The COVID-19 pandemic has changed every aspect of our lives, right from the way we shop, socialise, or even do business. While fraud and cybercrime have been around for years now, there's no denying that the pandemic accelerated the growth in fraudulent occurrences worldwide and will continue to do so as we continue to push forward in a. post-covid world. With more businesses taking the digital route, online frauds and scams have risen manifold. Hence fraud detection has become so much more important than ever.
That said, many businesses are still resorting to reviewing fraudulent transactions using legacy solutions. Rule-based linear processes are not only time-consuming but can also lead to high false positives in a bid to prevent fraud. This traditional approach towards identifying and authenticating customers makes the review process tedious and can lead to customer drop-offs.
Institutions and businesses today do not have the luxury of time and resources and must authenticate transactions in a matter of seconds. Therefore, time-sensitive decisions need real-time actionable insights in order to avoid false positives and manual reviews while halting bad transactions.
Why do legacy fraud detection methods Fail?
1. False Positives: Catalysts of the Growth vs. Fraud Conundrum
Adding several friction points in order to prevent fraud may result in false positives, and you’re likely to block a lot of genuine customers. For instance, too many high-value orders from a high-risk location have the chance of being fraudulent. But if you block all transactions from a risky region, you’ll lose out on genuine customers too.
2. Manual Reviews: Increases Workload of Risk Teams
Fraudsters are constantly working on smarter, faster, and more stealthy ways to commit fraud online, making it challenging to keep up manually. Fraud and its means keep evolving every day, and therefore, it is necessary to keep updating your fraud prevention system. But this also puts a burden on your risk analyst team as manual reviews increase.
3. Scalability: Results in Bigger Manual Ops Teams
Old school fraud detection systems are not scalable at all. Hiring more hands for manual operations is an immediate reaction when your user base explodes. Moreover, fraudsters use sophisticated methods to steal customer data and impersonate genuine users, making it even more difficult to detect this kind of behaviour. You would need to employ a whole bunch of people who would keep at it 24/7, non-stop. Just imagine the labour cost of such an endeavour!
Detecting fraud with machine learning
With stakes getting higher and the magnitude of revenue losses getting more profound, it is not enough to just detect fraud after the event. Businesses need solutions and techniques to pre-empt fraud and take appropriate actions. In order to detect an anomaly in real-time, a lot of parameters need to be considered, such as past transaction trends, location, frequency, behaviour, device usage, etc.
Hence, fraud detection with machine learning (ML) becomes viable due to the ability of ML algorithms to learn from historical fraud patterns and recognise them in future transactions. They appear more effective than humans when it comes to processing information. Also, ML algorithms can find sophisticated fraud traits that a human simply cannot detect.
Here are other reasons why ML is the way forward:
1. Improve Operational Efficiency
Machines can take over routine tasks and the repetitive work of manual fraud analysis, while your risk analyst team specialists can spend time on making more high-level decisions.
2. Automated Decisions in Real-time
The results should be available instantly when it comes to fraud decisions. Research shows that the longer a customer's journey takes, the less likely they are to complete the transaction. Effective use of ML bring growth and fraud prevention together rather than pitting against each other.
Machine learning is like having several teams of analysts running hundreds of thousands of queries and comparing the outcomes to find the best result -in milliseconds. It continuously assesses individual customer behaviour, so it can automatically block or flag a payment for analyst review when it spots an anomaly.
3. Scale As You Go
Unlike manual reviewing, machine learning improves with more data because the ML model can identify the differences and similarities between multiple behaviours. Once programmed to separate genuine transactions from fraudulent ones, the systems can work accordingly and predict them when dealing with new transactions.
4. Cap Your Costs
Remember that machine learning is like having several risk management teams running analysis on hundreds of thousands of payments per second. The human cost of this would be immense - the cost of machine learning is just that of the servers running.
5. Faster and More Accurate
In the same way, machine learning can often be more effective than humans at uncovering non-intuitive patterns or subtle trends. Machine learning models are able to learn from patterns of normal behaviour. They are very fast in adapting to changes in that normal behaviour and can quickly identify patterns of fraud transactions. For instance, they can often identify potential fraudsters even when there hasn’t been an actual fraudulent transaction yet.
Custom ML Models for Fraud Prevention at Your Fingertips
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Summing Up
Unlike humans, machines can perform repetitive, tedious tasks 24/7 and only need to escalate decisions when a specific insight is needed. Machine learning techniques are obviously more reliable than human review and transaction rules. The solutions are efficient, scalable as they process a large number of transactions in real time.
Therefore, it is imperative for businesses to automate processes to ensure rapid decision making, increased productivity, business process automation, and faster anomaly detection, all at a fraction of a second.
