Today’s banking customers have numerous pathways to connect with their financial institutions on a 24/7 basis. Unfortunately, if fraudsters are able to exploit these pathways, they will inevitably pounce. Online fraud detection and prevention solutions work by rapidly reviewing data to detect if fraudulent activity is taking place and prevent it before a bank or customer experiences any losses. These solutions can make the difference between stopping fraud in the first place and trying to fix any damage that has been done after the fact. 

Why is online fraud detection and prevention important?

 Online fraud occurs when fraudsters use digital channels to infiltrate a customers’ online account to make fraudulent purchases, transfer funds, or steal sensitive data. Online fraud detection and prevention solutions are designed to stop different types of fraud before any harm occurs. It’s the difference between blowing out a match once it sparks and calling the fire department to contain a blaze.

In fact, one particular type of fraud highlights how important it is to take a preventative approach: Account takeovers (ATOs). ATOs can yield significant financial gains for fraudsters. Financial losses to consumers and FIs from ATOs rose by 72% last year to reach $6.8 billion, according to recent research

What makes ATO attacks so concerning is the returns they yield for fraudsters. Fraudsters can use techniques like scripted attacks that enable them to input numerous login and password credentials in very little time. If even one combination is successful, they will be able to access a legitimate customer’s bank account or credit card without exerting much effort. Once a fraudster gains access to the bank account, the fraudsters can authorize transfers using real-time person-to-person (P2P) services or wires to accounts that they control with little hope of funds getting recovered. In some cases, the victim might not even realize they were defrauded until they review their financial statements later.

How banks’ fraud strategy currently falls short

Criminals will continue their fraud schemes for as long as possible – or, more specifically, until they are found out. But by the time they’ve been discovered, it’s too late – for the banks and customers, that is. The fraudster takes their spoils, moves on, and is unlikely to get caught or face consequences for their actions. In other words, by circumventing a bank’s security protocols and internal controls, the fraudsters are able to make a quick profit with relatively little effort.

This essentially sums up the major shortcoming of many existing fraud strategies in the financial services industry. Namely, they are designed to detect fraud and react to it after it happens. Banks can review what went wrong and invest in a solution that patches up the vulnerability. The trouble is that this a purely reactive approach that leaves banks stuck in a frustrating game of whack-a-mole in which they constantly try to squash fraud only after it pops up.

Moreover, banks often have controls and models in place that ultimately don’t do a great job at detecting or preventing fraud. AI, machine learning, and deep learning solutions are needed to react to changing fraud attack trends as they happen in real-time. Data enrichment and augmentation can also be used to highlight a specific bank customer’s risk profile.

3 steps to building an effective online fraud prevention and detection strategy

With fraudsters eager to exploit any vulnerability they can find in a financial institution’s system banks have to shift their strategy. Flipping the traditional script from reactive to preventative is the hallmark of a strong online prevention and detection system. Here are three key steps to constructing a strategy that will work for your own organization.

 1. Online fraud prevention requires strong digital onboarding controls

Account onboarding is the moment when a bank’s relationship with a customer begins – and one of the bank’s most significant business risks. This is because there is no existing relationship or prior record of authentication for banks to determine if the customer creating the online bank account is who they claim to be. Without online fraud detection and prevention mechanisms in place, many FIs might be unable to determine if they are dealing with a legitimate applicant or a scripted attack that copies and pastes information into the onboarding fields. 

Implementing behavioral analytics and machine learning solutions can help banks perform their due diligence more effectively by shifting away from manual reviews and automating onboarding processes. Machine learning tools can pull in data from other sources and perform data analysis to build a more complete picture of the person attempting to open a bank account online, including data from vendor consortiums. This type of collective trust informs banks if the person attempting to use their onboarding system has been affiliated with any suspicious activities, such as whether the user’s data has been used and commonly device attributes have been linked to an account with a different name or any fraudulent transactions with other FIs.

2. Build digital trust 

Banks should also inspect the digital hygiene of the applicant by checking with telecom companies if their device or email history raises any red flags. This could involve pinging telecom providers for data regarding the provided phone numbers and email addresses to ensure they are indeed legitimate. For example, an email address that only popped up a few days before the applicant started applying for an account could indicate a risk of fraud being attempted. Banks will also want to review the applicant’s PII with relevant agencies to make sure the applicant’s information checks out. These steps will help banks enrich their data and build a more accurate profile of the applicants to assess whether they pose a serious fraud risk or not.

3. Establish strong reporting practices 

Banks should also consider conducting an internal audit into their existing reporting processes and address any shortcomings that could allow potential fraud to unchecked. Data silos within an organization could lead to static communication between teams and allow potential fraud threats to go unnoticed.  

A bank’s cybersecurity team, for example, might not think to relay certain information to the same organization’s fraud management team such as an unusual number of user login attempts in a short window of time. However, the fraud management team would see this type of activity as a red flag because the logins are occurring from an unfamiliar mobile device in a new geographic location. Delays in exchanging this information could prevent fraud managers from conducting a thorough fraud investigation while the bank’s customer suffers significant losses.

A strong fraud prevention program should encourage communication between team members and implement a triage system to escalate things when necessary. The smoother flow of information allows each department to perform a risk assessment of transactions. Organizations that have implemented machine learning and deep learning as part of their fraud detection methods are more likely to be able to stop potential issues from emerging in the first place by quickly identifying the root of the issue and quickly implementing fixes. 

Reducing fraud losses for banks and customers starts with re-thinking what has ultimately been a losing strategy of reacting to fraud after it occurs. Banks that experience high levels of fraud losses will not only suffer from financial losses but could also suffer a reputational risk and lose customers who no longer trust the compromised bank to manage their assets. It could also cause banks to fall under scrutiny from financial regulatory agencies like the Consumer Financial Protection Bureau (CFPB) and OCC for having inadequate fraud controls. Having a comprehensive fraud platform that leverages machine learning and deep learning can help banks implement effective online fraud detection and prevention methods that can make it easy to automate onboarding, assess whether customers are behaving normally, or whether there is a potential risk of fraud afoot. At the end of the day, the key goal of a fraud detection and prevention strategy is to ensure that only legitimate customers are using and trusting the digital banking channels available to them.

Today’s banking experiences are all about convenience. Watch our on-demand webinar The future of payments in a convenience-driven era to learn how banks and payment providers can address the needs of today’s consumers.