Anomaly Detection in Financial Transactions

The world of finance never sleeps. No matter where you are and what time it is, global transactions are happening at every moment. For this reason, detecting anomalies in financial transactions is essential for maintaining the integrity and security of financial systems.  

Anomaly detection in finance involves identifying irregular patterns or outliers in transactional data that could indicate fraud, errors, or unusual activity. With the increasing complexity of financial operations, monitoring vast amounts of data for inconsistencies has become a critical task. 

To help readers understand its importance, this article will provide a comprehensive guide to anomaly detection in financial transactions.    

What is Anomaly Detection in Finance? 

Anomaly detection in finance refers to the process of detecting unexpected, unprecedented, and rare events in a data set. These are commonly known as anomalous events. Finding data anomalies is helpful because it paints a clear picture of the data.  

It is a fundamental concept behind many applications, mainly in real-time apps. Spotting anomalies in health, infrastructure, and processes is crucial for security and fintech apps.   

Importance of Anomaly Detection in Finance 

Here is why anomaly detection in financial transactions is important: 

1. Fraud Prevention 

Anomalies in financial transactions often indicate fraudulent activities, such as:  

• Unauthorized Credit Card Use 
• Money Laundering 
  Scams 

Detecting these irregular patterns early allows organizations to prevent financial losses and protect customer accounts. 

2. Regulatory Compliance 

Financial institutions are subject to strict regulations to prevent illegal activities. Anomaly detection in finance ensures compliance by identifying suspicious behaviors, such as: 

• Unusual Trading Volumes
• Irregular Fund Transfers 
• High-Risk Transactions 

Regulatory compliance helps organizations meet legal obligations. 

3. Risk Management 

Anomalies in financial data can signal potential risks, such as market manipulations or system failures. Detecting and addressing these risks proactively helps organizations minimize losses and maintain operational stability. 

4. Operational Efficiency 

Inconsistent financial records or transactions might indicate errors or inefficiencies in processes. Identifying these anomalies can improve operations, reduce errors, and ensure accurate reporting. 

4. Customer Trust 

Detecting and addressing anomalies quickly reassures customers that their financial information and transactions are secure. This builds trust and loyalty for success in the competitive financial industry.

Techniques for Anomaly Detection in Finance 

There are three main methods to detect anomalies in financial transactions: 

1. Statistical Detection 

This method uses statistical models to identify anomalies by analyzing deviations from normal behavior. Some common statistical techniques are: 

• Z-scores 
• Probability Distributions 
• Hypothesis Testing  

This approach is simple and interpretable, but it struggles with complex or high-dimensional datasets. Traditional statistics sometimes don’t easily capture patterns. 

 2. Machine Learning-Based Detection 

Machine learning detection uses algorithms to learn patterns of normal behavior from data and identify outliers in real time. This method works well for identifying dynamic fraud patterns. It relies heavily on quality training data and may require periodic retraining to remain effective as patterns evolve. 

Some notable Anomaly Detection algorithms:  

• Isolation Forest (ISO Forest)  
• Cluster-based Local Outlier Factor (CBLOS)  
• Histogram-based Outlier Detection (HBOS)  

3. Deep Learning Detection 

Deep learning approaches are capable of detecting complex, non-linear patterns in large datasets. These methods are highly accurate and particularly effective for high-dimensional anomaly detection in finance. 

However, they require substantial computational power and a significant amount of labeled data for training. 

Role of Machine Learning (ML) in Anomaly Detection in Finance 

Machine learning is reshaping transaction anomaly detection. With ML businesses can identify and prevent fraud earlier and effectively. ML uses advanced learning algorithms that can detect irregularities and learn from fraud patterns over time. 

These learning algorithms define how ML models approach the problem of detecting anomalies, depending on the nature of labeled data. They are categorized into three main types: 

• Supervised Learning relies on labeled datasets where instances of both normal and anomalous behavior are clearly identified. Machine learning models are trained to distinguish between these categories for accurate detection. It is commonly used in fraud detection when historical data includes known instances of fraudulent transactions. 

