Fraud risk management in the financial sector isn't a nice-to-have — it's a must-have. Without proper risk management and fraud prevention, institutions increase the odds of losing millions or even billions because of fraudulent activities.
In the past, catching fraud before it happened was next to impossible. So companies didn't realize any fraud had occurred until it was too late. But thanks to advancements in technology, their able to defend against the sophisticated criminals of today.
If you're not already using technology for risk management and fraud prevention, then keep reading. We'll discuss how financial institutions can use AI tools to reduce risk and potential fraud schemes.
What is fraud risk management?
Fraud risk management is a process companies use to reduce the chances of fraud. Traditionally, this meant using legacy technology and manual reviews to determine a customer's trustworthiness.
For example, when opening a new account, financial institutions use documents to verify applicant identities and prove income sources. But this is no longer enough.
Banking, insurance, and mortgage lending are all at risk to attacks from malicious acts of fraud. Scammers love to target financial institutions, especially now that most use digital channels. Online banking drove the sector forward, but also presented a major problem:
Fabricated (synthetic) identities and maniuplated documents are now the new normal.
And they led to a $20B in losses for financial institutions in 2020. Unfortunately, they're not always easy to catch. Even large financial organizations are feeling the pain. For instance, PayPal recently found 4.5 million illegitimate accounts in its customer base.
No company is safe, making it critical for them to adopt alternative approaches to fraud risk management.
Artificial intelligence is leading the finance industry into the next frontier of managing fraud risk. But more on that later. First, let's review fraud prevention.
What is fraud prevention?
Fraud prevention is the practice of stopping fraudsters before they commit a crime. This includes detecting suspicious activity, monitoring transactions, and reviewing data. It's important to understand what makes up a successful fraud prevention strategy.
- Determining your exposure to fraud risk
- Implementing best practices in a fraud prevention program
- Adopting tools to identify and prevent fraud
The goal of fraud prevention is to stop fraud before it occurs. That means training your people, building processes, and putting technologies in place.
What's the difference between fraud prevention and fraud detection?
The two terms are used interchangeably, but there's a difference:
- Fraud detection identifies behavioral patterns indicating fraud. For example, being alerted when someone opens a credit card account using a stolen I.D. and Social Security number.
- Fraud prevention stops a fraud attempt by blocking them before they happen. For instance, preventing someone from filing a false insurance claim using forged documents by detecting document forensics.
The best way to prevent fraud is to quickly detect it. And today, AI and machine learning prove useful for achieving both goals.
AI-powered fraud detection solutions are now widely used in fraud prevention strategies throughout the financial sector. We'll cover the "why" below.
What are the five principles of fraud risk management?
A fraud risk management strategy has five elements:
- Risk assessment: Identifying where your organization stands regarding fraud risks.
- Risk mitigation: Determining the steps to reduce key fraud risks.
- Fraud prevention: Using a mix of fraud detection strategies, tools, and best practices to identify potential fraud risks.
- Fraud detection: Adopting tools to identify and notify employees of potential fraud in real-time.
- Monitoring and Reporting: Tracking the progress, making adjustments, and reporting findings and outcomes to see what works or needs corrective action.
Let's look at each one.
Fraud risk assessment
The first step in any risk management strategy is identifying where your organization stands against risks of fraud. This includes understanding the current state of your business and taking stock of your vulnerabilities.
Analyze your employees, processes, and technologies to find and remove vulnerabilities. It may be an internal issue with how your employees handle sensitive data, or the software used to collect data from customers is out of date or lacks features needed to make fraud detection easier (such as machine learning).
Also, review external fraud factors like third-party vendors or partners. Are there any outside entities posing a threat to your business?
Identify your vulnerabilities by asking yourself these questions:
- How do I know if my system is secure?
- Do I have the right processes in place to prevent fraud?
- Do I have the right people in place to detect fraud?
- Are we properly trained?
- Is our staff compliant with anti-fraud laws?
