This article was originally published on 28/10/2021.
Artificial intelligence (AI) in fraud detection offers a way for small to midsize enterprises (SMEs) to enhance security in their organisations. Discover how companies can protect themselves and their customers from fraudsters using this new automated technology.
Organisations worldwide are adopting artificial intelligence to expand, automate, and analyse their business capabilities. Gartner estimates that tools such as generative AI will increasingly be a reality in the daily lives of customers and reduce the volume of data needed for machine learning (ML) by 70% in the coming years. However, a major advantage to businesses is the opportunity these automated tools offer to enhance fraud prevention for customers.
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 could revolutionise the banking and finance industry. Financial technology (FinTech) firms are looking to utilise AI-based alternatives to prevent financial fraud. In the financial year of 2021 and 2022 there were losses totalling £2.46 billion due to fraud. Such numbers suggest fraud protection services are a priority, and AI may be able to help.
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.
How AI in fraud detection works
AI for fraud detection in banking and finance has the capacity to catch potential fraud in the act. Using data matching techniques and scanning for fraud patterns it is used to identify suspicious activity by distinguishing genuine customer transitions from fraudulent behaviour.
For instance, 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.
Fraud protection machine learning models such as these also have the potential to identify strange or out-of-the-ordinary purchase patterns and behaviours. These 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 raise a flag, thereby making the AI system better equipped for identifying fraud.
Did you know?
Many businesses are using AI tools to automate mundane tasks, cut down on operational and manpower costs, better understand their consumers, customise the user experience, and analyse data.
Successful early adopters of AI have leveraged practical machine learning 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, there are many examples of where AI is gaining ground across various industries.
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. Below we have listed a number of the key benefits 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 data processing, 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.
It 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. A few examples of these 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.
Did you know?
A study conducted by GetApp in 2023 revealed that a high number of consumers wanted more transparency from companies with regard to their data practices.
Respondents preferred the following steps be taken from companies to build trust:
- Providing a clear statement on how they keep data safe on their homepage
- Sending an email with information on data risk mitigation practices
- Newsletter updates about company data security practices
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.
Detecting fraud patterns using AI and machine learning
AI fraud detection systems can cover a lot more ground than humans and quite often with a better level of reliability. These systems can prove prompt, scalable and capable of processing massive volumes of data in real-time when used in the correct circumstances.
This can be crucial to improve customer experience, enhance prevention of issues such as credit card fraud and monitoring suspicious activities effectively. Keeping potential fraud in check is something businesses handling financial transactions cannot afford to let slip as the costs in terms of financial losses and reputation can be high. AI for fraud detection fortunately can prove a helpful ally in this battle to stop this illegal activity.