Implementing Fraud Detection for Financial Institutions

We hear about sophisticated technology hacks almost every day. 

Fraudsters are constantly looking for new ways to outsmart traditional defenses, often utilizing the rapid advancement in new tech like AI. And to a degree, these bad actors remain one step ahead of institutions and cybersecurity teams. According to the FTC, fraud caused losses of more than $10 billion USD in 2023.

To help mitigate and prevent financial fraud, financial institutions can set up fraud detection systems and best practices. This can include various protocols that detect suspicious activity and behavior. Financial fraud detection helps protect assets, ensure regulatory compliance, and maintain customer trust.

This article teaches you all about financial fraud detection and discusses some of the most common challenges you'll encounter when implementing it. You'll learn what to do when faced with these challenges and how to select the right fraud detection tools and systems.

Financial Fraud Detection Overview

Financial fraud detection empowers organizations to safeguard their assets by identifying and mitigating fraudulent activities and identities. Modern fraud detection systems often use sophisticated technologies, like AI and machine learning (ML) algorithms, to minimize financial losses, ensure compliance, and maintain integrity. To achieve this, companies undertake a variety of activities, including real-time transaction monitoring, advanced algorithms, biometric verification, behavioral analysis, and device fingerprinting.

Two types of financial fraud detection exist—real-time detection and retrospective detection:

Real-time detection occurs immediately after a transaction, allowing for quick intervention if someone detects fraudulent activity. While this can prevent financial losses, it's costly and complex to implement as it requires a more sophisticated infrastructure and processing capabilities. It can also lead to false positives. 

For example, a tool like Apache Kafka can help process and analyze transaction data streams in real time. Prove’s Identity Flow can verify a user's identity in real time.

Retrospective detection focuses on analyzing past data and identifying fraud patterns, enabling in-depth analysis. Retrospective detection discovers frauds only afterward and, thus, can't directly prevent losses from happening.

Optimal fraud prevention arises from the synergy of real-time and retrospective detection methods.

Financial Fraud Detection Challenges

Given the highly complex nature of financial fraud, detecting it can be challenging. The task is even harder because fraud tactics keep changing, which means the detection systems must also change.

Let's look at some of the most common problems you'll face when implementing financial fraud detection and the ways to solve them.

Evolving Fraud Tactics

As mentioned in the previous section, financial fraud is constantly evolving, spurred on by rapid technological advancements. For instance, generative AI allows fraudsters to create fake documents, emails, and even images and videos. Making these fake documents is quite easy. The documents and images look more real than ever. This makes it hard to tell what is real and what is fake.

Fraudsters can also use adversarial ML, a technique used to confuse and mislead ML models. Adversarial ML tries to exploit vulnerabilities in fraud detection systems by manipulating data inputted in the model. In the context of financial fraud detection, fraudsters can intentionally modify transaction data, such as the amount or the location, to make the transaction seem legitimate to the ML model.

Given all this, financial fraud detection is a constantly moving target. Detection systems must follow the fraud trends and constantly improve. Just as fraudsters can use AI to sophisticate their activities, financial institutions can leverage AI to help combat those fraudulent activities.

Data Overload

The rapid growth of big data is another challenge for financial fraud detection. A rapid increase in the volume of financial transactions may overwhelm traditional fraud detection systems. Because each transaction generates data, systems must have the computational power and storage capacity to process these transactions. 

In addition, real-time detection systems must analyze the transactions as they happen. To effectively combat fraud in this high-volume environment, modern detection systems must possess the capacity to analyze transactions with minimal latency, a requirement that often exceeds the capabilities of traditional systems.

False Positives and Negatives

Any system that detects fraudulent activities inevitably leads to occasional false positives or false negatives. However, systems must try to keep these instances to a minimum. An increased number of false positives may irritate legitimate users and waste company time and resources. 

Nevertheless, false negatives mean that fraudulent activities have happened and haven't been caught. This can lead to significant financial losses and reputational damage.

Integration Issues

Another challenge organizations face is integrating modern financial fraud detection systems within a legacy infrastructure. If the existing systems in a company use an outdated architecture, they may not be compatible with certain programming languages, APIs, or modern data exchange formats. Such systems may also have difficulties with scalability, considering the high volume of transactions modern fraud detection systems deal with.

