How can data analytics tackle banking fraud
While we have touched on fraud and fraud detection in the past, the following blog post will explore fraud in the context of the banking industry, its challenges and how data analytics can combat fraud. Banking fraud is a tremendous cost to firms. Experts expect fraud to reach $31.67 billion by 2020, more money then Coco Cola’s profit. If data analytics can save even 1/10th of that money, that’s billions of dollars saved from fraud.
Why is banking fraud hard to combat?
Banking fraud is hard to combat because it comes with several challenges that render current security systems ineffective.
Legal or illegal transactions:
Fraudsters use legal transactions to disguise their illegal activities. Current security systems always try to flag transactions. However, systems get it wrong and sometimes flag transactions from customers. If banks are to combat fraud, they need a system that can distinguish legal from an illegal transaction.
Different types of fraud:
Employee fraud, cheque kiting, fraudulent loan applications and empty ATM envelope deposits are just some examples of banking fraud. Creating a security system to account for these fraud techniques is both challenging and complicated.
With that being said, how can data analytics combat banking fraud?
Identify the patterns
Customer transactions like bank withdrawals or cheque deposits follow certain patterns. Data analytics can analyse these trends and compare them against fraud indicators. Analytics can study the patterns in several transactions for millions of customers when something happens. For example, when an unusually large amount of money is transferred the account can then be flagged for further investigation.
The key to distinguishing banking fraud from legitimate transactions is to find the patterns that indicate illegal activity. The patterns of banking fraud should be identified as the transaction is happening and flagged in real time. Data analytics algorithms can study the patterns of a transaction and flag it if it shows any signs of fraud. Most fraudsters follow certain patterns of banking fraud – for example, tax-related scams take place during the tax season.
Integrating data structures
Data comes in two general categories, structured and unstructured data. Structured data refers to information like banking transactions, chequing deposits and more. While unstructured data refers to information like videos and social media content. Integrating these different data structures is challenging because their format and presentation are different. However, by integrating structured and unstructured data, banks get a detailed picture of banking fraud.
The advantage of data analytics is that it can integrate structured and unstructured data to form a cohesive, whole picture. Integrating data reveals new trends not found using other means. Thus, analytics programmers can merge data from different sources to reveal new information about banking fraud.
Banking fraud is done through a series of transactions or in conjunction with several people, and the connections are not immediately obvious. However, deep analytics programs can uncover these connections to catch fraudsters. For example, banks discovered that fraudsters test the limits of the security system through legitimate transactions, before committing fraud. This new information is invaluable for banks because it gives additional insight into how fraudsters work. Knowing the techniques fraudsters use is pivotal for combatting fraud in the future.
According to research, fraud activity that lasts for a day costs customers $34 per claim. By contrast, fraud caught after three to five months costs $1061 per claim. Hence, here’s clear proof that when fraud activity is nipped in the bud, it saves money for both customers and the institutions. Moreover, a bank’s reputation is boosted because customers are reassured their financial information.
The sheer processing power of data analytics makes it the best method for detecting fraud quickly. Data analytics and electronic methods speed up fraud detection by 18 days compared to conventional methods. Data analytics is effective because of its real-time capability, while machine learning allows analytics platforms to learn about new techniques quickly. Fraud transactions are flagged in real-time, making it easier to catch them quickly.
Banking fraud costs the industry billions of dollars because fraudsters use sophisticated techniques that make it hard to detect illegal activity. However, data analytics can help banks catch and prevent fraudulent activity because of its capacity to process millions of transactions, real-time capability and deep analysis that uncovers trends in data.
Data analytics is not just useful for combatting bank fraud, it can be used in different industries ranging from transport to aviation. Head to our blog to find out more.