Fraud detection in banking using Machine Learning

The importance of fraud detection in banking 

Fraud detection and prevention is a crucial aspect of today’s financial industry as it prevents financial losses and maintains customer trust and loyalty. When fraudulent activities emerge not only financial institutions are at risk but also their customers. The institutions are responsible for ensuring the safety of their customers' funds and even a minor fraud incident can damage their reputation.[1]

What is fraud detection and how does machine learning help? 

Fraud detection involves identifying suspicious financial behaviours as quick as possible and accurately and separating honest individuals from those that are looking for easy money exploits. Machine learning techniques have tremendously improved the sector by enhancing the efficiency and precision of fraud detection and automating the whole process.[2] 

In the past banks relied on traditional rule-based systems to detect fraudulent activities and while these systems somewhat worked on detecting known fraud patterns, they were limited in detecting new and evolving patterns.Rules-based solutions can only detect the anomalies that they are programmed to identify.[1], [3]

Aspects of machine learning in fraud detection and prevention 

The following are just a few of the use cases of fraud detection using machine learning[4]: 

1. Anomaly detection 

Unusual patterns or deviations from normal behaviour are identified in transactions and flagged accordingly to indicate suspicious activity that might be related to fraud. The algorithms learn to differentiate legitimate transactions from illicit ones. 

2. Risk scoring 

By using machine learning algorithms risk scores are assigned to transactions or user accounts based on various factors. Transactions amounts, geolocation, frequency of transactions and other past behaviour are all indicators that are used by these algorithms before assigning a score. Users that are assigned a higher risk score have a higher likelihood of encountering fraudulent activity and by using this score organisations can prioritise resources to investigate these instances sooner.

3. Network analysis 

Having a graph analysis of fraudulent networks and collaborations can aid in uncovering these networks faster by analysing the relationships between users, accounts and devices and identifying unusual or suspicious connections. 

4. Text analysis 

Machine learning algorithms can analyse text data (emails, social media posts, reviews) and identify patterns/keywords that might indicate frauds or scams. 

5. Identity verification 

Institutions often use user-provided information (biometric data, documents) to verify the identity of individuals and prevent identity theft. 

How a ML System works for fraud detection

Logistic regression, random forests, decision trees, and neural networks are some of the most remarkable ML techniques used in financial fraud detection and creating a ML system follows these steps[5]:

1. First the data needs to be collected 

2. Features are extracted such as identity, orders, geolocation, payment methods, network data 

3. Training the algorithms 

4. Creating a model and constantly improving and updating it

Conclusion 

Fraud detection and prevention are crucial in ensuring a secure financial ecosystem and maintaining customer trust. By continuously training, machine learning models can adapt to newer scam and fraud tactics and are always prepared to detect the newest fraud patterns. 

Bibliography 

[1] J. Jeffers, “Fraud Detection in Banking Using Machine Learning,” Security Boulevard, Apr. 24, 2023. https://securityboulevard.com/2023/04/fraud-detection-in-banking-using-machine-learnin g/ (accessed Jan. 12, 2024). 

[2] “Banks Go High-Tech: Unravel Fraud with Machine Learning,” Oct. 02, 2023. https://thecodest.co/blog/banks-go-high-tech-unravel-fraud-with-machine-learning/ (accessed Jan. 12, 2024). 

[3] Infosys Limited, “Fraud detection using AI in banking.” https://www.infosysbpm.com/blogs/bpm-analytics/fraud-detection-with-ai-in-banking-sect or.html (accessed Jan. 12, 2024). 

[4] “Fraud detection using machine learning: What to know.” https://stripe.com/en-ro/resources/more/how-machine-learning-works-for-payment-fraud -detection-and-prevention (accessed Jan. 12, 2024). 

[5] S. Srivastava, “Machine Learning in Finance: Leveraging the Technology for Financial Fraud Detection,” Appinventiv, Dec. 09, 2021. https://appinventiv.com/blog/role-of-machine-learning-in-financial-fraud-detection/ (accessed Jan. 12, 2024).