In 1994, a young Russian programmer and his partners hacked1 the system of a major US bank, stealing over 10 million dollars in 40 separate transactions. The perpetrators accessed the institution’s cash management computer system by exploiting the telecommunications network and using stolen IDs and passwords. Believed to be the first instance of online theft, the scandal ignited the need for fraud detection systems and standards, and it’s been an increasingly uphill battle into the current age of anti-fraud efforts..
In 2021 alone, consumers lost over 5.8 billion dollars2 to fraud—a staggering 70% higher than the year prior.
Fraudulent activities have become a persistent problem, in some cases causing substantial financial loss and reputational damage. As a result, fraud detection has become a priority issue for companies across various industries, particularly in the financial sector.
Defining Fraud Detection
Fraud detection is a process or set of activities that monitor, identify, and prevent illegal activity committed by bad actors. This process involves analyzing data and detecting behavioral patterns that suggest fraudulent actions. Its practice is prevalent in virtually all sectors, particularly those involving financial transactions, such as credit card companies, insurance providers, financial technology companies, and banks. A few prevalent examples of fraud include:
Identity Theft Acquiring a victim’s personal information without their consent, such as their social security number, name, and address (among others). Typically, this stolen information is used to make unauthorized transactions, set up bank accounts, or additional lines of credit, unbeknownst to the victim. In some cases, these accounts can be linked to active money laundering attempts, as well as other nefarious activity like Human Trafficking, and drug trafficking.
Phishing Tricking a victim into giving their personal information, phishing techniques typically come in the form of an unassuming email, text, or chat message, asking the user to click a link or provide details through manipulation or scare tactics.
Credit Card Fraud Involves stealing credit card information and making unauthorized purchases through the victim’s credit card.
Common Countermeasures of Fraud Detection
Fraud detection has evolved drastically over the years from manual, hands-on systems to sophisticated, automated technology like real-time fraud detection via machine learning models. Some common methods used today include:
Transaction Monitoring Transaction monitoring involves the monitoring and detection of patterns or outliers in financial transactions through the use of algorithms and automated systems. Transaction monitoring is a process that analyzes financial data in real-time, alerting an organization when it detects suspicious or possibly fraudulent activity based on foundational information collected on the account at the time of opening (KYC), and periodic checks or updates as the account matures over time (CDD/EDD). Such activities include:
Transactions involving unusually large amounts of money
Transactions taking place in high-risk locations or with suspected entities
Transactions that fall outside the normal use or direction of the account
Rules-Based Detection Rules-based detection utilizes a pre-defined set of criteria or rules for fraud identification. It relies on these rules or thresholds to detect whether a transaction is potentially fraudulent. If one of the rules state, for example, that a cash transfer must be evaluated if the amount exceeds a specified dollar amount, then the system will automatically alert the company to review the transaction in real-time.
Machine Learning A technique that utilizes algorithms, analytics, and statistical models to identify patterns of possible fraud within large volumes of financial transactions. Machine learning systems used for fraud identification are flexible; they are “trained” based on real transactional information at an institution or organization, that can then adapt to changing patterns over time and offer organizations a cost-friendly, scalable, and more accurate means of detecting and preventing fraud.
Limitations of Traditional Fraud Detection Methods
While traditional fraud detection methods are still essential in bank fraud investigations, they have limitations. Here are some of the cons:
False Positives Using more basic fraud identification systems like rules-based detection can result in false positives, causing both backlogs in the system, while also potentially preventing genuine transactions that don’t necessarily meet the rules put in place. A client, for instance, transferring a large fund amount (legitimately) could be blocked by the system, or flagged for follow-up investigation. The older, more manual methods can also act as a detriment due in part to human error, and outdated rules thresholds
Slow Detection Times Fraud detection alerts triggered by old rulescan pile up, making it harder for fraud analysts to review transactions manually. This increases lead times, causing delays in the fraud identification process and the overall customer experience. In recent years, this has become a growing problem for banks and fintechs, resulting in customer attrition.
Inability to Detect New Types of Fraud Traditional fraud detection methods are limited by the parameters they are bound by. Without systematic updates and reviews, older models are not be able to detect new, more sophisticated techniques and could have exploitable loopholes.
Explaining Real-Time Fraud Detection
Real-time fraud detection is a process that monitors, spots, and prevents fraudulent activities as they happen. It relies on machine learning to analyze large volumes of transactions in real time, taking only milliseconds to detect anomalies.
In today’s fast-paced digital age, real-time fraud detection processes are vital. Bad actors are emboldened with the assistance of technology to attempt larger fraudulent activities with real-time detriment to institutions and customers alike. The need for organizations to spot an anomaly in real-time has never been higher.
