Fighting fraud requires data and insights, but it also requires collaboration. Unfortunately, bad actors often focus more on collaboration than banks. Criminals share their data and insights to find the most effective ways to commit fraud. Once they find a weakness in a bank’s defenses, they show other criminals how to replicate their actions, and as the banks improve their technological defenses, the criminals continue to collaborate and find new ways to commit fraud.
Bad actors are constantly evolving, and if they evolve faster than your bank, you will become a victim of fraud. To protect your financial institution in this environment, you have to learn as much as possible as quickly as possible about the fraud landscape, but that’s impossible to do on your own. The key to a successful defense? Collaboration.
Variable Data and Risk-Confidence Scores
The first defense against fraudulent transactions is usually several types of binary data verification. Imagine a business owner receiving a fraudulent invoice directing them to send payment to a new bank account instead of the vendor’s usual account. The business owner doesn’t realize the invoice is fraudulent so they initiate payment from their account.
With no controls, the funds will go straight to the new account, but at this point, most financial institutions employ binary verification tools that ask yes-no questions about the transaction. For instance, does the account exist? Does the name on the account match the name on the payment? Is the account on a list of blocked accounts? If all answers to these questions are yes, the transaction moves forward, but there’s still a risk of fraud.
To reduce the risk of fraud, the financial institution should assess the transaction using a second layer of defense based on variable data, before allowing the funds to move to the new account. Rather than assessing yes-no binary data, variable data tools look at a variety of inputs, and they generate a risk-confidence score.
At the simplest, these tools look for aberrations from usual behavior. For instance, if the sender usually only makes $100 payments, the system will flag a $100,000 transaction as potentially fraudulent. However, new accounts and/or new account holders generally don’t have an established pattern of behavior, and to effectively assess transactions in this situation, the tools need to learn about the anatomy of legitimate and fraudulent transactions.
On their own, most banks don’t have enough data to train the fraud prevention tools. Banks that exclusively rely on their own data will hinder their efforts. Collaboration is key here.
The Risks of Taking a Siloed Approach to Fraud Detection
Traditionally, banks use internal data and analytics to create rule sets designed to detect fraud. Most banks take a reactive approach to fraud, shaping their detection methods around incidents that have already been detected. They create watch lists based on incidents that have just affected their banks. Their data is siloed, and its limited scope restricts their ability to detect fraud.
Financial institutions limit their knowledge base by not collaborating with competing banks, but in most cases, they also constrain their efforts through organizational boundaries. This siloed approach can never do more than detect fraud, and it will almost always miss evolving threats.
In contrast, collaboration can allow financial institutions to take a proactive approach to fraud. Rather than adjusting rule sets after fraud has occurred at their institution, bankers can work with other financial institutions and third parties to learn about the latest types of fraud. Then, they can adjust their defense posture accordingly.
Banks don’t have to wait to become victims of fraud to learn about the newest risks. Instead, they can learn from incidents at other banks and then take a proactive approach.
Perhaps, even more importantly, banks need to collaborate with third parties who track real-time insights about fraud methods. Cyber threat intelligence should inform how financial institutions approach fraud, but banks cannot get this information on their own. Rather than fighting a losing battle independently, financial institutions need to collaborate with third parties so that they can detect fraud before it occurs, prevent money from flowing to bad actors, and protect their reputations.
Data Sharing Through Banking Consortiums
To disrupt the data silos that allow fraud to thrive, financial institutions and other businesses need to work with their competitors. Consortiums are the ideal solution, and although they may require membership fees, time, or other resources, the return on investment is more than worth it. Case in point, from 2005 to 2010, members of the Credit Industry Fraud Avoidance System (CIFAS) avoided approximately £268 (approximately $335 USD) of fraud for every £1 ($1.25 USD) spent on membership fees.
In the United States, the Financial Services Information Sharing and Analysis Center (FS-ISAC) is one of the oldest and largest consortiums. Established in 1999, this non-profit organization is the only global intelligence-sharing community focused exclusively on financial services. With over 5,000 members in over 70 countries, FS-ISAC helps members access the intelligence they need to reduce risks and take a proactive approach to threats. Here’s how powerful collaboration can be—in 2009 and 2010, this group worked together to reduce account takeover attacks and achieved a 36% drop in attacks that led to financial losses in just one year.
Around the world, there are dozens of consortiums designed to help businesses collaborate against fraud. While some are focused on a single industry, others take a wider approach and sometimes include law enforcement. For example, the CyFin Program from The National Cyber-Forensics and Training Alliance was formed in 2002, and the organization brings together law enforcement and private sector companies in the financial, technology, manufacturing, healthcare, and other sectors to share intelligence, learn about threats, and find the best defense strategies. By actively sharing information, this group’s members are able to disrupt fraud cycles and prevent fraud incidents.
Despite the growing prevalence of these groups, financial institutions are often wary of involvement. They may worry about sharing information with competitors. They may be concerned about customer privacy or industry rules. They may be skeptical about the return on investment. Or they may wonder about the group’s efficacy. The FS-ISAC, for instance, took nearly 20 years to refine its approach to data gathering and intelligence sharing.
Successful Collaboration
The bad actors are working together much more effectively than the banks. They used to stick to the dark web, but now, they aren’t even bothering to hide. They brag about their consequences on TikTok, WhatsApp, and other social media sites, and they show other criminals how to follow in their footsteps. They also put what they’ve stolen up for sale so that other criminals can continue the fraud. This strategy is particularly popular in check-washing schemes where one criminal steals checks from the mail and then sells them to another criminal to wash and cash.
Financial institutions need to learn from the criminals. To work together successfully, financial institutions must commit to reducing fraud throughout the banking industry. If they only focus on reducing fraud at their individual financial institutions, they will simply shift fraud to another institution. To protect the industry as a whole, bankers must create an effective data-sharing strategy that protects the privacy of member banks and their clients. They also must share data bilaterally, and ideally, a trusted third party should oversee everything to ensure equal participation and assess success metrics.
Bankers also need to figure out how to get insights before fraud happens, and this requires collaboration not just with other banks but with the right partners. To reduce your financial institutions’s risk of fraud, you need to work with a fraud partner that collects data and insights that allow you to take a proactive approach to fraud.
At SQN Banking Systems, we collect cyber threat intelligence and leverage this data to constantly improve our fraud prevention and detection tools. Our tools and services help banks reduce their risk of check fraud, forgeries, and other types of bank fraud across all deposit channels in real-time. We focus on data and intelligence about emerging threats so that our clients can focus on other aspects of running a successful bank.
Ready to protect your resources, your customers, your reputation, and your bottom line? Then, you need to collaborate with the right fraud partners. To get started, contact us today. We’ll start with a fraud process review and help you discover the best ways to shore up your defenses from the threats of today and tomorrow.