The AI playbook for bank fraud detection

The AI playbook for bank fraud detection

Fraud detection is a crucial component in safeguarding the integrity of the financial industry. By protecting assets and maintaining customer trust, effective fraud detection strategies are fundamental in preventing financial losses and ensuring the smooth operation of financial systems.

An image depicting a modern financial technology office, where AI is applied in bank fraud detection. This scene shows a team of experts utilizing advanced technologies to analyze and prevent fraudulent activities in the banking sector.

This article provides a structured analysis of techniques for utilizing Artificial Intelligence (AI) for preventing fraud, particularly within the banking sector.

What is Fraud Detection?

Fraud detection in the banking sector involves identifying unusual patterns, behaviors, or activities that may indicate fraudulent actions within financial transactions or account operations.

This process is crucial for protecting the assets of both the institution and its customers. Modern fraud detection systems leverage advanced analytics, machine learning algorithms, and real-time monitoring to identify and prevent potential fraud. By analyzing vast amounts of data, these systems can detect anomalies that deviate from normal customer behavior, flag suspicious transactions for review, and even predict future fraudulent activities.

Effective fraud detection strategies help in minimizing financial losses, maintaining customer trust, and ensuring compliance with regulatory standards.

Types of Fraud in the Banking Sector

Understanding the types of fraud is essential for developing targeted detection. We’ll cover a few different types of fraud that occur and some general approaches for using AI to combat them. This is by no means an exhaustive list, however.

Identity Theft

This type of fraud involves the unauthorized use of someone’s personal information, such as Social Security number, account numbers, or other identifying information, to access or create bank accounts. It’s a foundational type of fraud that can lead to various other financial crimes, such as unauthorized transactions and account takeovers.

Transaction Fraud

This encompasses a range of unauthorized activities involving a customer’s account. It includes but is not limited to, card skimming, phishing, online scams, and fraudulent wire transfers. Transaction fraud directly targets the funds within an individual’s account and can be executed across various platforms, including ATM, online banking, and mobile transactions.

Loan Fraud

This occurs when false information is intentionally submitted during the loan application process. It can involve exaggerating income, falsifying employment history, or inflating asset values to meet the lending criteria. Loan fraud affects both individuals, who may unknowingly become involved in fraudulent schemes, and financial institutions, which face financial losses and regulatory repercussions.

Level 0 Detection: Prevention

Before diving deep into fraud detection techniques, it’s crucial to address ways to prevent fraud before more advanced methods need to applied for detection. Implementing proactive measures can significantly reduce the likelihood of fraud occurring in the first place.
Here’s a concise overview of effective fraud prevention strategies:


Utilizing Multi-Factor Authentication (MFA) enhances security significantly. By requiring additional verification factors beyond a simple password, the risk of unauthorized account access decreases substantially.


Implementing encryption and secure communication protocols, such as SSL and TLS, protects customer data during transmission, safeguarding sensitive information from interception or unauthorized access.

Customer Education

Educating customers is paramount for preventing fraud. Guidance on secure banking practices, including the creation of strong passwords and the identification of secure websites, empowers customers to take control of their account safety. Additionally, facilitating easy avenues for customers to report suspicious activities enables a quicker institutional response can prevent potential fraud incidents.

Robust Internal Processes

Consistent employee training on the latest fraud trends and prevention techniques ensures that the financial institution’s staff is equipped to recognize and counteract fraudulent attempts effectively. Regular audits and adherence to compliance standards are essential for identifying and addressing internal vulnerabilities, thereby strengthening the institution’s overall security posture. By integrating these preventative measures, financial institutions can significantly reduce the incidence of fraud, ensuring a more secure banking environment for all stakeholders.

Using AI for Fraud Detection

Utilizing machine learning to detect different types of fraud involves deploying various data-driven techniques and models that learn from historical fraud patterns and user behaviors.
Here’s a brief overview of how machine learning can be applied to each type of fraud:

Identity Theft

Pattern Recognition: Machine learning algorithms can analyze transaction histories, login patterns, and user behaviors to identify anomalies that may suggest identity theft. For example, sudden changes in spending habits or access from unusual locations could trigger alerts.

