The amount of credit card fraud committed jumped to over 40% in one year from 2019 to 2020 in the United Kingdom. Since 2015, fraudulent transactions grew by 600% worldwide, particularly within mobile banking. In the United States, $24.26 billion was lost due to payment card fraud worldwide from 2018. These are some of the damning statistics of fraudulent financial transactions that occur, which continues to be the case today. Financial and banking institutions are especially hit hard by it, and digital business has only heightened the opportunity for cybercrime to become more prominent.
What is giving a deeper impression of the finance and banking sectors is how difficult and time-consuming their fraudulent strategy is to resolve fraudulent cases. Filtering through paperwork to check the status of a case, responding to fraudulent queries within a certain amount of days, it all makes for a redundant strategy to manage fraud.
Thankfully, the rise of digital technologies, particularly Artificial Intelligence is identifying new ways to improve strategy in finance and banking to reduce fraud. Cyber-criminals try their best to access customer accounts and money, and financial providers are now integrating Artificial Intelligence and Machine Learning operations to use big data to their advantage. Here are some ways in which finance and banking are benefitting from AI in the fraud domain:
- Tracking Behaviour Activity – According to Deltec Bank, Bahamas, “Machine learning incorporates behavioral analytics to anticipate the online behavioral trends of a customer, such as the trend of transactions.” The analytics can help track any anomalies in online activity or payments. By doing this, the technology can halt transactions where the algorithm considers them to be fraudulent. The technology can assume this is illicit behavior and request the customer to confirm whether this is a fraudulent transaction or not through modes of SMS.
- Accurate Data Analysis – One of the key benefits of big data is that machine learning algorithms can use this effectively to analyze large amounts of transactional data and determine maliciousness in real-time. By detecting patterned behavior, ML can detect the complexity of them that may not be easy or may be time-consuming for the average financial analyst.
- Anomaly Detection Rules – Danske Bank worked with Teradata to improve the capability of identifying fraud. This improved fraud detection capability by 50%. Not only is that a positive, but its success in reducing the need for human analyst resources leading to the increased cost to employ them. Some of the anomaly techniques include K-Nearest Neighbour (KNN) that classifies new transaction records against previous suspicious transactions for that particular account or customer. Clustering is another technique, used to group records that are similar to each other. Clustering labels data based on similarity, something that human analysts won’t see.
- Early Warning Detections – Through AI, institutions can detect malware and phishing to identify suspicious behavior through emails sent to employees. JPMorgan Chase has applied this as part of their fraudulent strategy. Deep learning has proven to be better to detect fraudulent threats than security systems, and one of the popular fraud methods is by email and malware. Early detections allow for institutions to act on the warning immediately before an action is performed.
Banks are also implementing functions that detect fraud to improve on cases that make be considered money laundering, and also performing regulatory KYC (Know Your Customer) checks. There is no shortage of how powerful machines and deep learning algorithms are to protect customers and institutions from experiencing fraud. Fraud strategy has been increasingly painful for banking and financial sectors due to the plethora of fraudulent transactions that occur from many different branches; smartphones, credit and debit cards, and even kiosks. Whilst criminals are attempting to identify loopholes, AI is slowly beginning to bridge the gap of fraud, but there is still room for development.
To sum up, artificial intelligence is transforming fraudulent strategy within the banking and finance sectors. Due to the rise of big data and digital technologies, criminals are attempting to seek loopholes through technology to rob customers of their money online. Bankers and finances have been hit hard by fraud over recent years, and over 90% of fraud detection technologies are incorporating rules to identify suspicious transactions for review. This approach acts as a gateway to develop warning signs to halt any fraudulent activity. PayPal is a prime example of using this approach. Institutions are now beginning to appreciate the need for a more robust and advanced strategy to tackle fraud, and the banking and finance sectors are the ones that need it most.
Disclaimer: The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance and an MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.