• Unsupervised Learning algorithms work without labeled data. They identify anomalies based on deviations from the normal patterns observed in the data. Unsupervised methods are especially useful in cybersecurity where anomalous events are rare or when labeled data is unavailable. 

• Semi-Supervised Learning combines elements of supervised and unsupervised approaches. The model is trained on a dataset containing only normal behavior and then identifies deviations as potential anomalies. Semi-supervised anomaly detection in finance is useful in credit risk assessment where data representing normal operations is abundant, but examples of anomalies are rare. 

Choosing Between Supervised and Unsurprised Models 

Here’s a comparison to help evaluate which is more suitable for financial anomaly detection: 

1. Supervised Models 

Supervised models are highly accurate in detecting known types of anomalies. These models provide easily interpretable results and are ideal for use cases where the anomalies are well-defined. 

However, they require a large amount of labeled data which can be expensive and time-consuming. Supervised models also struggle with detecting novel or unseen anomalies that differ from the training data. 

2. Unsupervised Models 

These models are useful for real-world cases where labeled data is not available. Unsupervised models can identify unknown or new types of anomalies since it’s not limited to predefined categories. They are also scalable for large datasets where labeling is impractical. 

Unsupervised models flag rare but normal events as anomalies. They are also less precise than supervised models due to the lack of explicit guidance. 

In short, supervised models excel when the problem is well-defined, and labeled data is available. Unsupervised models are better for exploratory or dynamic environments. 

How to shift from Unsupervised model to Supervised?  

The method intends to separate the anomalies and separately create an anomaly model for every KPI so that we can create labels for every KPI. The model will tell us whether the specific transaction is risky in light of that KPI. The transaction can be difficult in light of multiple KPIs, or it can be difficult in one KPI and non-risky in another.  

Data preparation steps are as similar as common data science problems; that is why a standard layer should be prepared to be used in unsupervised model preparation and in supervised model preparation. Data cleaning to Feature extraction is generally a common layer, then unsupervised models and supervised models can utilize them to develop the models.  

Data Extraction and Preparation  

The data lies typically in databases or managed cloud platforms in different settings. One must design the data pulling job carefully to include the important columns. This is a very critical job for Data engineers as well as Data scientists. If we dive into this topic, our original focus of this blog will shift as it is a very challenging problem, and its details are overwhelming.  

Once the data is available to the data scientist, the first and foremost step is to realize the column types and their nature. There might be some columns that are usable for later stages, like data, time, etc., or maybe obliterated. One can then identify and correct the types. 

Data cleaning includes cleaning ‘nan’ values, data imputation column to column basis, converting numeric to float, removing outliers, and performing data discretization if necessary.  

Some other steps:  

1. Convert known category variables to categorical  
2. Add ‘year’ and ‘month’ features for later usage  
3. Run Pandas’ describe’ method to pull basic stats about the variables  
4. Print top values and other information for variables  

Correlation Analysis  

Sample the data for further testing and skew measures. In this way, the distribution of the column can be accurately visualized and corrected if skewed not evenly. 

Run correlations of categorical and numeric features to identify and remove overly correlated features. We must carefully select the correlated columns. There might be a case where two columns correlated strongly; we might choose the wrong column which does not align with a business goal. Besides correlation analysis, columns must be selected according to the model’s requirement and the use case. 

Dealing with textual features. Here are the steps to follow:  

Fix the text features with standard tfidf vectorizer parameters¶  

1. Review the vocabulary for abbreviations that can be normalized  
2. Apply case, space, and punctuation normalizations  

Normalize reduced variable set based on correlation and skew  

1. Convert categorical features to encoded vectors for machine-readable input  
2. Convert numeric features to scaled versions so that numeric features all cover the same range of values between 0 and 1  

Also, run the Principal Components Analysis Explained Variance measure and graph to choose the component count cut-off  

Choose number of PCA components based on PCA explained variance intersection at 95% variance. 

