The Global Banking Fraud Survey of 2019 revealed that more than half of the surveyed institutions encountered a rise in fraud cases, both in frequency and overall financial impact on a global scale. Furthermore, the 2023 APAC Digital Banking Fraud Trends Report shed light on the alarming escalation of bank fraud, with scams constituting 54% of reported cases. Notably, there was a staggering 200% surge in voice scams from 2022 to 2023.
Amidst the broader backdrop of global challenges, ranging from the aftermath of the pandemic to geopolitical conflicts, fraudsters have adapted their tactics. Exploiting heightened stress and anxiety levels, they employ human-centric coercion and manipulate remote access tools, effectively outsmarting even the most sophisticated cybersecurity solutions.
In this article, let’s examine the nuanced landscape of bank fraud to equip financial institutions in fortifying their defenses. We will then dive into how artificial intelligence and machine learning can play a pivotal role in banking fraud detection and prevention.
Types of Bank Frauds
In the intricate banking landscape, the threat of fraud looms large, posing diverse challenges that financial institutions must navigate. From deceptive cyber maneuvers to crafty social engineering tactics, various fraudulent schemes threaten the financial sector. This section delves into the various types of bank frauds, unraveling the intricacies of each and shedding light on the implications they carry for both banks and their clientele.
Card Fraud
Credit card fraud is one of the most common types of banking fraud. Fraudsters gain unauthorized access to payment cards like credit, debit, gift or prepaid cards for financial gain. Sometimes, the fraud committed includes skimming and card cloning.
Global losses from card fraud totalled $34 billion in 2023 and are expected to reach $43 billion by 2026 (source: https://www.clearlypayments.com/blog/credit-card-fraud-in-2023/).
Check Fraud
Check fraud emerges when deceitful individuals employ paper or digital checks to acquire funds illicitly.
Perpetrators may engage in various fraudulent activities, such as writing deceitful checks on their own or closed accounts, forging signatures, or fabricating entirely fictitious checks.
According to the 2022 AFP® Payment Fraud and Control Report, the most impacted payment frauds are checks at 66% and wire transfers at 37% in 2021.
Friendly Fraud
Commonly referred to as chargeback fraud, friendly fraud transpires when a genuine payment executed through a credit card, debit card, or another payment method becomes subject to dispute.
Diverging from conventional fraud scenarios involving unidentified third-party perpetrators, friendly fraud constitutes a form of first-party misuse of credit/debit cards.
In this situation, the customer initiates the transaction, only to subsequently assert that the charge was fraudulent or unauthorized.
New Account Fraud
New account fraud, also known as account creation or fake account fraud, occurs when a fraudster opens an account with the intent of committing fraud, often using stolen or synthetic identities.
They may acquire identities through breaches or phishing, even using information from children, the deceased, or the homeless. Sometimes, the fraudster may use their identity for fraudulent activities, constituting first-party fraud.
Another method involves creating synthetic identities by combining real and stolen details. Once the new account is set up, fraudsters make charges or issue checks in the victim's name.
Account Takeover (ATO)
An ATO happens when fraudsters gain unauthorized access to accounts, often via stolen credentials. The techniques used typically are
- Phishing attacks: Deceptive attempts, fraudsters send a fake email or text message that directs customers to a fake bank login page, to acquire sensitive information and credentials.
- Credential stuffing: Also known as “brute force” attacks, fraudsters use sophisticated bots to automatically test random, stolen or dark-web-bought credentials until they gain access to an account.
- Social engineering: Deceitful manipulation, fraudsters or hired imposters apply physiological manipulation or scare tactics to make individuals divulge confidential information.
- Cybersecurity issues: Fraudsters may exploit the vulnerabilities of a bank’s security infrastructure with malware attacks, ransomware, and data breaches.
- Call centre fraud: A method used by fraud gangs to extract sensitive information from call centres or individuals using impersonation and social engineering. Primarily operating from Southeast Asian countries like Indonesia, Thailand, Malaysia, Cambodia and Myanmar, this type of fraud has been on the rise. With a higher profit margin than sex trafficking, criminal rings have shifted to scam call centers.
Money Laundering
This involves legitimizing unlawfully obtained funds through foreign banks or legitimate businesses.
These funds, known as "dirty money," originate from illegal activities like drug trafficking, human trafficking, corruption, embezzlement, or illegal gambling, often orchestrated by organized fraud rings or gangs.
The laundering process consists of three stages:
- Placement: Small amounts of money are introduced into the financial system in increments below the Anti-Money Laundering (AML) reporting threshold, a tactic known as "smurfing."
- Layering: The funds undergo various transactions, such as purchases or investments, often routed through holding companies or different entities to obscure their origin and create a level of separation.
- Integration/Extraction: The funds are integrated into the economy by investing in real estate, business ventures, purchasing goods or services, hiring fictitious employees, and other means.
P2P Payment Fraud
In recent years, the boom of cash apps like Paypal, Venmo, Google Pay, Apple Pay, Zelle, Alipay, and others in the peer-to-peer (P2P) payment landscape has created an ideal playground for fraudsters. They exploit the often limited data and insights available to these digital payment platforms for fraud prevention.
Fraudsters sell non-existent goods or use stolen credit cards to create new P2P accounts for unauthorized transactions.
Forbes reports a staggering $1.7 billion in estimated P2P fraud losses in 2022, a 90% increase from 2021.
Application Fraud
Criminals engage in application fraud by utilizing stolen or synthetic IDs to apply for loans or credit lines.
