AI in the banking sector: How fraud detection with AI is making banking safer
AI has several use cases in banking and fintech, but fraud detection and prevention tops the list. The emergence of digital banking and online payment platforms means that banks are no longer just brick-and-mortar establishments. While this spells convenience for all stakeholders, it also opens the doors to fraudsters and miscreants in the financial space.
Why use AI in banking fraud detection?
Online fraud statistics are alarming. Cybercrime costs the world economy $600 billion annually, which is 0.8% of the global GDP. Studies show that in the first quarter of 2021 alone, fraud attempts rose 149% over the previous year – fuelled, no doubt, by the post-Covid increase in online transactions. In response, more than half of all financial institutions have stepped up to employ AI to detect and prevent fraud in 2022.
> AI makes fraud detection faster, more reliable, and more efficient where traditional fraud-detection models fail. There are several reasons for using AI for fraud detection in banking, such as –
Efficiency and accuracy
AI-powered systems can process huge amounts of data faster and more accurately than legacy software. It significantly reduces the error margin in identifying normal and fraudulent customer behaviour, authenticates payments faster, and provides analysts with actionable insights.
AI can detect and flag anomalies in real-time banking transactions, app usage, payment methods, and other financial activities. This accelerates fraud detection and helps block maleficence and prevent fraud.
Machine learning (ML) advantages
Rules-based solutions can only detect the anomalies that they are programmed to identify. AI models use complex ML algorithms that self-learn by processing historical data and continuously attune themselves to evolving fraud patterns. ML can also build predictive models to mitigate fraud risk with minimal human intervention.
Enhanced customer experience
Besides detecting anomalies efficiently, AI in banking systems also minimises false positives. This is crucial in safeguarding the customer experience without compromising on security.
How AI banking fraud detection models work?
Here is how AI-driven fraud detection and prevention models work:
They start by gathering, processing, and categorising historical data. This includes ‘good data’ (labelled information about legitimate transactions) and ‘bad data’ (labelled information about fraudulent transactions).
Data engineers feed the machine with varied examples of banking fraud patterns to make the algorithm agile, versatile, and business-specific.
Data from every new transaction feeds back into the system. Self-learning and adaptive analytics enable the machine to incorporate the new data and adjust to the changing fraud environment, enabling it to recognise new forms of fraud.
Fraud detection using AI in banking
As organised cybercrime gets increasingly refined and complex, there is a growing need to migrate from suboptimal fraud management systems to AI solutions. Here is how AI tackles some common banking fraud types:
Cybercriminals steal a customer’s identity by hacking into their account and changing crucial account user credentials.
Since AI is familiar with the customer’s behaviour patterns, it can detect unusual activity such as password changes and contact details. It notifies the customer and uses features such as multi-factor authentication to prevent identity theft.
Phishing emails aim to extract confidential financial information, such as credit card numbers and bank passwords, by posing as authentic entities.
ML algorithms can detect fraudulent activity through email subject lines, content, and other details and classify questionable emails as spam. This alerts the user and mitigates fraud risk.
Credit card theft
Fraudsters often use phishing or identity theft to access a legitimate user’s credit card details. This allows them to transact without physically acquiring the card.
AI can detect anomalies in the card owner’s spending patterns and flag them in real time. It can also build predictive models to foretell the user’s future expenditure and send notifications in case of aberrant behaviour. The legitimate card owner can then block the card and contain damages.
Additionally, AI-driven banking systems can build ‘purchase profiles’ of customers and flag transactions that depart significantly from the norm.
Forged signatures, fake IDs, and fake credit card and loan applications are common issues in banking.
ML algorithms can differentiate between original and fake identities, authenticate signatures, and spot forgeries with a high accuracy rate. Tools such as multi-factor authentication and AI-backed KYC measures also prevent forgery.
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How can Infosys BPM help?
At Infosys BPM, we provide cutting-edge analytics solutions tailored for the banking and finance sectors. Our end-to-end fraud management systems help organisations analyse huge and complex data sets to detect anomalies, reduce false positives, and provide high levels of security.
Know more about Infosys BPM fraud management solutions and prevention offerings.