Legacy infrastructure also comes with downtime risks, which can disrupt important processes. Existing infrastructure may also lack modern security features, making it vulnerable when integrating with new fraud detection systems. Legacy infrastructure can sometimes require significant custom coding to be able to integrate with modern fraud detection systems, which in turn requires more time and higher costs.

Balance in Security and User Experience

While fraud detection systems help in identifying fraudsters, they can annoy legitimate users, who may see those security features as a hurdle.

For example, CAPTCHA tests are useful for identifying bots, but legitimate users may be irritated by them, especially if they are frequent and difficult to solve. Multifactor authentication (MFA) is another useful security feature; however, it adds additional steps to the login process. Shorter user sessions improve security, but they may disrupt users in their work, cause them to lose progress, and make them log in multiple times.

Such security features can negatively affect customer satisfaction and user retention. Therefore, it's important to strike the right balance between necessary security features, and user experience and satisfaction.

Financial Fraud Detection Solutions

In the previous sections, we discussed some of the main challenges in detecting financial fraud. Fortunately, these challenges are not insurmountable, and the next section discusses some solutions.

Advanced AI and ML Systems

AI and ML have been a common theme throughout this article. They've been mentioned in the context of evolving fraud tactics, which often rely on advanced AI tools. However, AI can also be used to combat financial fraud. 

ML models can analyze huge amounts of data—something that's virtually impossible to manually perform on scale. After being trained on those vast data sets, ML models can learn to recognize patterns in fraudulent transactions and flag suspicious activity. They're also able to recognize certain trends and relationships in the data that could otherwise go unnoticed.

One advantage of AI models is that they can learn dynamically, which is crucial given the fast evolution of fraud tactics. As new data comes, AI models can quickly learn from it and adapt. AI models also deliver higher accuracy than traditional systems, which leads to fewer false positives.

Another benefit of AI systems is real-time analysis. For instance, PayPal's AI fraud detection system evaluates each transaction in real time, analyzing factors like amount, location, and device. The system looks for anything unusual and evaluates the probability that any given transaction is fraudulent.

Behavior Analytics and Passive Biometrics

Behavior analytics is another aspect of financial fraud detection you should pay attention to. Financial institutions and companies analyze the behavior of users and establish a baseline behavior for each one individually. Once the baseline is established, any unusual deviation from it can be a red flag.

Fraud detection systems can consider the user's location, their device, the usual transaction frequency and amounts, transaction times, as well as spending patterns. A sudden change in one or more of these items should trigger the detection systems and lead to additional investigation. The system should continuously adapt to changing user behavior and update the established baseline whenever necessary.

Distinct from traditional active biometrics, passive biometrics leverages background behavioral data for continuous and unobtrusive authentication. Passive biometrics encompasses the use of biometric data in the background, without needing an active interaction from the user. Examples include mouse movements, keyboard typing speed and patterns, as well as touchscreen dynamics. Passive biometrics offer many advantages, such as the ease of collecting data, the ability to continuously analyze data, and most importantly, the characteristic of not being intrusive to the user.

Anomaly Detection and Rule-Based Systems

Through anomaly detection, a system attempts to identify transactions that are considered unusual as they are significantly different from typical transactions. Traditional methods of anomaly detection involve defining a threshold within which a transaction is considered usual and then identifying outliers that lie outside of the defined thresholds. 

In this sense, it's similar to behavior analysis. While behavior analysis focuses only on the user's behavior, anomaly detection looks for anomalies much more broadly. Anomaly detection looks for a deviation from the usual in any aspect of the transaction, whether it's related to the user's behavior or not.

More modern approaches use ML techniques like clustering. This is a common unsupervised technique that divides data points into several different groups (clusters) based on shared features. Unlike supervised learning methods, clustering doesn't require the data points to be labeled. Some common clustering techniques are *k*-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical clustering.

Apart from anomaly detection, rule-based systems can also be used. These involve defining static rules based on historical data and domain knowledge, and then identifying transactions that are lying outside of these rules.

