Abstract: Cyber security is the most important part of global connectivity and usage of cloud services as it encompasses everything that applies to protecting your sensitive data and personal information. Recently, there has been so much hype everywhere regarding the use of ML and AI in fraud detection & prevention that it has been difficult for a number of individuals to distinguish myth from reality in cyber security. The organized crime schemes are so refined and quick to adapt, which makes it necessary for the fraud detection & prevention tool to provide sub-par results. In order to meet the needs of modern financial institutions some of the key functions and essential things are needed to be looked and studied for fraud detection.
In today’s digital world, cyber security is the most important part of global connectivity and usage of cloud services as it encompasses everything that applies to protecting your sensitive data, personal information, bank details, etc. Today, it has become a challenge to find the best fraud detection and prevention solution for your business or organization. Requirements from vendor fact sheets and internal stakeholders can give an overwhelming ding that your solution needs to have it all and then some. However, in reality, all your business use cases and your choice for the right solution should simply tick all the boxes of your must-have list. Your list must contain most of the necessary as well as out-of-the-box features for cyber security to minimize the need for resource customizations and time invested.
Artificial Intelligence (AI) and Machine Learning (ML) in Fraud Detection & Prevention
Payment fraud is a perfect use case for artificial intelligence (AI) and machine learning (ML), and has a long track record of successful use. When consumers get an email, text, call, or in-app messages from the issuer of their card for informing a fraud on their card or asking them to validate a transaction, they may not be aware of the fact that there are a brilliant set of algorithms behind this bit of excellent customer service.
Recently, there has been so much hype everywhere regarding the use of ML and AI in fraud detection & prevention that it has been difficult for a number of individuals to distinguish myth from reality in cyber security. There are times where people come to the conclusion that ML and AI have just been invented, or just been used for the first time in payments fraud. However, ML refers to analytic techniques that learns pattern in data sets without any guidance of a human analyst.While, AI refers to the extensive application of specific kinds of analytics in order to accomplish tasks such as from driving a car to detecting a fraudulent transaction.
Machine learning efficiently helps data scientists to determine the transactions that are most likely to be fraudulent by significantly minimizing false positives. These ML techniques allows automated discovery of patterns across huge volumes of streaming transactions and are thus, extremely effective in fraud detection and prevention. If done properly, ML can clearly distinguish fraudulent and legitimate behaviors while adapting over time to novel, previously unnoticed fraud tactics. This entails thousands of computations to be correctly performed in milliseconds.
Without a right understanding of fraud-specific techniques of data science as well as the domain, you can easily employ the algorithms of ML that learn the wrong thing, which ultimately results in an inflated mistake that is difficult to undo.
How Fraud Monitoring Tool Can Meet Your Needs?
To start initially, an ideal fraud detection and prevention solution should be able to detect and respond to a wide-ranging array of fraud scenarios that includes both specific and industry-known. However, the tool should have the ability to react to surprising as well as unknown fraud occurrences. The tool must be essential in providing versatile mix of features to accumulate and analyze the data, sketch accurate conclusions, take the needed actions, and finally produce all-inclusive reports. The fraud detection and prevention solution or tool should be capable enough to incorporate in your existing ecosystem and this tool should also become something that your fraud team cannot live without at some point.
Although, not every fraud detection& prevention solution on the market conform to this standard, and thus, it is crucial that businesses or organizations do their research and find the right tool with right comprehensive fraud monitoring. According to the Research Dive published report, the global fraud detection & prevention market is estimated to grow at a CAGR of 26.5% during the forecast period.
Key Functions of a Fraud Detection & Prevention Tool
The organized crime schemes are so refined and quick to adapt, which makes it necessary for the fraud detection & prevention tool to provide sub-par results. Each use case should be supported by expertly crafted solution, which is optimal for the problem at hand. In order to meet the needs of modern financial institutions some of the key functions of fraud detection & prevention tool are listed below:
- Combine Machine Learning to Detect a Wider Range of Fraud
The fraudulent events meeting the specific criteria can be filtered out with an advanced rule engine along with a proper set of rules.For instance, the rule engine will identify transactions where the amount, place, or time deviates from the normal scenario. Machine learning techniques can also help to detect more sophisticated cases such as transactions to mule accounts or phishing attacks. Think about it as a structure of filters that alerts the systems to enhance authentication by blocking transfers and allowing them down the pipeline.
