Banks and financial institutions are increasingly utilising artificial intelligence (AI) technology for a plethora of reasons, such as credit scoring to assess a borrower’s risk more accurately or for enhancing customer service with virtual assistants. But one of the most important use cases of AI is in the fight against fraud and money laundering.
Onfido’s 2022 Identify Fraud Report has identified a concerning 47 per cent increase in identity fraud since 2019, with financial services remaining one of the highest targeted sectors.
Further research from McAfee reveals cybercrime costs the global economy $600billion annually, while consulting firm Accenture forecasts cyberattacks could cost companies $5.2trillion worldwide by 2024. Global payment card fraud losses, specifically, amounted to $28.58billion in 2020, says a Nilson report.
Payments card fraud is such a concern that the UK recently implemented tighter anti-fraud checks on card payments with new Strong Customer Authentication (SCA) rules coming into force in March 2022, activated for almost all online purchases above £25 to provide a greater level of security against fraudsters.
With cybercriminals getting ever more inventive with their malicious get-rich-quick schemes, fraud prevention and detection has never been more critical.
Protecting payments and transactions with AI
By utilising sophisticated technologies and large amounts of data via AI, financial industry members can fight against fraud in innovative ways, analysing data and training algorithms to help improve their ability to recognise fraudulent activity and tackle it quickly.
According to a global survey of over 500 financial services professionals by NVIDIA, financial institutions know all too well the key role AI can play in maintaining a competitive advantage. Eighty-three per cent of respondents of its State of AI in Financial Services study indicated AI is important to their company’s future success, while a further 34 per cent of respondents believe that AI can increase their company’s annual revenue by 20 per cent or more. Fraud detection involving payments and transactions was the top AI use case across all respondents at 31 per cent, followed by conversational AI at 28 per cent and algorithmic trading at 27 per cent.
“Fraud results in significant losses to the bank and ultimately to the consumer, either indirectly through potentially increased prices for products or directly by having funds stolen from their account,” comments Kevin Levitt, director of industry business development, financial services at NVIDIA.
“Banks must stay a step ahead of the bad actors and AI is a critical tool to protect the broader financial ecosystem.”
A constant fight with fraud
Computing giant Dell Technologies agrees and suggests the cost of credit card fraud would be much higher were “payment processors not waging a constant fight against fraud using all the tools at their disposal”.
Dell’s Fighting Fraud The Smart Way – With Data Analytics and Artificial Intelligence report outlines how tools including AI and machine learning can analyse transaction data in milliseconds, weeding out the fraudulent transactions from the genuine ones.
These technologies that go into fraud prevention systems enable financial institutions to instantly analyse data, while continually training algorithms to help them improve their recognition of verified user biometrics and potentially fraudulent activities.
Parallel efforts are helping the industry ward off bad players on the merchant side. This results in a more “trustworthy transaction experience for legitimate cardholders and merchants and more digital barriers to stop the criminals who try to exploit vulnerabilities in the payment systems.”
Saving time and costs
Another key benefit of AI in fraud prevention is the provision of more cost-effective solutions. AI automation can free up manual resources that can be allocated elsewhere, leaving the AI models to do the intensive work of initial detection.
Programmes can also detect fraud and prevent it there and then, rather than weeks later when chargebacks occur. Customer experiences can be enhanced in this way, as well as reducing wait times for analysing fraud and allowing companies to respond to customers in a more timely manner.
AI in action
Trintech, a financial software-as-a-service (SaaS) firm and one of the world’s leading providers of financial close software, has invested heavily in artificial intelligence and automation, integrating these with Microsoft SQL Server running on Dell PowerEdge Servers. The AI-powered solution automates routine functions for its accounting and financial services customers.
“Trintech is in the process of building out machine learning and AI technologies that help improve controls in financial close processes to detect fraud and lower costs without increasing risk profile and still satisfying audits,” says Michael Ross, chief product officer at Trintech. “We believe this will be the next revolution in financial close automation.”
Trintech utilises AI to help its customers:
- Evaluate and quantify risk across various financial close processes, entities, and functions
- Automate and optimise workflows based on risk
- Leverage insights into compliance controls through data analysis
- Develop best practices for risk evaluation, controls, automation and optimisation, based on benchmarking data
- Evaluate and quantify key market trends to determine impact and drive proactive risk prevention measures
“Over the past year, Trintech has invested heavily in its Financial Controls AI capabilities to help our customers reduce financial risk from multiple angles, save time and resources, and ultimately transform their operations,” says Ross.