Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling.
One of machine learning's most well-known use cases is fraud detection, an area that has drawn the attention of a growing number of technology suppliers looking to develop the best algorithms and techniques to solve a problem that costs businesses millions of dollars each year.
Fraud management has been painful for banking and commerce industry. The number of transactions has increased due to increase in payment channels like credit/debit cards, smartphones, kiosks, etc. At the same time, criminals have become adept at finding loopholes. As a result, it's getting tough for businesses to authenticate transactions. Data scientists have been successful in solving this problem with machine learning and predictive analytics. Automated fraud screening systems powered by machine learning can help businesses in reducing fraud.
Machines are much better than humans at processing large datasets. They are able to detect and recognize thousands of patterns on a user's purchasing journey instead of the few captured by creating rules. We can predict fraud in a large volume of transactions by applying cognitive computing technologies to raw data. This is the reason why we use machine learning algorithms for preventing fraud for our clients. The three factors which explain the importance of machine learning in detecting fraud are:
1. Speed :
In rule-based systems, people create ad hoc rules to determine which types of orders to accept or reject. This process is time-consuming and involves manual interaction. As the velocity of commerce is increasing, it's very important to have a quicker solution to detect fraud. Our merchants want results fast. In microseconds!! Only machine learning techniques enable us to achieve that with the sort of confidence level needed to approve or decline a transaction.
Machine learning can evaluate huge numbers of transactions in real time. It is continuously analyzing and processing new data. Moreover, an advanced model such as neural networks autonomously updating its models to reflect the latest trends.
2. Scale :
Machine learning algorithms and models become more effective with increasing data sets. Whereas in rule-based models the cost of maintaining the fraud detection system multiplies as customer base increases.
Machine-learning improves with more data because the ML model can pick out the differences and similarities between multiple behaviors. Once told which transactions are genuine and which are fraudulent, the systems can work through them and begin to pick out those which fit either bucket. These can also predict them in the future when dealing with fresh transactions. There is a risk in scaling at a fast pace. If there is an undetected fraud in the training data machine learning will train the system to ignore that type of fraud in the future.
3. Efficiency :
Contrary to humans, machines can perform repetitive tasks. Similarly, ML algorithms do the dirty work of data analysis and only escalate decisions to humans when their input adds insights. ML can often be more effective than humans at detecting subtle or non-intuitive patterns to help identify fraudulent transactions. As discussed earlier, it can also help to avoid false positives. Moreover, unsupervised ML models can continuously analyze and process new data and then autonomously update its models to reflect the latest trends.
Since machine learning is a very popular field among experts today, there is a huge scope of innovation. Experimentation with different algorithms and models can help businesses in detecting fraud. Machine learning techniques are obviously reliable than human review and transaction rules. The machine learning solutions are efficient, scalable and process a large number of transactions in real time. But extracting data and training data sets for correct prediction is a tough task.