AI-driven Fraud Detection in Banking

Fraud lurks like a dark shadow over the financial industry. Cybercriminals are growing more adept in their approaches, and standard fraud detection technologies simply cannot keep up. Now, the discussion has shifted to fraud detection utilizing AI in banking, which is a light of innovation and a potential solution in the ongoing struggle against dishonesty and deception.

 

The Association of Certified Fraud Examiners found that fraud costs organizations an estimated 5% of their annual revenue, amounting to billions annually. Banks may reduce these costs by detecting unscrupulous actors early on — or, better yet, by noticing behavioral trends and patterns that can prevent fraud before it occurs. To do this, financial institutions need to incorporate AI into their fraud detection tactics.

 

What is AI in Banking?

 

Artificial intelligence (AI) is becoming an increasingly important technology for the banking industry. When used to power internal operations and customer-facing applications, it can assist banks in improving customer service, fraud detection, and financial and investment management.

 

Financial services companies, such as banks, have embraced digital transformation initiatives in order to stay ahead of technological trends, increase their competitive advantage, and provide valuable services and better customer experiences.

 

The advent of AI technologies has increased the importance of digital transformation, which has the potential to remake the industry and determine which companies thrive.

 

Historically, incumbent financial service firms have struggled to innovate. A McKinsey study1 (link sits outside ibm.com) discovered that huge banks were 40% less productive than digital natives. Many growing banking firms are pioneering artificial intelligence use cases, emphasizing the need for incumbent banks to catch up and innovate.

 

Natural language processing (NLP) has long been used by investment banks to parse massive volumes of data that they either generate internally or obtain from third parties. They employ NLP to analyze data sets in order to make more educated decisions about critical investments and wealth management.

 

How AI is used to prevent Banking Fraud

 

Bank fraud takes numerous forms, ranging from obtaining consumers' account numbers to creating false accounts and incurring costs. AI can detect several types of bank fraud, reducing the impact of unscrupulous actors. 

 

1. Forged Documents

 

Banking relies on several document-based identity checks to prevent fraud — pay stubs, tax statements, and utility bills, for example. With so many paper and digital documents, this creates many opportunities for fraudsters to use forged or fake documents to open accounts and quality for loans.

 

It’s easier than you think. While AI can be used to prevent document fraud, it’s also a tool that enables fraud. Fraudsters can use AI to generate fake IDs and other document types. These fakes are extremely realistic and can not only fool the human eye, but also identity verification technology. 

 

2. Identity Theft.

 

Identity theft is a widespread problem in which fraudsters pose as someone else in order to obtain money, products, and services in their name without repaying the bill. The Bureau of Justice Statistics revealed that over 23.9 million persons in the United States experienced identity theft in 2021.

 

AI can detect identity theft by analyzing large quantities of data in real time and identifying patterns. This proactive method allows banks to detect and prevent fraudulent transactions before they happen.

 

3. 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 behavior. 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.

 

4. Phishing

 

Phishing is the act of impersonating an individual, corporation, or entity in order to obtain personal information, such as credit card numbers and passwords, by email or other forms of communication. Depending on the information available to the cyber criminal, phishing typically results in data breaches and/or financial loss. Banks and financial organizations are especially vulnerable to phishing, according to Scaler's 2023 ThreatLabz Phishing Report, which identified banking, insurance, government, healthcare, and education as the top five areas attacked. 

 

Also Read | Best 5 AI-Powered Content Generation

 

Challenges to AI in Banking

 

Introducing AI into banking is not without risks and challenges. According to the IBM Institute for Business Value 2024 Global Outlook for Banking and Financial Markets survey, more than 60% of banking CEOs are concerned about new AI vulnerabilities. This includes:

 

1. Cybersecurity

 

Generative AI technology can help with fraud prevention and compliance management, but it also introduces hazards. Embedding open AI tools and technologies into banking IT systems raises security concerns since AI models are particularly lucrative targets for bad actors. That is why banks require a comprehensive AI governance framework that successfully balances innovation and risk management.

 

2. Legal ambiguity in operations

 

Generative AI models require training on current data sets to be effective. There are also some unresolved questions about whether analysing publicly available data, such as news headlines and instructional films, constitutes copyright infringement4(link located outside of ibm.com). One option to circumvent this problem is to employ AI models trained using bank-owned data, such as customer service encounters or private research.

 

3. Prejudice from model bias

 

Banks are increasingly engaging in environmental, social, and governance (ESG) activities to demonstrate openness and accountability for their decisions. Because AI models are trained on human-created data, they may inherit some of the biases that affect humans. Banks must eliminate bias in how they promote products and judge creditworthiness, which has historically disadvantaged certain ethnicities.

 

Also Read | AI's Breakthrough in Financial Fraud Investigation

 

Future of Banking and AI 

 

Banking organizations are under increasing pressure to digitally change. Customers expect automated experiences with self-service capabilities, but they also want encounters to feel personalized and distinctively human.

 

Banks continue to prioritize AI investment in order to stay competitive and provide clients with increasingly sophisticated tools for managing their money and investments. Customers continue to prioritize banks that can offer personalized AI applications that help them gain visibility on their financial opportunities.

 

In the future, banks will advertise their use of AI and how they can deploy advancements faster than competitors. AI will help banks transition to new operating models, embrace digitization and smart automation, and achieve continued profitability in a new era of commercial and retail banking.