For example, a rule can be set up that if the user makes a transaction in a different country than usual, the transaction can be flagged. Another rule can be set up for transactions with much higher amounts than usual or on a different device. Once a rule is set up, anytime such activities are noticed, the system can ask for further verification from the user.

Anomaly detection and rule-based systems aren't mutually exclusive, so they can be combined to better identify fraudulent transactions.

Collaboration Between Security and Fraud Teams

To improve the detection of fraudulent transactions, the security and fraud teams within a financial institution or company must closely collaborate. Although these two teams often operate in silos, they have expertise that are complementary. If working in tandem, these two teams can address both financial fraud and cybersecurity threats.

The collaboration between the security and fraud teams allows them to share data and insights. Data from the security teams can be integrated into the fraud detection systems and tools. This can include network logs, user access patterns, and threat intelligence feeds. 

Meanwhile, data from the fraud teams can suggest broader security issues. For example, if the fraud team detects an unusual transaction, the security team can check for security breaches.

Given that both security and fraud teams handle sensitive data, it's important to establish data-sharing protocols. For example, this can include the fraud team having access to transaction data, such as customer information and history, and the security team dealing with data related to authentication and system access logs. It's important to note that the cooperation between the two teams can be complex, as well as costly, since it may need additional software, tools, and maintenance.

Collaboration between fraud and security teams is particularly important in financial institutions, given that they have a lot to lose if fraudulent activities go unnoticed. This can cause significant financial losses and reputational damage. The same is true for industries such as insurance and healthcare.

Integration of Blockchain Technology

Blockchain technology has a couple of features, such as transparency, that can significantly help in fraud detection efforts. With blockchain, all the data can be found in a distributed ledger, which is widely available, making it easy to trace transactions.

Since this ledger is distributed, it's not controlled by a single entity. This leads to another useful aspect of blockchain—its immutability. As mentioned previously, the ledger contains records of all previous transactions. Changing previous records requires a consensus from the network. As such, it's extremely difficult for fraudsters to control the entire network and alter the records.

The blockchain has another highly useful feature when it comes to fraud detection—its traceability. The ledgers not only contain records of all previous transactions but are all linked together, making it possible to trace the path of the funds back to the start. If there are suspicions of fraud, this allows financial institutions to track the movements of the assets across the network.

One possible limitation of blockchain technology is scalability, especially considering the enormous number of transactions financial institutions have to process. In general, blockchain technology can process a limited number of transactions per second, which may not be enough for large financial institutions. This can create bottlenecks and network congestion, which may lead to transaction delays.

Selection of the Right Fraud Detection Tools and Systems

Various tools can help with financial fraud detection, and it's important that you select the right tools and systems for your use case.

A good fraud detection system should be able to integrate quickly, be customizable based on changing needs, and ideally be consolidated into one platform. A tool like the Prove Developer Portal allows you to easily integrate the Prove Pre-Fill solution, making identity verification and user authentication straightforward to implement. The Prove Pre-Fill solution prevents fraud, reduces consumer friction, and helps you comply with the Know Your Client (KYC) standard.

The tools and systems you use should also be able to identify compromised credentials, detect bots and differentiate between them and legitimate users, and analyze behavior for user intent verification. 

There are also some practical considerations to keep in mind when choosing a fraud detection tool. First, the system should be scalable, and the fraud detection system must be compatible with the existing systems in the organization to avoid operational disruptions.

Second, the system you choose should also have real-time identity verification and adaptive fraud prevention. This can be secured through a platform such as Prove, which enables you to onboard users rapidly and in a frictionless manner through accurate identity verification.

Conclusion

As technologies keep advancing, financial fraud is becoming more and more sophisticated. Financial institutions and companies must respond by investing in better fraud detection systems and tools, but this isn't always easy. Data can overload the system, and you have to deal with false positives and negatives as well as integration issues. Thankfully, using advanced AI and ML systems can help, as can behavioral analytics, anomaly detection, and integration of blockchain technology.

Prove can help with all your digital identity verification and authentication needs. Prove allows for passwordless and one-time-password-less (OTP-less) authentication, phone-based identity verification without scans or selfies, as well as prepopulating forms with verified identity information. It streamlines fraud detection with real-time insights, adaptive ML, and identity verification solutions for a secure and user-friendly experience.

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