Combating bank fraud has remained a consistent challenge for banks globally. Amid the banking sector's digital transformation, fraudsters continually devise innovative methods to exploit vulnerabilities and target both banks and their clients.
December 5, 2023
15 minutes
Shuant Goh
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.
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.
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 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, 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.
An ATO happens when fraudsters gain unauthorized access to accounts, often via stolen credentials. The techniques used typically are
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:
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.
Criminals engage in application fraud by utilizing stolen or synthetic IDs to apply for loans or credit lines.
Instances include
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.
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.
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.
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.
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.
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.
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.
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.
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
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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