How Fintech Startups can use AI to detect and prevent online fraud?

Every dollar lost to fraud in the finance sector has a ripple effect, impacting not just the immediate financials of companies but also eroding customer trust and confidence.

According to a report by the Association of Certified Fraud Examiners (ACFE), organizations lose an estimated 5% of their revenue to fraud annually.

For fintech startups, this translates into significant financial losses, hampered growth, and damaged reputations. The cost of fraud extends beyond direct financial losses to include legal fees, investigation costs, and increased insurance premiums.

According to the 2023 LexisNexis True Cost of Fraud Study, for every dollar lost to fraud, financial institutions in the Asia Pacific (APAC) region incur an average cost of SGD 3.95, while in North America, the cost rises to USD 4.41. These costs include direct financial losses, internal labour, legal expenses, and recovery fees, significantly impacting the bottom line of financial firms

Cyber fraud encompasses a wide range of malicious activities, including:

• Phishing Attacks: Fraudsters trick individuals into revealing sensitive information through deceptive emails or websites.
• Account Takeover: Unauthorized access to a victim's account to steal funds or data.
• Identity Theft: Fraudsters use stolen personal information to open accounts or make unauthorized transactions.
• Payment Fraud: Unauthorized or fraudulent transactions, often involving credit card or digital payment methods.
• Insider Fraud: Employees or insiders exploit their access to commit fraud.

To counter these threats, artificial intelligence and machine learning have become essential, enabling real-time fraud detection and enhancing security.

Tech giants like Amazon, Apple, and Google have pioneered AI in business strategies, influencing the banking and fintech industries to adopt similar approaches for fraud detection.

Key findings from the survey done by KPMG

• More than half of the participants in the global survey reported an increase in the overall value and volume of external fraud. From 2015 to 2018, there was a global increase in fraud typologies, including account takeover and identity theft, cyberattacks, card-not-present fraud, and authorised push payment schemes.
• The majority of respondents worldwide stated that internal staff fraud detected either reduced or kept the same in terms of overall cost, average cost, and volume. However, this might not accurately depict the price of internal fraud. Many external frauds start with an employee of the bank.
• Less than 25% of fraud losses are recovered by more than half of respondents, indicating the importance of fraud prevention. Banks are spending money on new technology to ward against fraud.
• Cyberattacks were deemed by institutions assessed as the biggest threat to fraud risk across all regions.
• Scams are becoming more common, according to banks throughout the world. By persuading and forcing customers to make payments to them, fraudsters are evading bank safeguards.
• To minimise scam losses, customers play a critical role in preventing and identifying fraudulent activity on their accounts. There should be more done to inform clients about fraud and swindles.
• Banks worldwide are preparing to open their doors to third parties so they may access their client data, which is why open banking is seen as a serious threat to fraud risk.

Common Financial Frauds and How AI Stops Them

AI and ML technologies offer robust solutions to detect and prevent common financial frauds:

Phishing Attacks

In phishing attacks, fraudsters trick individuals into revealing sensitive information, such as login credentials or credit card numbers, by masquerading as trustworthy entities. These attacks often come in the form of deceptive emails, text messages, or websites designed to look like legitimate financial institutions.

How AI Stops Them: AI models analyze the content and metadata of emails and web traffic to identify patterns indicative of phishing attempts. Machine learning algorithms can detect anomalies in email headers, URLs, and sender information. Additionally, natural language processing (NLP) techniques help identify suspicious language and phrases commonly used in phishing attacks. By continuously learning from new data, AI systems can block or quarantine phishing attempts before they reach the intended recipients.

Account Takeover

Account takeover fraud occurs when fraudsters gain unauthorized access to a victim's account, often through stolen credentials obtained via phishing or data breaches. Once inside, they can change account settings, transfer funds, and steal sensitive information.

How AI Stops Them: Machine learning algorithms monitor user account activity for anomalies, such as unusual login locations, IP addresses, or device usage. Behavioural biometrics analyze user behaviours like typing speed, mouse movements, and login times to establish a baseline of normal activity. Any deviation from this baseline triggers alerts for potential account takeovers, prompting additional verification steps or temporary account suspension.

