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Every person works smart in this grooming world. And in some cases there are several people who use technology in the wrong way. So, how to build a fraud detection solution for your organization? It would be challenging for you.
You have to take requirements from your internal stakeholders and vendors fact sheets, so they can give an overwhelming impression for your company that needs to have it all. Although In reality, your selected terms have to simply tick all the boxes on your must-have list and cover your business use cases. So, it should contain one of the hottest features out-of-the-box, to minimize the need for time- and resource-consuming customizations.
So, how will you manage a fraud monitoring tool to be able to meet people’s needs? Well, it’s all start an ideal solution that can be able to identify and respond to the real fraud scenarios, both industry-known and specific to your organization. Moreover, it’s vital for the tool to be able to react to unknown and perhaps surprising fraud occurrences. So, it has to provide a global mix of features to collect and analyze the data, draw correct conclusions, that can take actions based on results, and finally produce comprehensive reports. So, in this case It should be able to integrate in your existing ecosystem and, at some point, this tool should become something your fraud team cannot imagine living without.
It shows clearly, that’s a tall order for fraud detection software. But not sure for every fraud detection solution on the market lives up to this standard, because it is so important for organizations to do their research and find a tool that can provide comprehensive fraud monitoring.
So, in that case you have to help you to evaluate the key requirements, our Buyer’s Guide to Evaluating Fraud Detection Tools especially for the explanation of the top nine capabilities that a fraud monitoring tool must provide in order to meet the needs of modern financial institutions.
A Key Function of a Fraud Detection Tool
Detecting a wider range of fraud by combining machine learning with an advanced rule engine.
So, in the advanced case for a rule engine with a proper set of rules will filter out the fraudulent events meeting specific criteria. Basically, the rule engine will catch transactions whose time, place or amount values deviate from a normal scenario. But it has to offer the detection of more sophisticated cases, like phishing attacks or transactions to mule accounts. But you need to think about it as a system of filters that blocks transfers that should allow them down the pipeline or alerts the system to step-up authentication.
When we talk about a rule-based system that can no longer keep up with fraud attacks that evolve in complexity, speed and automation. But for reliability to rule libraries keep on expanding, which puts pressure on the system, slows operations and increases the false positives rate. So, according to such position, you have to provide ultimate capabilities to combat a wide range of fraud attempts but remember it should without affecting the processing speed, think of a combination of rules with machine learning algorithms.
So, choose the best machine learning fraud detection solution that deploys different algorithms to support from your vendor’s experts.