Data Enrichment: By integrating external data sources, machine learning systems can better assess the legitimacy of transactions and user identities. This includes cross-referencing with public databases, credit bureaus, and other financial institutions.

Biometric Verification: Implementing machine learning models that analyze biometric data, such as facial recognition or fingerprint patterns, can help in authenticating user identities and flagging unauthorized access attempts. Here’s one such example from Socure fighting fraud with computer vision.

Transaction Fraud

Real-Time Monitoring: Machine learning models can be trained to monitor transactions in real-time, comparing each transaction against a user’s typical behavior and known fraud indicators, such as transaction size, frequency, and geographical patterns.

Anomaly Detection: By establishing a baseline of normal activity for each user, machine learning can identify deviations that may indicate fraudulent transactions. These anomalies can include unusual transaction amounts, rapid succession of transactions, or transactions from new devices or locations.

Text Analysis: For phishing and online scams, natural language processing (NLP), a subset of machine learning, can analyze the text of emails, messages, and website content to detect phishing attempts, fraudulent links, and scam tactics.

Loan Fraud

Application Analysis: Machine learning algorithms can scrutinize loan applications for discrepancies, inconsistencies, and anomalies. By comparing the information provided against historical data and known fraud patterns, the system can identify red flags, such as overstated income, underreported liabilities, or falsified documents.

Behavioral Analysis: Analyzing the behavior of applicants during the application process can provide additional clues to potential fraud. Machine learning can detect patterns that deviate from the norm, such as the speed of form completion, changes in application details, or unusual sequences of actions.

The exact details of different machine learning and AI techniques will vary for each of these different types of fraud. I will recommend some fundamental best practices that can help any AI team reach their full potential in terms of maximizing fraud detection.

Laying the Foundation: MLOps and Best Practices

Broadly speaking, most issues addressable through artificial intelligence hinge on two main components: data and a model. The objective of the model is to perform a specific action or analysis based on that data. The task of the model could be to generate more data that could be related to the input data (for example an LLM responding to a prompt), or it could be simply classifying the data into a specific category (for example classifying a transaction as fraudulent or legitimate). Usually, every model will need to be “trained” on data, which essentially means running an algorithm that passes the data though the model to generate a prediction, and then calculating some sort of error to adjust the model in a way that makes the model more likely to predict the correct value on subsequent iterations.

The Data

In the context of fraud detection for banks, data is a crucial component to detecting fraud. The word “data” is often used somewhat loosely, however, in the context of fraud detection, “data” can be viewed all information related to a customers and their previous activity.

All information that could potentially be used by a human to make a decision could potentially be useful to AI systems. Additionally, if you are ever in doubt about which data could be useful, I’ve found its helpful to error on the side of storing too much information, as opposed to not enough, as it’s always easy to delete data later on.

Another important factor to utilizing data properly for machine learning is automation. Over time, you will acquire more data, which will likely be useful for improving your systems. As fraudsters evolve their tactics, you will need to adapt your systems to detect new patterns that occur. So you may be wondering, what is the best way to quickly adjust to new tactics? Or in other words, how can an engineering team quickly build new models for new data? In my opinion, the first and most important step to take is to build a data flywheel.

Data Flywheel

A data flywheel is a system where data collection and analysis improve continuously through an iterative process. In the context of fraud detection for banks, this means creating a mechanism where every piece of data collected not only contributes to the immediate detection and prevention of fraud but also enhances the bank’s ability to predict and prevent future fraud.

One of the core ideas behind a data flywheel is that it automates the process of preparing new datasets which can be used to improve your fraud detection models. By automating this process, you effectively are able to reduce the time and effort to improve your existing systems, which will likely result in faster deployment of updated models, and ultimately detecting fraud that may not have been caught by previous versions of the system.