Instances include
- Gradual Credit Card Scheme: A criminal applies for a credit card, incrementally building credit over months or years before maxing out the card with no intention of repayment.
- Loan Stacking: Fraudsters submit multiple credit or loan applications simultaneously across various financial institutions using automated bots and virtual machines, disappearing with the money before detection.
- Synthetic Identities: Fraudsters blend real and fake information to create synthetic identities for third-party application fraud, while first-party fraud involves using true identity with false details like a fake residence or inflated income.
Loan Fraud
A subset of application fraud, loan fraud saw a concerning uptick, with nearly 1% of mortgage applications containing fraud in Q2 2022 (1 in 131 applications), as reported by CoreLogic.
This form encompasses mortgage fraud, loan scams, and payday fraud, all involving criminals using personal information to secure loans.
The surge in loan fraud is partly attributed to the popularity of online lenders, often skipping thorough background checks and relying on easily obtainable information like name, address, social security number, and income, making it susceptible to theft or fraudulent acquisition.
Strategies to Fight Banking Fraud
Unveiling the Power of Machine Learning and AI
In the relentless battle against banking fraud, the integration of cutting-edge technologies becomes paramount.
Among the forefront defenders are Machine Learning (ML) and Artificial Intelligence (AI) systems, wielding advanced mechanisms that reshape the fraud prevention landscape.
Decoding the Fortification
At the core of ML and AI-powered fraud prevention lies a sophisticated mechanism designed to fortify banking systems against ever-evolving threats.
The process begins with meticulous data ingestion and pre-processing, where diverse datasets, including user profiling, transactional data, user behavior metrics, and device information, undergo a transformational journey.
Normalization becomes a linchpin, ensuring that features with varying scales and variances harmonize, preventing dominance by any single feature.
- Handling missing values introduces resilience, using techniques like imputation, deletion, or predictive modeling to ensure robustness, which is especially crucial in the realm of financial data.
- Feature engineering elevates the process, converting raw data into meaningful indicators of fraudulent behavior.
This intricate step demands substantial domain knowledge, ensuring the creation of pertinent features tailored to the nuanced challenges of fraud detection.
In traditional banking, the scope expands beyond online channels to include offline transactions at ATMs and counters, requiring a seamless integration process for holistic vigilance.
Machine Learning Model Development
The arsenal of ML models employed includes decision trees for sequential decision-making, neural networks for identifying complex, non-linear patterns, and ensemble methods such as bagging and boosting for heightened accuracy and resilience against overfitting.
- Decision Trees: Sequentially decide fraud risk based on data attributes.
- Neural Networks: Identify intricate patterns in large datasets, particularly effective against sophisticated fraud schemes.
- Ensemble Method: Combine multiple algorithms for superior accuracy, utilizing bagging and boosting for robust predictions.
These models are strategically deployed, utilizing supervised learning to detect known fraud patterns and unsupervised learning to identify anomalies in data, crucial for unveiling new and evolving types of fraud.
By combining both types of machine learning, the fraud solution becomes a potent, efficient, and accurate tool for detecting and preventing a broad spectrum of fraud scenarios, be it current or future potential threats.
Advantages: Empowering Banks with Precision
Real-Time Monitoring: Navigating the Dynamic Landscape
Real-time monitoring emerges as a game-changer, offering a dynamic evaluation of transactions using trained ML models.
Parameters like transaction amount, location, user behavior, and device information contribute to a risk score, enabling instantaneous identification of potential fraud.
In addition to seamlessly handling substantial real-time data volumes, the ML models can dynamically adapt and assimilate new data, empowering the decision engine to continuously evolve and enhance its capabilities in detecting fraud over time.
This dynamic approach ensures adaptability to emerging fraud trends, a crucial aspect for mitigating financial losses.
Comprehensive Fraud & Risk Management: Operation Efficiency Unleashed
The holistic approach to fraud management integrates seamlessly with comprehensive risk management.
At every stage of the credit or lending process, indicators, rules, models, strategies, and workflows can be set from initial screening to underwriting, continuous monitoring and collection.
Overcoming the challenge of standalone solutions, the end-to-end picture ensures operational efficiency and cost-effectiveness.
Beyond standalone solutions, each bank operates under its distinctive credit policy. The advantage of customization empowers banks to adjust the risk assessment process, aligning it precisely with specific requirements, rules, policies, and risk tolerance levels as necessitated.
Integrating offline channels, including ATMs and counters, contributes to a robust defense against fraudulent activities.
Conclusion
A Proactive Stance in the Face of Rising Threats
As the specter of banking fraud looms large in today's dynamic environment, the adoption of ML and AI-powered strategies emerges as a proactive stance.
The real-time risk scoring, automated decision-making, and vigilant model monitoring and updating underscore a commitment to staying ahead of evolving threats while maintaining operational excellence.
Key Highlights
- Balance of Accuracy and Speed: Processing large transaction volumes within milliseconds without compromising accuracy.
- Model Monitoring and Updating: Continuous oversight, using AUC-ROC metrics and regular checks for data and model drift. Scheduled re-training and updates ensure adaptability to emerging threats.
- Complexity and Domain Expert: Building an end-to-end credit risk management system with fraud strategy is complex and requires a huge team of domain experts, with years of R&D, and training models to achieve accuracy and speed.
In the relentless pursuit of security, machine learning and artificial intelligence become the beacon guiding financial institutions through the intricate landscape of banking fraud prevention.
If you’d like to understand how this works in detail, contact us for a free demo today: https://trustdecision.com/#getDemo.
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