Identity Theft

In identity theft, fraudsters use stolen personal information to open new accounts, apply for loans, or make unauthorized transactions in the victim's name. This type of fraud often involves data breaches where large volumes of personal data are compromised.

How AI Stops Them: AI systems employ advanced identity verification techniques, such as facial recognition, fingerprint scanning, and voice recognition, to confirm the identity of users. Machine learning models analyze application data for inconsistencies and suspicious patterns, such as mismatched addresses or phone numbers. By cross-referencing data from multiple sources, AI can detect and prevent identity theft before it causes significant harm.

Payment Fraud

Payment fraud involves unauthorized transactions made using stolen credit card or bank account information. Fraudsters may use techniques like card skimming, hacking, or intercepting payment details to carry out fraudulent transactions.

How AI Stops Them: AI-driven systems analyze transaction data in real time to identify unusual spending patterns, such as high-value purchases or rapid transactions in different locations. Predictive analytics models use historical data to recognize patterns indicative of payment fraud. AI can also integrate with payment gateways to provide real-time fraud detection and prevention, flagging suspicious transactions for manual review or automatically declining them.

Insider Fraud

Insider fraud occurs when employees or other insiders exploit their access to commit fraud, such as embezzling funds, manipulating records, or leaking sensitive information. Insiders often have a deep understanding of the organization's systems and processes, making their actions difficult to detect.

How AI Stops Them: AI monitors employee activities and identifies behaviours that deviate from established norms. Machine learning models analyze access logs, transaction records, and communication patterns to detect unusual actions, such as accessing restricted areas of the system or performing unauthorized transactions. By continuously learning and adapting, AI systems can identify potential insider threats and trigger alerts for further investigation.

AI-Driven Fraud Detection Technologies in Banking

AI-driven fraud detection technologies in banking are transforming how financial institutions combat fraud. These technologies leverage advanced algorithms and real-time data analysis to detect and prevent fraudulent activities. Key technologies include:

• Predictive Analytics

Predictive analytics uses historical data and machine learning algorithms to forecast potential fraudulent activities. By identifying patterns and trends in past data, predictive models can flag transactions or behaviours that are likely to be fraudulent. Financial institutions use these insights to take preemptive actions, such as tightening security measures or requiring additional verification for high-risk transactions.

• Behavioral Biometrics

Behavioural biometrics analyze unique user behaviours, such as typing patterns, mouse movements, and touchscreen interactions, to authenticate users and detect anomalies. Unlike traditional biometrics, which relies on physical traits, behavioural biometrics focuses on how users interact with their devices. This technology provides an additional layer of security by continuously monitoring user behaviour and flagging deviations from established patterns.

• Natural Language Processing (NLP)

NLP techniques enable AI systems to understand and analyze human language. In the context of fraud detection, NLP is used to analyze communication channels, such as emails, text messages, and chat logs, for signs of fraudulent intent. AI can detect phishing attempts, social engineering schemes, and other fraudulent communications by identifying suspicious language and patterns.

• Anomaly Detection

Anomaly detection involves identifying deviations from normal transaction patterns in real time. AI systems use unsupervised learning algorithms to establish a baseline of normal behaviour for individual users and transactions. Any activity that deviates significantly from this baseline is flagged as potentially fraudulent. This approach is particularly effective for detecting new and emerging fraud tactics that may not yet be captured in historical data.

Real-World Case Studies

• JPMorgan Chase

The leader in global finance uses AI to swiftly examine court records and spot possible fraud. The artificial intelligence technology, known as DocLLM, can identify red flags and irregularities in a matter of seconds, assisting the bank in more successfully preventing fraudulent activity.

• Mastercard

The multinational payment card service firm has an AI-based platform called Decision Intelligence. It looks at how cardholders spend money and determines the likelihood of fraud for each transaction as it happens. This allows Mastercard to stop suspicious transactions before they go through. The new generative AI technology has already helped Mastercard “score and safely approve 143 billion transactions a year”.

AI and ML technologies are indispensable tools for fintech startups in the fight against online fraud.

By adopting these advanced solutions, startups can protect their operations, enhance customer trust, and ensure secure financial transactions. As fraudsters continue to evolve their tactics, staying ahead with cutting-edge technology is crucial for maintaining the integrity of the financial sector.

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