- Are we following best practices?
If you can't answer yes to all these questions, consider implementing some form of fraud prevention.
Fraud risk mitigation
Once you identified your vulnerabilities, you can determine the best actions to mitigate them.
Start by using data analytics to understand fraud's impact and come up with steps for how to respond. For example, you can implement mandatory fraud training programs if you discover your employees are unknowingly accepting fraudulent applications. .
You can also use analytics to decide which types of fraud to focus on. For instance, if you notice customers using stolen credit cards, focus more on this type of fraud.
Analytics also assist with monitoring compliance. If your employees aren't complying with your policies, it's time to take corrective measures.
Now, you know your vulnerabilities and what needs to be done—it's time to put a plan into place.
Start by creating a plan for each vulnerability you've discovered. Next, create a process for tracking the results of your efforts.
Then train your staff to spot fraud and report suspicious activity.
Once you implement a plan to prevent fraud, start detecting it.
Use analytics to track fraud trends over time to better understand how often fraud occurs. Then, use predictive analytics to anticipate when fraudsters will strike next. Predictive analytics identify patterns in past events that indicate future behavior, which can potentially stop future fraud.
In addition, machine learning can help automate the detection of fraud. This way, you don't need to manually analyze every transaction. Fraud management solutions like Inscribe use AI to automatically classify documents as either legitimate or fraudulent.
The software learns from previous documents and identifies patterns associated with fraud. So, instead of spending hours analyzing applications, you can instead focus on approving more great-fit customers.
Monitoring and reporting
After implementing your plan, track and report its effectiveness. Use analytics to measure whether your plan has been successful. And make adjustments in areas needing improvement.
Start by tracking the number of cases detected and resolved. Are there more instances of fraud than before? Or is fraud on the decline? Is there a particular type of fraud occurring more than others?
Use your findings to determine how to continue improving fraud detection and prevention.
The goal is to find out why certain events occur and learn from them, and the end result should be that you can improve your security and prevent financial losses.
When reporting on your findings, make sure to share the results with your teams. Let them know what’s working and where there is room for improvement. The reporting stage is the time to complete a review of employee performance.
Create an evaluation process that determines:
- How to tell someone their behavior isn't acceptable
- What an employee has to do to resolve a case
- How to let people know there's a problem, or something went wrong
- How to instill accountability
- The segregation of duties between departments and roles
- The role of senior management in your fraud risk plan
- How you'll hold people accountable for their actions
- What happens if they don't comply
It's important to remember that fraud prevention doesn't just involve technology. It involves people too.
So include your teams in the strategy designed for fraud prevention.
How can AI strengthen fraud management and prevention?
The financial sector has seen a sharp rise in fraud that calls for innovation to ensure effective fraud risk management and prevention.
The old way of manually processing applications and documents isn't reliable, leaving massive amounts of room for human error and increasing the risk of scammers getting into your systems illegally.
Artificial intelligence and machine learning eliminate this issue by automating fraud detection. This means that you no longer need to rely on humans to review each application.
Instead, you can focus on what matters most—customer approvals, experience, and satisfaction.
AI-powered solutions detect fraudulent activity before it occurs, and evolve to better identify discrepancies in the application process (like learning formats of new financial statements to better detect fakes).
Financial institutions like banks, mortgage brokers, insurance agencies, and property managers use Inscribe to detect signs of fraud before approving applications. This includes using image fraud detection to determine:
- Digital tampering
- File forensics
- Text overlays
- And more
With Inscribe, underwriters can process applications with confidence that they're not being bamboozled.
Next steps to managing fraud risk
With AI-powered tools like Inscribe, you can automate internal controls and free up resources to focus on what really matters--removing bottlenecks and providing exceptional customer service.
As we've seen, fraud prevention doesn't just mean stopping bad actors from stealing money. It also means protecting consumers from scams.
If you'd like to learn how AI reduces fraud risk and prevents millions in losses, get started with Inscribe today.