Model Versioning and Benchmarking

Besides building a data flywheel, it’s also important to have a system in place for keeping track of how different models are performing on previous versions of your datasets. In some cases, models trained on new data may not actually be the best model for predicting the future types of fraud, as fraudsters continuously evolve their strategies. Therefore, having a robust system for versioning your models and testing them against both current and historical datasets is crucial. This enables you to ensure that your models are not only accurate in detecting known types of fraud but are also adaptable to new and emerging patterns.

This process of continuous evaluation and reevaluation is key to the data flywheel concept. By consistently benchmarking model performance, you can make informed decisions about which models to deploy and when to retrain them. This approach also helps in identifying any bias or weaknesses in the models, allowing for timely adjustments. Weights and Biases offers excellent tools for this process, and I highly recommend their tools for keeping track of models and performance metrics!

Fostering Competition and Creating Transparent Objectives

Machine learning research is moving at a blistering pace. It seems like everyday, there is a new technique that improves upon existing methods. There is a huge opportunity to leverage this abundance of research, and quickly adopt new methods into existing systems. However, it’s crucial to also verify the performance of new systems before deploying them into production. Creating an environment where experimentation is encouraged, along with creating implementing transparent metrics for success will foster innovation on any machine learning team. This means creating safe datasets accessible by engineers within the organization, so that new experiments can be quickly ran, without the burden of bureaucracy in regards to obtaining access to the data, or deciding how well a model is performing in reference to previous models. Now, this will require more resources for tasks like creating anonymized versions of the data and building infrastructure for data security, however, I believe that the additional resources required will usually pay off in the long run. Additionally, having an internal leaderboard of performance on test datasets will likely encourage experimentation, and quicker adoption of new methods that outperform previous methods.

Cross Collaboration for Performance Evaluation

In any machine learning system, there is typically a core metric that is used for determining the quality of a system. For example, imagine a simple facial recognition system, which uses an accuracy metric to determine the number of faces that were correctly classified in a dataset. Additionally, other metrics like precision could be used to determine the proportion of positive identifications that were actually correct, while recall measures the proportion of actual positives that were correctly identified. In the context of a facial recognition system, precision would be critical in scenarios where the consequences of false identification are high, such as in security applications. On the other hand, recall might be more important in situations where failing to identify a face could have significant implications, like in missing person searches.

In regards to fraud detection, the metrics and objectives the systems use are often a bit more complex. For example, a falsely classified fraud event could result in huge losses for a bank. However, if the system is too aggressive, it could result in an inconvenienced customer. Usually in situations like this, there is a tradeoff between false positive and false negative events, and this balance is often different depending on the context.

This complexity underlines the importance of cross collaboration between different departments within financial institutions. Data scientists, security experts, customer service teams, and legal departments need to work together to define the metrics that matter most to their specific fraud detection goals. This collaboration ensures that the machine learning system is not only technically accurate but also aligned with business objectives and regulatory requirements.

An effective strategy for balancing these metrics is to implement feedback loops where the outputs of the fraud detection system are continually reviewed and refined based on actual fraud outcomes and customer feedback. This iterative process allows for adjustments to be made to the system, improving its accuracy over time.

A Case Study on Fraud Detection

This case study delves into the technical intricacies and strategic decision-making behind a cutting-edge machine learning-based approach for fraud detection, as detailed in the research by Vanini et al. (2023). The primary goal of this study was to develop methods to accurately identify external fraudsters looking to initiate fraudulent payments for their own benefit. The research is grounded in the reality of modern banking, where such fraudulent activities are increasingly common and sophisticated, posing significant risks to both individuals and financial institutions.

We will now cover the core components of the system designed in the study.

Data Collection and Preprocessing

The initial step in building an effective fraud detection framework was the comprehensive collection and meticulous preprocessing of data. The researchers compiled an extensive dataset encompassing various facets of customer interactions and transaction details. This dataset included:

Transaction Data: Details of each transaction, including amount, time, location, and type (e.g., withdrawal, transfer, payment). This provided a direct window into the financial movements potentially indicative of fraudulent activity.

Customer Interaction Logs: Records of customer activities within online banking platforms, capturing sequences of actions (e.g., logins, navigation paths, transaction attempts). This behavioral data is crucial for understanding normal versus anomalous user patterns.