Identify the most weighted contributing feature and category from each PCA component. Also, you can always backtrack and see which columns are preferable to use in models. PCA has simplified this feature selection process by this mechanism. 

 Train Anomaly detection Algorithms  

For every KPI, train separate anomaly detection algorithms so that we can identify the different risks at different levels.   

It is always a good practice to separate Train, Validation, and Test Set. But from where we would identify the correct working of the model. Remember if we can make simple rules of KPIs and mark the data with those rules. These can serve as weak comparison benchmarks. So, in this way, we can always verify whether the model is performing the theme in Validation and Test Set training. Moreover, apart from the PYOD and sklearn library given parameters to check the model performance, this additional step can ensure the model is trained accurately. 

Training supervised models  

Through the data and its nature, ml models can be easily selected. Therefore, the metrics must be precision, recall, and F1 to measure risky/non-risky behavior against every KPI. Models like XGBoost, Neural Nets, Random Forest, Gradient Boosting Classifier, and Support Vector Machine (SVM) can be tried to check which ones are the better candidate.   

Moreover, two libraries can be compelling in optimizing the models and explaining the model’s features. Optuna is very powerful tool for selecting the optimum hyperparameters to maximize the classification accuracy.   

The other tool, SHAP (SHapely Additive exPlanations), is a tool of explainable AI to explain the model’s outputs. 

So, the important columns and their impact are identifiable through the SHAP, and we can eliminate those columns with low impact in modeling. 

Advantages of using the Supervised model instead of Anomaly models  

We are not using anomaly detection algorithms in production. Instead, we intend to use the labels generated and train a multi-label machine learning model. The reasons for that are:  

1. Instead of several models, only one multi-label will address all KPIs, and the computation cost would decrease.  
2. 2Training one model will be beneficial once we have the human-level annotation. Also, the shift from unsupervised to supervised learning will be much easier. The labels will then easily fine-tune or re-train one model instead of several anomaly models.  
3. Multi-label classification will open doors to several varieties of models for each KPI instead of limited choice in clustering or anomaly algorithms.  
4. Metrics will be adequately defined, and you can measure the accuracy for each label much more quickly and easily.  

Challenges in Anomaly Detection in Finance 

Here are some of the common challenges that arise during anomaly detection in financial transactions: 

1. Defining Anomalies 

What constitutes an anomaly? This question can be highly subjective, and context based. In one scenario, an unusual transaction size might be normal while in another, it might signal fraud. 

Without a clear and specific definition, anomaly detection models may produce false positives or fail to detect true anomalies. 

2. Lack of Labelled Data 

Anomaly detectors are as good as the data provided to them. Limited labeled data reduces the effectiveness of supervised learning techniques. Pushing reliance on unsupervised or semi-supervised methods that can be less precise. 

3. Data Bias 

In some datasets, normal instances vastly outnumber anomalous ones. ML models trained on such imbalance data become biased toward predicting normal behavior and miss anomalies. 

4. Changing Data 

Sometimes data changes over time, models trained on static data struggle to adapt. This leads to outdated results and undetected anomalies in new patterns. 

5. Complex Data 

Data with high dimensionality can make detecting anomalies computationally expensive and complex.  

Financial data especially has many features which is why traditional detection techniques often fail. 

Conclusion 

Anomaly detection in finance plays a critical role in maintaining the integrity and stability of the financial world. It helps prevent fraud, mitigate risks, and ensure regulatory compliance. Financial institutions rely on it to analyze vast datasets in real-time to detect evolving threats and safeguard their operations.  

While challenges persist, continuous advancements in technology are making anomaly detection increasingly accurate and effective. As financial transactions become more complex, the importance of anomaly detection systems will only grow for secure, transparent, and trustworthy financial ecosystems. Contact us at [email protected] today and avail top-class services from the industry experts and professionals for your business.