Prevent Identity Theft Using AI Transform Security Landscape

AI(Artificial Intelligence) for Prevent Identity Theft

Ever wondered what’s common between money and technology? People like both money and technology to be fast and easy. That’s why FinTech innovations are making a quick entrance into the finance industry. FinTech makes it easier for consumers to connect with financial services. This urge for swift and frictionless transactions has led to an increase in real-time access to the new credits, mobile wallets, and instant transfer of goods.

But this improvement in speed and convenience has led to an increase in online frauds.

In 2017, Equifax data was compromised by the hackers and the information of over 143 million customers was exposed. Other security attacks such as WannaCry ransomware, Binance KYC breach, and DoorDash data breach has exposed the vulnerabilities in the financial cyber security globally.

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Not only this, cyber criminals successfully stole 1.2 billion euros through frauds and scams online. Moreover, FTC recorded that identity theft accounted for 14.5% of the fraud cases in 2018. It isn’t over yet. We will discuss all common frauds that were recorded in the previous years and how AI could help financial institutes to put a stop to them.

Common Types of Identity Frauds

So what types of identity frauds are more common? Let’s break it down piece by piece.

Credit Card Fraud

Credit card fraud is by far the most common type of identity theft, representing 29.1% of the total fraud cases. What is credit card fraud? What threat does it pose to financial integrity? Let’s explore…. A fraud in which fraudsters use a stolen, revoked, or lost card and use it for financial gains. Sometimes, the fraudsters use a stolen identity to register a credit card and make purchases using it. And due to no robust verification system, the banks and financial institutions are scammed.

Account takeover Frauds

Account takeover fraud resulted in £14.7 million loss during 2018. Account takeover fraud involves a criminal fraudulently using another person’s credit or debit card account by first gathering information on the victim and then contacting the card-issuing banks or company claiming to be a genuine person. This form of identity fraud is common all across the globe.

Application Fraud

With a 159% increase from the previous year, application fraud resulted in £29.4 million losses in 2018. Application fraud occurs when fraudsters use stolen or lost documents for opening an account against someone else’s name. For identification purposes, criminals may try to steal documents such as utility bills and bank statements to build up useful personal information. Alternatively, they may use counterfeit documents.

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Card has not received a fraud

This type of fraud occurs when a card is stolen in transit, after a card issuer sends it out and before the genuine cardholder receives it. Card not-received fraud losses fell by 38 percent in 2018 to £6.3 million. Criminals typically target properties with communal letterboxes, such as flats and student halls of residence, and external mailboxes to commit this type of fraud. People who do get their mail redirected when they change address are also vulnerable to this type of fraud.

Artificial Intelligence Holds the Key to Compliance and Security

Artificial Intelligence (AI), on the other hand, continues to emerge swiftly and providing security to industries, which rely heavily on data. Experts believe that AI will change the landscape of security and compliance in the financial sector.

“By not being proactive and not employing automated protection techniques, businesses are dicing with unnecessary risks and the penalties that come with compliance and security breaches.” an employee at Oracle explains.

Staying Ahead of the Curve Using AI

Regulators and financial institutes are up against the odds of compliance and to avoid cyber crimes, data theft, and payment frauds. It is forcing the financial sector to adopt next-generation tech solutions, for better planning and preparing themselves against the unidentified attacks by cyber criminals.

Built on the backbone of technologies like artificial intelligence and machine learning, these solutions can analyze loads of data and identify suspicious activities accurately and swiftly. Renowned banks such as JPMorgan, Bank of America, CitiBank, and others are adopting these technologies to improve security and become compliant with data protection laws.

A PwC study reveals, 52% of financial service executives are making investments in AI and 72% of business analyst believes that AI and ML will be the business advantage of the future.

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With that in mind, here are a few ways AI and ML-based systems are useful.

Fraud Prevention and Security

Fraud detection and security issue is always a top priority for the finance sector and AI has the potential to solve it. Frauds like account takeover, payment, and charge backs are very common and need to be dealt with accordingly. Using the data available and applying supervised learning algorithms, suspicious activities can be detected and eliminated and many digital banking platforms are using AI-driven KYC verification services.

By using ML algorithms, businesses can automate fraud detection and prevention processes and employ human intelligence for strategic tasks.

Mobile Banking

Mobile banking is revolutionizing the way how customers and banks interact. ML and AI algorithms are assisting in customer service, security, and fraud detection in mobile banking.

Some prominent examples of implications of AI and ML in mobile banking are chat bots like Bank of America’s Erica, a virtual assistant that uses reinforcement ML algorithms. Additionally, the facial recognition technology to secure the sign-in process is also AI-powered and is more secure than simple bio-metric security. To further sweeten the deal, banking institutes also collect data and apply machine learning to personalize the customer experience.

Conclusion

Overall artificial intelligence makes it easier for fraud detection and prevention. The AI-driven identity theft solutions process data faster and learn from each ongoing transactions. Experts predict that the AI-based identity verification industry will boom in 2020.

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