There is excellent documentation available.
Accessible custom rules with a monthly update on performance.
For us, it enables the bank or the issuer to offer their customers protection on their online transactions.
In terms of the fraud that we see on the card site, card not present fraud, the person gives contribution to what the total fraud has increased substantially.
The features were pretty straightforward. I just used them as I went along with very little interference all the years that I have used it.
It's a very good product for compliance and transaction monitoring for anti-money laundering.
It's a very good case management system.
What is Fraud Detection and Prevention?
What is Fraud Detection and Prevention?
It wasn’t that long ago that fraud detection and prevention involved reviewing a fair bit of historical data analysis. Data scientists would be poring over tons of credit card records in order to spot fraudulent (or with luck, potentially fraudulent) activity.
Fast forward to today and we see fraud detection systems depend on catching and stopping fraud the second it’s spotted or even before it actually occurs. Automated solutions for fraud management enables businesses to identify unusual patterns consistent with deceitful activity and then stop the transaction before completion.
The Challenge for Fraud Detection.
The biggest challenge today for fraud detection and prevention involves working with the sheer amount of data that must be processed by institutions during real-time monitoring of fraudulent activity.
With enormous amounts of data in motion and only milliseconds to identify and stop illegal activity, the fraud detection software required to perform this activity must be a powerful and robust solution to manage all tasks with efficiency. Even with most SLAs (Service Level Agreements) typically giving 99.7% service guarantees, the leftover 0.3% of missed transactions still translates to millions of dollars that large financial institutions lose annually.
How Does It Work?
Fraud detection applications will generate analytics of customer transactions that are simultaneously reviewed to identify patterns of behavior to detect fraudulent activity. Patterns of activity would also include size, location, and transaction frequency.
Additionally, more sophisticated customer metrics based on history profiles will be used in the analytics. This list of suspected behaviors and metrics are held in-memory and not in a database query; to allow for instant examination of each credit card transaction in an effort to provide immediate processing to massive amounts of data from various data sources.
Most fraud detection and prevention software will generally provide:
· Batch data integration in real-time
· Predefined alert scenarios
· Audited case management and workflow
· Testing tools for high-performance scenarios
· Rapid roll-out
· Operational performance and investigations reports (pre-defined)
Most often, business solutions will require fraud detection, compliance and security as goals to achieve. As artificial intelligence (AI) and machine learning continue to make progress, these next generation technologies will continue to automate manual processes with increasing speed and accuracy when combining large data sets with behavioral analytics.
Supervised learning: Algorithms learning from existing historical data to look for patterns of impropriety that should be flagged
Unsupervised learning: Machine learning algorithms that assess and examine data that while it does not flag as fraud, is used to uncover new anomalies and patterns of impropriety.
Network Analysis: Analyze and identify paths or connections and hubs, revealing patterns alongside social networks of interest that are deemed critical in an investigator’s toolkit.
Text Analytics: Used to accurately identify displays of times, companies, names, monetary values, etc., through content categorization, search and via entity extraction.
Take Practical Steps: What Can You Do to Reduce the Risk of Fraud?
Many fraud and detection applications will have specialized tools to assist you with ways to reduce risk both efficiently and cost-effectively.
· Preform and audit of your existing ERP’s security in order to determine its effectiveness while also working to identify vulnerabilities.
· Create an SoD policy (Segregation of Duties) that is tailored to your company’s potential areas of risk.
· Ensure that SoD reporting is automated and regularly checked for users with violations.
· In order to maintain clean access, identify potential SoD conflicts prior to the assignment of new access rights.
· Make sure that you review access regularly and that all users’ access matches their individual current responsibilities and that the removal of redundant rights is completed.
· Stay on top of critical data changes, like bank accounts which could be indicative of fraudulent activity.
Fraud Detection and Prevention Use Cases.
This time of year is at the very least mildly stressful for most people. They worry about making math error mistakes for example, that could result in an audit down the road. Yet others will engage in activities that are illegal in order to receive funds that are not justifiably their own.
In 2018, the IRS distributed nearly $464 billion to US citizens who were owed a tax refund. With such a large amount going out, the US government uses data analytics to assess each return individually. For example, the system used by the IRS can take a typical tax return from an individual and look back over the past three decades. The system will look for characteristics in all the returns and make the determination if they all align to the most current one submitted by the taxpayer.
Fraud can occur in the medical sector, for example when providers prescribe drugs or treatment for medical issues to individuals who do not have a genuine reason for receiving them. Fraud can also occur with drug companies who charge inflated prices for medicine, the list goes on.
Fraud data analytics will examine approval timelines for similar generic drugs as an example. Analytics will be used to contrast the generic drugs with one waiting for approval. If the process to award approval seems unusually long, investigators will look at why there seems to be slowdowns in the approval process.
More commonly, analytics will look for cases regarding pharmacy refill fraudulent activity. Most often is the case that a pharmacist will refill a subscription before a patient will need it. Algorithms applied to the analytics would include regions, states or even individual pharmacies for example to assess if fraudulent activity is occurring.
As the digital economy has risen, so has the rapid spread of fraud and cybersecurity risks. Adopting technologies using analytics and artificial intelligence to detect and prevent fraud is the most logical solution avaliable.
Today's fraudsters will work smarter and faster. This is not a warning; it is a promise. To identify and prevent fraudulent activity you will need a trusted software solution partner to protect the interests of your business. Depending on your specific business needs, one of the fraud detection and prevention software solutions listed will help.