ACH fraud detection is a must-have for online business and merchants need to be clever to stay one step ahead of fraudsters. Automated clearing house (ACH) transfers, commonly known as ACH payments, are the silent workhorse of the American financial system, processing vast sums daily with little fanfare.
This unsung hero of direct deposit paychecks, automatic bill payments, and business-to-business transactions is an essential cog in the wheel of commerce. Yet, it harbors an Achilles’ heel that fraudsters relentlessly exploit: the lag between transaction initiation and settlement leaves a window wide open for deception and theft.
In business, the impact of ACH fraud goes beyond the initial loss of funds. It triggers a chain reaction – disrupting cash flow, eroding customer trust, and leading to a challenging process of damage control.
And while large corporations may absorb these shocks, for small and medium-sized enterprises, they can strike a fatal blow to the business's very heart.
But the question is not as simple as how to detect ACH fraud: one must be careful in implementing security measures because if the system is too strict, it will lower your payment acceptance rate by turning away honest customers.
While the problem may seem tricky, you can reap the benefits of accepting ACH payments while limiting your fraud exposure to unauthorized ACH transactions without a high amount of false positives or customer churn. All you need is a modern decisioning engine-based fraud detection system.
This article will walk you through modern approaches to ACH fraud protection, as well as the following:
How ACH payments work and function
Mechanisms of ACH payment scams
The shortcomings of traditional fraud detection
The plague of false positives and low acceptance rates
The better way to detect ACH fraud with AI risk decisioning
Conclusion: fight fraudulent ACH transactions without blocking good customers
Let’s get started.
How ACH payments work and function
Automated clearing house payments are electronic payments that move funds from one bank account to another through a centralized system—the ACH network. This network acts as the financial equivalent of an air traffic control center, guiding transactions to their intended destinations safely and efficiently.
When a business or individual initiates an ACH transfer, they send a batch of transactions to their bank. These transactions are accumulated and sent through the ACH network at predetermined intervals. Upon receipt, the ACH operator (such as the Federal Reserve or The Clearing House in the U.S.) sorts the transactions and makes them available to the receiving banks.
This process ensures the systematic transfer of funds, with the actual movement typically completed within one to two business days.
How businesses accept ACH payments
Businesses opt into the automated clearing house network through a financial institution or payment processor, which provides the framework to accept ACH payments directly from customers’ bank accounts.
To get started, businesses need to gather customer authorization, bank account details, and routing numbers. Once set up, they can process payments for services or disburse funds, like payroll, with ease.
Typical use cases for ACH transactions
The versatility of ACH payments makes them suitable for a variety of use cases, which include:
Direct deposit of employee salaries, benefits, and reimbursements.
Consumer bill payments for utilities, mortgages, loans, and insurance premiums.
Business-to-business (B2B) payments for invoices and supplier costs.
Government transactions, such as tax refunds and social security benefits.
Ecommerce purchases directly from bank accounts.
In fact, since the introduction of same-day ACH payments, NACHA reports that there were $608B worth of funds moved in Q3 2023 alone!
Mechanisms of ACH payment scams
Naturally, a popular payment method like ACH will attract a fair number of fraudsters and criminals. For example, the Association for Financial Professionals (AFP) reported the following in their 2023 Payments Fraud and Control Survey:
“Fraud via ACH debits decreased from 37% in 2021 to 30% in 2022. The percentage of fraud activity via ACH debits had been increasing gradually – from 33 percent in 2019 to 34 percent in 2020 and to 37% in 2021. Time will tell whether the recent decline is the beginning of a trend or not.”
These impacts of ACH fraud schemes are non-trivial, and companies need to remain vigilant, employing a combination of user education, security best practices, and advanced fraud detection technologies to safeguard against these pervasive threats.
ACH fraud is an umbrella term that encompasses various tactics used by criminals to illegally obtain money from an individual or business's bank account using the ACH network.
A fraudulent ACH transaction can be relatively easy or quite complex to pull off, depending on the target’s security systems and the sophistication of the fraudsters.
Here are eight factors that draw criminals to commit ACH fraud:
Timing gap:
ACH transactions are not instantaneous; they require a processing window that can range from one to a few business days. Fraudsters exploit this time lag to initiate unauthorized transactions and withdraw funds before the fraud is detected, which is called ACH kiting.
The volume of transactions:
The sheer volume of ACH transactions processed daily can make individual fraudulent transactions harder to spot. Businesses and financial institutions that do not have advanced monitoring systems may not detect anomalies until it's too late.
