.  

Persona-based Intelligence to detect anomalies

Your digital footprints can tell the story of who you are. Whether it is the websites you visit, the sites you shop from, or the way you transact online, each action leaves a footprint. When you add up all these individual actions over time, they form a persona – your virtual reflection. When a persona contains enough relevant information, it can be used to detect fraudulent behavior.

The algorithm already knows which persona the user exhibits, so even if the correct password is typed on the associated device, the system knows when bad actors are featuring a persona that does not belong to the actual user. This data can also prevent account takeover when a fraudster uses stolen or compromised credentials to access an account. Using persona-based security intelligence, organizations can detect when an account is being accessed from an unusual device or location, providing enough time to take action to prevent the fraudster from taking over the account.

Device Intelligence to identify high-risk devices

Fraudsters keep changing IP addresses or mimic settings on their pc or browser. The good news is that they behave abnormally such as using one device for many transactions or manipulating a device to facilitate an attack on an account owner's device. Device intelligence analyses various data points, including IP address, device ID, and geolocation data, to determine whether a device is being used by the real owner of the device or a fraudster. If a device is at high risk, it can be flagged for further review. It can prevent cybercriminals from using stolen or compromised devices to commit fraud.  

Key takeaways

AI-based fraud prevention systems are not industry specific. The only thing it needs is data to work. Hence, it can be deployed in various industries, ranging from banks, financial institutions, and e-commerce websites to even matrimonial and dating apps. As more and more people get online for their daily needs, data breaches and cyber fraud will increase. Consumers don't prefer going through additional security measures as they want a seamless and user-friendly experience on a digital platform. Hence it becomes even more crucial for businesses to deploy the latest automation and technologies to secure their platform and simultaneously offer a hassle-free experience for their users.  

This column was first published in INDIAai

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