Account Information: Customer-specific information, including account types, balance history, and usage frequency. These details helped in contextualizing transaction data and customer behavior within individual financial landscapes.

Historical Fraud Cases: Documented instances of confirmed fraud, serving as a critical resource for training and validating the machine learning models. This data ensured that the system could learn from past incidents to better predict and identify future fraud.

Feature Engineering

With the raw data in hand, the researchers embarked on a feature engineering process, transforming the collected information into a structured format conducive to machine learning analysis. This involved extracting behavioral features from customer interaction logs, such as the frequency and sequence of actions, to capture typical versus atypical user behavior. Additionally, transactional features were derived, including the analysis of transaction patterns and anomalies when compared to historical customer data. The process also included compiling customer-related features that consider sociodemographic details and account characteristics, providing a fuller picture of each customer’s banking profile.

Model Selection and Implementation:

The core of the bank’s new fraud detection system consisted of three main models:

Local Outlier Factor Model (LOF): Chosen for its proficiency in identifying deviations from typical customer behavior, the LOF model was integral in pinpointing transactions that strayed significantly from established patterns. This unsupervised learning technique was particularly suited to detecting new, previously unseen types of fraud.

Isolation Forest Model (IF): This model excelled in isolating anomalies within the data, making it an invaluable tool for identifying fraudulent transactions amidst vast datasets. Its efficiency and scalability made IF an ideal choice for real-time fraud detection.

Bagged Decision Trees Model (BDT): Representing normal customer behaviors, the BDT model served as the baseline against which transactions were compared. Its selection was based on the model’s robustness and its ability to handle diverse data features without overfitting.

The Ensemble

In the next stage, these models’ outputs are combined. This combination uses both simple averaging and a more nuanced method where the limited available fraud case data fine-tune the models’ contributions in a supervised manner. Specifically, penalized logistic regression is applied for the final fraud classification. This method not only aids in decision-making but also enhances model transparency. It does so by assigning weights to different parts of the model, helping identify which base learners, features, and data samples are most influential in detecting fraud, leading to a more targeted and effective fraud detection system.

Triage Model

Recognizing the importance of prioritizing threats, the researchers implemented a triage model to manage alerts generated by the machine learning algorithms. This model adjusted fraud detection thresholds based on transaction sizes, thereby allocating investigative resources more effectively and reducing the potential economic impact of fraud.

The learning objective of the model is to optimize the balance between catching as much fraud as possible (true positive rate) and minimizing the mistaken flagging of legitimate transactions as fraudulent (false positive rate). The model aims to reduce overall financial losses by improving how accurately it can identify real fraud, especially in larger transactions, without overwhelming the system with false alarms.

The model’s effectiveness is assessed using ROC curves, which help in visualizing the trade-off between detecting frauds correctly and avoiding false alarms. By adjusting the detection thresholds based on the transaction size, the model learns to minimize the bank’s expected losses due to fraud while keeping the rate of false alerts manageable. The goal is to find the optimal threshold settings that maximize the detection of fraudulent transactions without surpassing a predefined rate of false alarms, ensuring the bank’s fraud detection efforts are both efficient and economically sound.

Risk Model

Finally, the researchers integrated a comprehensive risk model to quantify potential losses and understand the broader risk landscape. This model offered a statistical perspective on the likelihood and impact of various fraud scenarios, guiding strategic decisions and countermeasure implementations.

Outcome and Analysis

By leveraging the unique strengths of LOF, IF, and BDT in combination with the triage and risk models, the researchers achieved a more nuanced, responsive approach to fraud detection. The system’s adaptability meant that it could evolve in tandem with emerging fraud trends, providing a level of security that static, rule-based systems could not match.


In conclusion, the battle against financial fraud is an ever-evolving challenge that demands constant vigilance and innovation. This article has traversed the broad spectrum of fraud detection, from understanding the types of fraud prevalent in the banking sector to the deployment of cutting-edge AI technologies designed to thwart these malicious activities. Through the layers of prevention strategies, data management practices, and AI application, we can perceive a clear trajectory towards more secure and resilient financial environments.