Detecting Financial Fraud in Real-Time: A Guide to ML Monitoring

Fraud is a significant challenge for financial institutions and businesses. Fraudsters constantly adapt their tactics to evade detection and defraud victims, causing significant financial losses and reputational damage to the organizations they target. To combat this challenge, many businesses and financial institutions have turned to machine learning (ML) models to help detect and prevent fraud.

ML models are powerful tools that can analyze large volumes of data and detect patterns that may not be apparent to human analysts. However, these models are not perfect and can produce inaccurate or unreliable results if they are not properly monitored and maintained. As fraud continues to evolve, it’s essential to implement a robust model monitoring system to ensure that ML models effectively detect fraud and minimize false positives.

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Why ML monitoring for fraud detection

ML Monitoring involves tracking the performance of data and ML models over time, detecting and addressing any changes that may affect its accuracy or reliability. This process includes identifying new patterns and trends in data that the model may not have been trained to recognize and detecting any drift or degradation in model performance. Model monitoring is crucial for fraud detection, as fraudsters constantly change their tactics, and the data used to train the model can quickly become outdated.

Data quality validation

An important aspect of model monitoring is validating data quality to ensure that the data used to train the model is accurate, consistent, and complete. If the training data is incomplete or contains errors, it can lead to inaccurate or biased results. Data validation also involves monitoring the data input to the model in real-time to detect any anomalies or inconsistencies. Read more about how to validate data quality in our blog post.

Performance monitoring

Another critical component is performance monitoring to track the accuracy and reliability of the model over time and detect any changes in its performance. Performance monitoring can be accomplished using various techniques, such as statistical process control charts, outlier detection, and model comparison.

Model comparison

Model comparison is another valuable tool for monitoring. This involves comparing the performance of the model to other models or benchmarks to detect any drift or degradation in performance. Model comparison can also help identify new patterns and trends in data that the model may not have been trained to recognize.

Data drift

It’s also important to identify feature, model, and actual drift between various model environments and versions to monitor for fraud patterns, data quality issues, and anomalous distribution behavior. Sudden changes in data distribution could be indicative of a new fraud tactic which may prompt a retrain of your model.

Benefits of ML monitoring in fraud detection

Implementing a robust model monitoring system offers several benefits for fraud detection including:

Improved accuracy

Ensure that the ML model always uses the most current and accurate data, identify when a model is making incorrect predictions or failing to detect fraud, allowing for prompt intervention and correction. This can ultimately lead to improved accuracy and better fraud detection.

Minimizing false positives

By monitoring the inputs and outputs of the model, reduce false positives (i.e., cases where legitimate transactions are incorrectly classified as fraudulent), which can be costly for financial institutions in terms of lost business and customer satisfaction.

Faster detection of fraud

Identify new patterns and trends in data and changes in model performance to detect fraudulent activities in real-time, allowing financial institutions to respond quickly and prevent further damage.

Improved operational efficiency

Reduce the time and effort required to investigate fraud cases by providing accurate and reliable results. Optimize your existing models and make it easier for your team to build and deploy new models.

WhyLabs

WhyLabs Observatory is the solution for detecting and alerting on any data issues as data is fed into a machine learning model, including data drift, new unique values, missing values, etc. Financial services firms can utilize the Observatory to minimize losses from fraud. The WhyLabs Observatory platform can identify data quality issues/changes in a data’s distribution, detect anomalies, and send notifications. It can also show which aspects of the data have issues, speeding up time to resolution. This saves time from debugging so that data scientists and machine learning engineers can spend more time developing and deploying models that provide value for your business.

The WhyLabs platform monitors data, whether it is being transformed in a feature store, moving through a data pipeline (batch or real-time), or feeding into AI/ML systems or applications. The WhyLabs platform has two components, the open-source whylogs logging library and the WhyLabs Observatory. The whylogs logging library fits into existing tech stacks through a simple Python or Java integration. It supports both structured and unstructured data. No raw data is copied/duplicated/moved out of the environment, eliminating any risks of data leaks. whylogs analyzes the whole dataset and creates a statistical profile of all the different aspects of the data. By creating statistical profiles, whylogs captures rare events, seasonality, and outliers that otherwise might be missed with sampling as well as keeping sensitive financial data private.

Once whylogs profiles are ingested into the WhyLabs Observatory, monitors are enabled and anomaly detection is run on the profiles. Pre-built data monitors can be enabled with just a click to look for data drift, null values, data type changes, new unique values, and model performance metrics (e.g. Accuracy, Precision, Recall, and F1 Score). If there isn’t a pre-built monitor available for data issues/model metrics, there is a guided wizard on creating a custom monitor available. If anomalies are detected, notifications are generated showing which aspects of the data/model have issues. For more on data and model monitoring, go here.

Conclusion

Financial fraud is a complex and constantly evolving problem! As we mentioned in our recent financial fraud classification blog, every $1 of fraud loss costs financial services firms $4 in losses. Machine learning models offer a powerful solution for fraud detection, but they must be properly monitored and maintained to be effective. Implementing a robust model monitoring system is crucial for ensuring the accuracy and reliability of ML models in detecting fraud, and can help minimize false positives and improve operational efficiency. By investing in a monitoring solution, businesses and financial institutions can stay ahead of fraudsters and protect themselves from financial losses and reputational damage.

Please check out the Resources section below to get started with whylogs and WhyLabs. If you’re interested in learning how you can apply data and/or model monitoring to your organization, please contact us, and we would be happy to talk!

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Resources

• WhyLabs - free sign-up
• whylogs documentation
• WhyLabs documentation
• Rsqrd AI Slack community