Real-time fraud detection constantly monitors customer activity as it happens, alerting an organization immediately once suspicious behavior occurs. Because it runs on machine learning algorithms, you can feed the system more training data, improving its effectiveness and accuracy over time.
Running a real-time system is also more cost-effective and scalable, as it can handle the more tedious, repetitive tasks with little to no risk of human-induced error. This approach allows companies to prioritize their internal talent and focus on more comprehensive investigative projects that expand beyond the current capabilities of ML models. The combination of ML + human-in-the-loop processes allows for additional streamlining and reduced overhead costs for institutions of all sizes.
Real-Time Fraud Detection Techniques
Real-time fraud detection comprises a variety of AI-based techniques that are utilized for different purposes. Here are some examples:
Network Analysis Network Analysis looks at patterns of interactions and connections across networks to detect possible fraudulent activity. For example, in a credit card transaction, the entities would be the credit card owner/customer, the merchant, and the card issuer of the credit card. Network analysis studies the typical interactions of these entities with each other and, over time, can identify abnormal activity between them.
Anomaly Detection Anomaly detection is a technique to spot suspicious transactions or patterns outside the normal expected activity of an account. Like network analysis, it uses machine learning algorithms to process large amounts of financial data, spotting unusual behavior and can lead to identifying and defining evolving fraud typoligies.
Behavioral Analysis This technique focuses on user behavior as a basis for real-time fraud detection. It looks at pertinent details such as transaction history and device information to identify unusual patterns or red flags. Behavioral analysis might look at sudden fluctuations in a client’s financial transactions or whether the customer’s device contains illegal software. Additionally, analysis of this nature may also look at identifiers like physical device usage, and a host of biometric characteristics associated to the account (i.e. does the customer normally log in via mobile or desktop, do they physically type in a username and password vs autofilling one on their device.
Implementing Real-Time Fraud Detection
Establishing and implementing effective real-time fraud detection within an organization requires proper infrastructure, system integration, and highly skilled professionals. Here are some important aspects to consider when doing so:
Infrastructure Requirements Aside from machine learning frameworks and AI technologies, implementing real-time fraud detection will require specialized, high-performance computer hardware, large data storage, and a sound security infrastructure, among other things. While more cost-effective than hiring human hands, companies need experts to run and maintain these systems.
Integration with Existing Systems To prevent fraud more effectively, companies must integrate their real-time fraud detection process with existing systems such as databases and transaction processing systems. This enables organizations to leverage existing data and allows different internal departments to work together to prevent fraud.
Staffing and Training Real-time fraud prevention is technically demanding, requiring highly trained experts and strong collaboration. Thus, hiring experienced professionals in related fields such as cybersecurity, finance, and data analysis is vital. Organizations must follow through and provide proper skills training on different techniques, systems, and emerging fraud issues.
Fraud is an accelerating, ever-evolving threat that financial institutions must continuously monitor and combat to protect their customers and maintain their reputation. Traditional methods for fraud detection have limitations that often result in missed fraudulent activity or false positives. The advent of real-time fraud detection techniques has dramatically enhanced fraud detection capabilities, allowing for faster detection times and the ability to detect new types of fraud.
By implementing real-time fraud detection technologies, financial institutions can stay ahead of evolving fraud tactics and provide customers with a more secure experience. Companies must recognize the benefits of real-time fraud detection and take steps to integrate these technologies into their existing systems to stay ahead of the constantly evolving threats.
Fighting Fraud with You+Us
Fraud detection can save a company billions of dollars in losses. However, establishing a solid fraud detection system can be challenging, requiring significant resources, effort, and time. This is why organizations outsource financial services and related fintech solutions to proven and trusted partners with considerable experience in the space.
Let’s build a safer financial space together.
Because at TaskUs, we offer the best of both worlds. By combining top-level operators and purpose-built technology, we deliver the strongest collaboration of tools, training, and processes to deter, combat, and ultimately thwart cybercriminals. We provide world-class risk management solutions, such as real-time fraud detection, so you and your clients can transact worry-free. Recognized as the Everest Group’s World’s Fastest Business Process (outsourcing) Service Provider in 2022 and highly rated in the Gartner Peer Review, TaskUs is responsible for providing Ridiculously Good fraud detection services to companies.
Learn more about our fraud detection capabilities.
Pragya is a financial crime practitioner with 20+ years of experience helping banks and financial institutions meet their regulatory requirements. She previously helped established Genpact’s FCRM service line, built & deployed risk and compliance services for multiple clients. She is CAMs certified.
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