Lack of strong authentication:
Not all businesses implement strong authentication methods for ACH transactions. If a company relies solely on basic information such as an account and bank routing number, it’s easier for fraudsters to execute unauthorized transactions.
Phishing and social engineering:
Fraudsters often rely on phishing schemes to obtain the necessary banking information. Many users can be deceived by these tactics, which makes obtaining the information to commit ACH fraud easier than one might expect.
Data breaches:
The frequency and scale of data breaches mean that a lot of personal and banking information is available on the dark web. This information can be used to perform ACH fraud on a massive scale.
Inadequate employee training:
Companies may not always train their employees effectively on the latest fraud detection techniques. This lack of training can lead to opportunities for fraudsters to manipulate internal staff into facilitating fraudulent transactions via ACH email scams.
Banking regulations:
For consumer accounts, banking regulations often limit the liability of individuals for unauthorized ACH debits, provided they report the fraud on time. Fraudsters exploit this by targeting individuals who may not check their bank statements regularly, allowing unauthorized ACH debits to go unnoticed for longer periods. When the question of ACH fraud, who is liable comes up, it’s the financial institutions holding the bag.
Complexity of business transactions:
In B2B transactions, where the movement of funds can be more complex and the amounts larger, it's easier for fraudsters to disguise unauthorized transactions as legitimate business activities.
Naturally, financial institutions, fraud detection systems, and automated clearing house networks have implemented different ways to deal with ACH scams and unauthorized ACH payments.
What is ACH fraud detection?
ACH fraud detection refers to the systematic process of identifying potentially fraudulent transactions within the Automated Clearing House (ACH) network, which processes large volumes of credit and debit transactions in batches. It involves monitoring, analyzing, and verifying ACH transactions to prevent unauthorized or illegal electronic fund transfers.
The process of ACH fraud detection typically employs a combination of advanced technologies and stringent procedures, including real-time anomaly detection systems, behavioral analytics, machine learning algorithms, and multi-factor authentication.
When it comes to companies asking themselves how to prevent ACH fraud, most fraud detection systems rely on three pillars:
Validate customer information:
Use third-party services to validate the authenticity of customer information before setting up ACH credits. This may include checking the customer's credit score, verifying identity, and confirming that the banking details are legitimate.
Establish know-your-customer (KYC) procedures:
Implementing strong KYC compliance processes helps ensure that incoming payments are from legitimate sources and that the business is not inadvertently involved in money laundering.
Monitor for suspicious activity:
Implement real-time transaction monitoring for patterns indicative of fraudulent activity, such as irregular high-value payments, sudden changes in transaction volume, or frequent changes in account details.
These technologies work together to flag unusual activities that deviate from established patterns of behavior, such as unexpected large transfers or abnormal transaction frequencies, prompting further investigation to confirm their legitimacy.
While these steps are sufficient in curbing ACH fraud, they come with a caveat: they produce too many false positives.
The shortcomings of traditional fraud detection
In the face of this threat, traditional fraud detection systems, much like old fortresses, show their age and fragility. They were designed for a different era, with checks and balances crafted for slower-moving financial threats, not the agile and ever-evolving tactics of today's cyber pirates.
The crux of the issue with these bygone systems lies in their reliance on static rules. Consider them as sentinels who only recognize the faces of known troublemakers, allowing cleverly disguised fraudsters to slip by unnoticed.
This old-school rule-based approach struggles to adapt to the innovative and sophisticated schemes that modern fraudsters employ, often leaving legitimate transactions caught in the crossfire.
With the advent of generative AI in fraud detection all of this is changing, and one key area of impact is that of false positives and low acceptance rates.
The plague of false positives and low acceptance rates
The heavy hand of these outdated systems is felt in the abundance of false positives they generate. In their zeal to block fraud, they often cast too wide a net, snaring legitimate transactions in the process.
For a business, this overzealousness can choke the flow of commerce, turning away genuine customers whose payments do not fit the rigid profile set by the system.
Imagine a customer making an unusually large purchase or a business initiating a higher-than-average payout. These are red flags to a conventional system, and more often than not, they'll stop these transactions dead in their tracks – a classic case of throwing the baby out with the bathwater.
The result?
A frustrated customer or vendor, a lost sale, a tarnished reputation, and ultimately, a blow to the business's bottom line.
Meanwhile, fraudsters continue to innovate, slipping through the cracks with tactics that haven't yet been codified into the static rule sets. And with every legitimate transaction that's wrongly declined, a potential revenue opportunity is lost, and customer dissatisfaction grows.
In the end, the traditional systems' inadequacy is twofold: they fail to stop fraud effectively, and they block good business, leading to low payment acceptance rates that no enterprise can afford in today's competitive landscape.