However, your solution should not depend solely on rules. When a fraud attack is evolved in complexity, automation, and speed, a rule-based system is not able to keep-up with it for a longer time. Rule libraries keep on growing, which puts pressure on the system by slowing operations and upturning the false positives rate. In order to battle a wide array of fraud attempts without disturbing the processing speed, think of a combination of ML with algorithms for your fraud detection and prevention solution.
Machine learning has the ability to analyze a huge amount and variety of data and thus, lives up to the hype. This makes ML an indispensable element for your fraud detection mixture. Machine learning has the capability to extract value from data with only with a little human input. Thus, choose a ML solution that equips different algorithms by picking the best algorithm for your situation with the help of your vendor’s experts.Look for a ML implementation that will offer insights into the analysis process as well as provide evidence related to why a transaction was accepted or declined.
- Prevent Fraud Out-of-the-Box
Your fraud detection & prevention tool must be able to detect fraud right from the very beginning. Choose a cyber-security tool that supports your requirements for business continuity and also ensures a smooth alteration from the standing fraud processes. It is important to find the right fraud detection & prevention solution, which provides a sufficient level of out-of-the-box protection as you can’t afford any freeze in your risk analytics and anti-fraud efforts. Look for a turnkey package to analyze transactions through a combination of machine learning and a rule engine for your business or organization. There is no doubt that out-of-the-box is a good start but the cyber security solution should be flexible enough to adapt it to your data and own needs.
- Apply a Dynamic Approach
The fraud monitoring basis should be able to integrate with future as well as existing multi-factor authentication options. The solution should be able to constantly evaluate the risk of a particular event. On the basis of this evaluation, organize the authentication flow. According to its risk level, dynamically, it should trigger the most suitable authentication technique for a given situation. For instance, if a certain transaction is estimated as suspicious due to location of the user,infrequent timing, or considerably larger amount than before, your fraud detection & prevention solution should be able to intensify the authentication criteria rather than simply putting on hold or rejecting the transaction for manual review.
- Explore the Full Potential of Data By Preparing or the Challenges Precise to Mobile Channel
A number of additional challenges are brought by the mobile channel brings that distinguish it from the usual internet banking experience. Your fraud detection & prevention solution should identify these distinctions.
The fraud detection & prevention tool may not collect all the data points without detecting the specifics of the mobile channel, and therefore, can draw incorrect conclusions. This is because mobile phones in general offer much richer context and allows more advanced analysis, supporting the broader context of the mobile channel is crucial for fighting mobile fraud.
Your fraud detection & prevention must provide analysis on the basis of a wide array of data collected from the devices of your users. For instance, this data can include device health, if the device has been jail-broken or any suspicious activity is detected. Insight can also be provided for biometrics and authentication, for instance PIN strength or face recognition score. Another example from a wide array of mobile specific intelligence is the general device information, which includes the version of the model, device, operating system, etc.
However, these data points are only valued if they are valid. Thus, you should make sure that both the transfer between server & mobile device and data collection are safe. A safe& protected communication channel sovereign from other existing communication protocols will certify that the device security status can be reliable upon arriving to your fraud detection & monitoring system.
Author Bio:Abhinav Chandrayan has worked in the Writing industry for 2 years, gaining experience in Media & Advertising and Market Research Industry. As a seasoned writer, he is passionate about advancing his writing skills by reading and working on versatile domains. In addition to writing, he is also involved in filmmaking, where his film has won the Gold Film of the Year Award in the year 2016 at IFP. Outside of the office, Abhinav enjoys traveling, sports, and exploring different movie niches. Link with me on LinkedIn HERE