A Case Study on Barclays Fraud Detection
Introduction
Financial fraud is like a shape-shifting villain in Marvel comics, always finding new ways to exploit advancements in technology. But fear not, for Data Science is the superhero we need to fight back. With every advancement technology makes, the fraudsters and malicious intent filled organisations take their fraud strategies to the next level. In this article, we will explore in detail about financial frauds and how data science is used to curb this virus, making use of amends to fight it effectively & efficiently using the famous Barclays Financial fraud incident as a case study to conclude.
What is financial fraud?
The term fraud could be generally defined as misrepresentation of facts for one’s benefit, whilst the other individual/entity is at the risk of loss in any which way. A financial fraud can be defined as tricking individuals or entities for financial gain by concealing or falsifying essential information. It affects organisations of all sizes and industries and it takes many forms, from basic identity theft to intricate corporate schemes aimed at deceiving investors and regulators. The consequences of financial fraud are severe, resulting in financial losses, consumer fraud, damage to reputations, and regulatory penalties. To protect their interests and maintain trust, organisations must invest in robust fraud detection and prevention.
The British ‘Fraudemic’ trend
The UK is generally considered to have become a global centre for financial fraud (including scams). As an English speaking country with a highly digital population, an ageing demographic, and a strong economy, the UK is an attractive source of potential victims for criminal gangs.
Frauds have been seeing all time high in the UK, post-pandemic times. Fraud is the latest major happening crime in the kingdom where the sun never sets. The BBC states there’s been a shopping spike of 101% lately and gave birth to the term Fraudemic. The below graph shows the accurate info-
The Barclays scandal - Hall of shame
According to The Times of NewYork, In 2017 The British Serious Fraud Office charged Barclays and four of the bank’s former executives with conspiracy to commit fraud. The charges come after a five-year investigation into the London-based lender’s fund-raising at the height of the 2008 financial crisis.
The accused are John S. Varley, a former chief executive; Roger A. Jenkins, a former chairman of investment banking for the Middle East; Richard W. Boath, a former head of investment banking; and Thomas L. Kalaris, a former head of the bank’s wealth division. Barclays and Mr. Varley and Mr. Jenkins were also charged with providing unlawful financial assistance.
Barclays had raised nearly 12 billion pounds, or about $15 billion at current exchange rates, from an arm of Qatar’s sovereign wealth fund and other investors. Such deals allowed the bank to avoid a bailout. The fraud office has been scrutinising whether the bank properly disclosed an agreement with Qatar that involved a payment for advisory services. It was also examining a $3 billion loan that Barclays made available to the Qatar government.
How does this fraud work?
Fraudsters have developed sophisticated techniques to trick victims into making payments. They spend a lot of time and resources finding victims, gaining their trust, and ultimately convincing them to make the payment. This involves multiple interactions with the victim. compromised genuine web sites or 100% malicious sites.
A helpful way to understand the development of a scam is looking at the ‘killchain’, which traces out the steps involved.
The killchain system
The victim journey
Reducing the scams
Enter Data Science- The game changer
The war on fraud is a never-ending one. Fraudulent actions cost billions of dollars annually, and the techniques employed by these criminals are ever-changing. This dynamic world challenges traditional detection techniques because they frequently rely on inflexible criteria that need to be revised.
This is where Data Science comes into play. With the use of advanced analytics and big data, it proves to be a formidable tool to play against financial frauds.
How does Data Science recognize fraud?
Large-scale datasets are analysed using machine learning techniques in data science to look for hidden patterns and anomalies that could be indicators of fraud. The days of just following predetermined rules are long gone. Today, algorithms are able to continuously adapt and learn in order to identify and eliminate unusual and emerging threats.
Fraud detection is a set of proactive measures undertaken to identify and prevent fraudulent activities and financial losses. Its main analytical techniques can be divided into two groups:
Statistical: statistical parameter calculation, regression, probability distributions, data matching
Artificial intelligence (AI): data mining, machine learning, deep learning
Machine learning represents an essential pillar for fraud detection. Its toolkit provides two approaches:
Supervised methods: k-nearest neighbours, logistic regression, support vector machines, decision tree, random forest, time-series analysis, neural networks, etc.
Unsupervised methods: cluster analysis, link analysis, self-organising maps, principal component analysis, anomaly recognition, etc.
There is no universal and reliable machine learning algorithm for fraud detection. Instead, for the real-world data science use cases, several techniques or their combinations are usually tested, the model predictive accuracy is calculated, and the optimal approach is selected.
The main challenge for fraud detection systems is to rapidly adapt to constantly changing fraud patterns and fraudsters' tactics and to promptly uncover new and increasingly elaborate schemes. Fraud cases are always in a minority and are well concealed among the real transactions.
Barclays new measures against fraud detection using Data Science
For Barclays, Data Science played a crucial role in detecting the fraud and preventing similar incidents in the future. The bank implemented a new fraud detection system that uses machine learning algorithms to analyse transactional data and identify potential fraud. The system is constantly learning and adapting to new fraud patterns, making it more effective at detecting and preventing fraud over time.
The new system uses anomaly detection algorithms to identify unusual patterns in transactional data, which may indicate fraudulent activity. The system also uses natural language processing (NLP) algorithms to analyse customer interactions and detect anomalies in communication patterns, which may indicate potential fraud.
Furthermore, Barclays has also implemented a data governance framework that ensures data quality, transparency, and accountability. The framework includes data validation, data lineage, and data privacy controls, which help to ensure that the data used in fraud detection is accurate, complete, and secure.
Conclusion
In conclusion, financial fraud is a serious issue that affects the entire financial system. However, with the emergence of data science, banks have a new weapon in their arsenal to combat fraud. The Barclays case study highlights the power of data science in detecting and preventing financial fraud and serves as a reminder that banks must continue to invest in robust fraud detection and prevention systems to protect their customers and maintain trust in the financial system.