How banks leverage machine learning in fraud detection

The ever-evolving landscape of fraud demands sophisticated tools for banks to combat fraud. Machine learning has emerged as a powerful weapon in this fight, empowering banks to identify and prevent fraudulent activity with unmatched accuracy and efficiency.

In this article, we’ll explain what machine learning is, its benefits, and how banks can use it for successful fraud detection.

What is machine learning?

Machine learning involves teaching computers to identify anomalous or suspicious behaviour by analysing data patterns. In the financial industry, it helps detect fraud by recognising deviations from a customer's typical transaction or spending habits, such as an unexpectedly large purchase made abroad. As these systems evolve and become more intelligent, they become increasingly adept at promptly and accurately identifying fraud - saving money and protecting consumers.

There are two types of ML:

• Supervised learning uses labelled datasets to train a model to recognise patterns and predict outcomes.
  
  For example, a machine might be presented with labelled data indicating that individuals with moustaches are male and those without are female. Once trained, the model would categorise all individuals with moustaches as male and those without as female.
  
  Supervised machine learning for fraud detection can result in exceptional accuracy because it trains with large quantities of data before going live. However, it has limitations when faced with fraud techniques it was not exposed to during the training phase. That’s where unsupervised learning comes in.

• Unsupervised learning involves training a model on unlabelled data, where data points do not have predefined categories or labels. The goal is for the machine to identify patterns, similarities and differences within the data.
  
  For example, if a machine is given a dataset of individuals with and without moustaches, without any labels, it would use unsupervised learning to differentiate patterns and group individuals into two distinct categories: those with moustaches and those without.
  
  One of the big advantages of unsupervised learning models is in their proactivity. They can learn and adapt to real-time evolving fraud patterns without instruction, putting banks on the front foot against fraud.

How banks leverage ML in detecting fraud

Machine learning (ML) is revolutionising fraud detection by enabling financial institutions to analyse vast amounts of data in real time, identify suspicious patterns, and adapt to evolving fraud techniques. By leveraging ML, banks can enhance both customer onboarding and transaction monitoring processes, ensuring robust protection against fraud.

Here's how ML empowers banks’ customer onboarding and transaction monitoring:

Real-time risk assessment and anomaly detection

ML algorithms can analyse customer and transaction data in real time, providing immediate assessments of fraud risk. These models continuously update risk scores based on new information, allowing for adaptive fraud detection. This capability enables banks to take swift action to prevent fraudulent activities, whether during the onboarding process or in ongoing transactions.

Pattern recognition and behavioural biometrics

ML can identify unusual patterns in customer data that may indicate fraudulent activity, such as inconsistent addresses, suspicious transaction histories, or rapid account changes. Additionally, ML can analyse customer behaviour, including typing patterns, mouse movements, spending habits, transaction locations, and login times, to detect signs of unauthorised access or deviations from normal behaviour.

Adaptive learning

ML models can learn from new data and adapt to evolving fraud techniques, ensuring their effectiveness over time. By incorporating feedback from fraud investigators, these models can be refined to improve accuracy and stay ahead of emerging threats. This continuous learning process helps banks maintain a high level of security and efficiency in both customer onboarding and transaction monitoring.

Why should banks implement ML with their fraud management tools?

Human intervention remains crucial in fraud detection. Human error and reviewing speed may be influencing customer satisfaction. By implementing machine learning in banks’ fraud management tools can reduce human error and enhance processing time, other benefits of implementing machine learning includes:

• Adaptability to emerging fraud
  
  ML serves as a powerful tool that significantly enhances an organisation's ability to adapt to ever-evolving fraud patterns. In the dynamic landscape of financial crime, fraudsters continuously develop new techniques to exploit vulnerabilities. Traditional fraud detection methods often struggle to keep pace with these rapid changes.

• Reduce false positives
  
  ML algorithms can analyse vast amounts of data to identify subtle patterns and anomalies that indicate genuine fraudulent activity, minimising the risk of false positives. By reducing false alarms, ML frees up fraud investigators to focus on high-risk cases, improving efficiency and reducing their overall workload.

• Lower burden on fraud investigators’ workload
  
  ML can automate routine tasks, such as flagging suspicious transactions and conducting initial investigations. This frees up investigators for more complex cases. Machine learning algorithms can also prioritise cases based on risk, ensuring that investigators focus on the most critical threats as a priority.

• Save operational costs
  
  ML-powered fraud detection can streamline processes and reduce the time and resources required to identify and investigate fraudulent activity. By preventing fraudulent transactions, banks can avoid financial losses and lower the associated costs of investigations and remediation. This can lead to significant cost savings for banks and relieve the burden on fraud investigators, allowing them to focus on more complex cases.

• Increase efficiency in fraud detection
  
  ML can analyse transactions in real time, enabling banks to detect and prevent fraud before it causes significant damage. Even as new fraud techniques emerge, machine learning models can continuously learn and adapt, making them more accurate and responsive over time.

• Mitigate fraud risk
  
  ML can identify emerging fraud trends and patterns, allowing banks to take proactive measures to protect themselves and their customers. By strengthening fraud detection capabilities, banks can ensure they comply with regulations, improve their reputation, and build customer trust and loyalty.

Key steps to successful ML implementation

Implementing machine learning (ML) successfully requires a strategic approach that encompasses several key steps.

First, it's essential to define clear objectives and identify the specific problems ML is intended to solve, such as increasing detection accuracy or reducing false positives. Next, selecting the appropriate ML model based on data volume, complexity, and urgency is crucial. Ensuring high-quality and sufficient data for training and testing the models is another critical step.

Additionally, robust infrastructure and advanced tools are necessary to support ML development and deployment. Assembling a skilled team of data scientists and engineers is vital for developing and maintaining effective models. Addressing ethical considerations, such as potential biases and privacy risks, ensures compliance with legislation and best practices. Continuous monitoring and improvement of ML models help adapt to evolving fraud techniques, while prioritising customer experience ensures that the implementation enhances overall satisfaction without causing disruptions. By following these steps, financial institutions can leverage ML to enhance their fraud detection capabilities and achieve their goals.

Why GBG machine learning?

GBG's machine learning solutions are tailored to fit organisations of all sizes.

We offer two primary model types:

1. Bespoke models: Ideal for organisations with ample historical data, these models are custom-built to meet specific needs.
2. Expert models: Pre-trained on extensive APAC regional data, our expert models are perfect for organisations with limited historical data.

Our user-friendly platform empowers fraud investigators and data scientists to set up rules without coding expertise. To help your team get the best from the platform, we also provide comprehensive training and ongoing support, including model retraining as needed.

We understand the importance of efficient transaction processing. Our machine learning solutions are designed to minimise any potential impact on transaction times, ensuring you can continue to deliver a seamless customer experience.

Conclusion

Machine learning is transforming the way banks approach fraud detection. It increases fraud detection accuracy, reduces false positives, frees up resources, and lowers costs. However, as with any new technology, achieving these benefits requires careful planning and implementation. That’s why it’s important to partner with the experts to ensure you can unlock the true advantages of machine learning in your fraud detection strategy.

As a trusted provider of fraud detection solutions, GBG offers customisable ML models designed to meet the evolving needs of banks of all sizes. Whether you have extensive historical data or limited resources, we have the tools to empower your team. Our user-friendly platform and comprehensive training ensure you can leverage ML's full potential to detect and fight fraud

Schedule a demo to discover the capabilities of machine learning in fraud detection and reducing false positive alerts.