AI in fraud detection

Published on 28/10/2021 by Sukanya Awasthi

Organisations worldwide are adopting artificial intelligence (AI) to expand, automate, and analyse their business capabilities. Gartner estimates that cloud-based AI will grow by five times from 2019 to 2023, making it a popular cloud service.

The use of AI can be helpful in fraud detection

What is AI?

AI can be described as technology that mimics human behaviour and performs tasks such as learning, problem-solving, and decision making. According to Gartner, AI interprets events, automates decisions, and takes actions using advanced analytical and logic-based techniques.

AI can potentially revolutionise the banking and finance industry, and financial technology (FinTech) firms are looking to utilise AI-based alternatives to prevent financial fraud. In 2020, the UK alone saw fraud of over £1.26 billion. Such numbers suggest that it may be best to make fraud detection a priority, and AI could help with that.

In this article, we will explore how AI is changing the financial services industry, the benefits of using AI in fraud detection, and the challenges of using an algorithm-powered program to detect fraud.

Use of AI in different industries

In the last several years, AI may have become one of the more prominent choices for automating processes across different industries. Many businesses are using AI to automate mundane tasks, cut down on operational and manpower costs, better understand their consumers, customise the user experience, and analyse data.

AI can be applied in various industries, including:

  • Manufacturing  
  • Finance 
  • Healthcare 
  • Retail and eCommerce 
  • Technology 
  • Education
  • Automobile

Successful early adopters of AI have leveraged practical machine learning (ML) solutions to deliver business value. ML is a subset of AI and uses previously collected data to improve system algorithms for learning and analysis. From robotic surgeries to improving SEO rankings through ML, self-driving cars, chatbots, and virtual assistants for eCommerce, it’s fair to say that AI is gaining ground across various industries.

AI in fraud detection

Every time you get a call from your bank after making a purchase using your credit card, it’s usually AI-powered systems running in the background helping your bank with fraud detection. These calls —along with push notifications or SMS verifications— are a form of two-factor authentication initiated to verify the identity of the person who has made the transaction.

AI also has the potential to identify strange or out of the ordinary purchase patterns and behaviours, which can then be used to alert banks whenever any potentially suspicious transaction is conducted at the customer’s end. Not just that, AI can also prioritise suspected fraudulent activities so that investigations can happen on the basis of urgency or importance.

ML techniques —which are developed by using the historical data of consumers— can remember the usual spending patterns of the customers so that whenever it spots an anomaly, it can raises a flag, thereby making the AI system better equipped for identifying fraud.

The benefits of using AI in fraud detection

Financial institutions have always struggled with fraud. Massive amounts of data and traffic make it even more difficult to keep instances of fraud in check. Algorithms for fraud detection can potentially be used in the commercial sector and could prove to be useful analytic tools. We will now list some benefits that stem from the use of AI in fraud detection.

Real-time data processing

AI-powered systems can process data in real-time, which may prove to be one of their biggest advantages in detecting fraud across different banking services. With real-time monitoring and processing of data, it becomes easier to classify, store, and visualise data. Not only that, but instant data processing also helps flag outliers and data anomalies for immediate remedial action, speeding up fraud detection and resolution.

Better customer assistance

Before the introduction of AI in the banking sector, customer queries were usually resolved by the customer support staff, which sometimes could be a prolonged process. AI can help reduce the wait-time of detecting and analysing fraud by automating the process, hence assisting banks in responding to customers in a timely manner. AI could also potentially enhance the customer experience by reducing false positives (erroneously flagging a transaction as fraud) during fraud detection processes.

Offers a cost-effective solution

What makes AI-driven automated fraud detection systems cost-effective is that they free up a lot of manual resources that otherwise might be busy attending to manually monitoring fraudulent or suspicious transactions. These resources could then be utilised for other complex tasks that require human intervention.

The challenges of using AI in fraud detection

Although AI may be potentially changing the FinTech landscape in the UK and all across the globe, there are still some challenges businesses may face when using it, and while integrating it with current company processes, some of which are listed below.

Risk of data leaks

One of the major challenges involved is the risk of privacy leaks. Many organisations may be afraid that their data would be compromised if the AI system is hosted on the cloud. This could make them a bit reluctant to incorporate it within their own processes.

Low quantity of data

Another aspect is that machine learning models require a large quantity of data to become accurate sources. For smaller businesses, the amount of data available for processing may not be enough. Additionally, if the data is presented in an unstructured (not organized in a pre-defined manner) or non-standard manner, it becomes more difficult to make good use of it.

Lack of proper infrastructure

Sometimes banks do not have sufficient infrastructure to support AI and ML technologies. Additionally, despite having all the necessary resources, they may lack the data infrastructure required to assess user activities and behaviours in order to establish baseline knowledge —information or data collected at the start of a specific time period, to which future changes can be compared— of what constitutes fraud.

In a nutshell

Human inspections are often less reliable than ML approaches. AI systems are usually prompt, scalable, and capable of processing massive volumes of data in real-time. Considering this, it may just be the right time for banks to adopt AI, especially in light of the fact that sophisticated detection systems helped the finance industry by preventing £1.6 billion of fraud in 2020.

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This article may refer to products, programs or services that are not available in your country, or that may be restricted under the laws or regulations of your country. We suggest that you consult the software provider directly for information regarding product availability and compliance with local laws.

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About the author

Content Analyst for the UK market. Committed to offering insights on technology, emerging trends, and software suggestions to SMBs. Plant lady, café hopper, and dog mom.

Content Analyst for the UK market. Committed to offering insights on technology, emerging trends, and software suggestions to SMBs. Plant lady, café hopper, and dog mom.