This scenario underscores the pressing need for a new champion in fraud detection – a system nimble enough to adapt in real-time, smart enough to learn from patterns, and discerning enough to tell friend from foe. That's where the promise of modern decisioning engines comes into the narrative, poised to redefine the battleground of ACH fraud prevention.
The difference between the old and the new is tangible. One of our clients, Fluz, using Oscilar’s AI risk decisioning platform saw a dramatic, 20% increase in their ACH approval rates. Moving the needle on this KPI had a massive impact on user satisfaction as well, leading to a streamlined transaction process that helped them rebuild trust among their user base.
To see how Oscilar is working to change these traditional ACH fraud detection methods and increasing ACH approval rates, check out the full Fluz case study.
The best way to detect ACH fraud with AI risk decisioning
Oscilar’s AI risk decision engine enhances the approval rates of ACH transactions by using sophisticated analytics, machine learning, and a set of predefined rules to quickly and accurately assess the risk associated with each transaction. Here's how we help improve your ACH approval rates:
Accurate risk assessment
By analyzing large volumes of transaction data and customer behavior patterns, the AI risk decision engine can more accurately differentiate between legitimate transactions and potential fraud. This reduces false declines, which directly improves approval rates.
Real-time processing
Decision engines process transactions in real time, allowing for instant approvals or stopping suspect ACH payments. This speed means that there's no need for manual review in many cases, which can bottleneck the approval process.
Customization of decision policies
Businesses can tailor the decision-making criteria based on their specific risk tolerance, customer profiles, and industry standards. This means that risk parameters can be adjusted to improve approval rates without proportionately increasing risk.
Dynamic learning capabilities
Machine learning models within decision engines adapt over time, learning from past decisions to improve future performance. This includes recognizing safe transaction patterns, which can lead to higher approval rates.
Integrated data sources
By pulling in data from a variety of sources, such as historical transaction data, customer banking information, and even third-party data services, the decision engine has a more holistic view of each transaction, leading to more informed and positive decisions. Oscilar already has over 60 service providers integrated into our platform, allowing you to make the correct decisions using the right data.
Reduction of manual reviews
The efficiency of a decision engine reduces the dependency on manual transaction reviews. This not only speeds up the approval process but also reallocates resources to only the most complex and high-risk cases.
Continuous monitoring and updating
A decision engine continuously monitors transactions and is regularly updated with the latest fraud trends, ensuring that legitimate transactions are less likely to be caught in outdated fraud filters.
Predictive analytics
With predictive analytics, the decision engine can foresee potential issues based on current and past trends, helping to proactively adjust decisioning rules to maintain high approval rates.
Scalability
As transaction volumes grow, the decision engine can scale accordingly, ensuring that the approval rate does not suffer due to increased volume.
Multi-factor authentication integration
For transactions that may seem risky, a decision engine can integrate additional verification steps, such as multi-factor authentication, to confirm legitimacy without outright denying the transaction.
Conclusion: fight fraudulent ACH transactions without blocking good customers
The landscape of financial transactions is perpetually at odds with the ingenuity of fraudsters, especially within the realm of ACH payments. It's a delicate balance to maintain—bolstering defenses against fraud while ensuring that customer experience remains seamless and approval rates stay high.
Conventional methods often fall short, ensnared by the ever-evolving tactics of fraudsters or hampered by outdated technology that fails to discern legitimate from fraudulent activity efficiently. The cost of getting it wrong is high: too strict, and you're declining legitimate transactions and alienating customers; too lenient, and you're vulnerable to financial crime.
Enter Oscilar's AI risk decisioning engine, a transformative solution designed to harmonize security with accessibility. By leveraging advanced machine learning algorithms and a vast network of data points, Oscilar's engine conducts a multidimensional analysis of each ACH transaction in real-time.
It sifts through the noise to detect patterns and anomalies that signify fraud, all the while adapting to new threats as they emerge. This intelligent system means businesses can confidently accept ACH payments, enjoying high approval rates and customer satisfaction.
With Oscilar, the days of trading off between fraud prevention and transaction approval rates are a relic of the past. Oscilar ensures that businesses no longer need to sacrifice customer experience at the altar of fraud prevention—instead, they can advance securely, with both assurance and ease.
Next Steps: How to get started with generative AI-powered risk decisioning for your business
Ready to revolutionize your decision-making with cutting-edge AI? Take the first step towards enhanced fraud detection, reduced false positives, and improved operational efficiency with Oscilar's Generative AI for Risk Decisioning.
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