FRAUD DETECTION USING Machine Learning (ML) GRAPHS
Updated: Mar 3
With 1 in 5 people falling prey to imposter scams and reporting a financial loss, detecting frauds with an old-school approach will not let you keep frauds at bay. It’s high time that implementing machine learning graphs should be your top priority so that fraud management doesn’t become a hassle for you in the long run.
With the advancements in technology, fraudsters are getting more sophisticated over time. They are creating a network of synthetic identities that combine legitimate information like social security or national identification number, name, phone number, and physical address to secure accessibility. The Federal Trade Commission (FTC) findings show that in 2021 alone, frauds accounted for $5.9 in losses.
Legacy fraud detection is based on real-time analysis which includes analysis of the behavior of individuals, rudimentary rules, analytics, and checking anomalies from diverse data sets.
New technologies like machine learning or artificial intelligence have come into the picture to detect fraud. Software based on machine learning graphs identifies anomalous patterns in real-time with the use of predictive analytics and computing power. Hence when fraud graphs are applied to artificial intelligence or machine learning applications, the system finds more relevant and precise relationships between the entities.
This powerful fraud detection tool increases efficiency, thereby letting us use time more wisely. Through machine learning graphs, frauds with email phishing, payment fraud (credit card and bank loan scams), identity theft, account takeover, or synthetic theft can be prevented.
Let’s understand how machine learning graph systems work to detect fraud.
Machine Learning (ML) Graphs for fraud detection
When it comes to fraud detection, the more the data, the more precise will be the detection. For supervised machine learning, the data must be labeled under two categories- good (genuine customers who have never committed fraud) or bad (customers who have a chargeback associated with them or manually labeled as fraudsters. Once data is entered ML generates features (traditional features, behavioral features, real-time features, individual customer features, session tracking features, entity features, and network-derived features) to detect fraudulent behaviors. The different categories from which features are generated include identity, orders, payment methods, locations, and network.
A fraud detection ML algorithm is a set of rules to solve complex detection problems. Initially, the algorithm is trained on a seller’s historical data termed a training set. The more fraud in this training set the better, so the machine learns lots of examples from it, however, that's not always the case. Many times we have less than 10 fraud cases out of millions of transactions. Hence a lot of work needs to be done on building the right features for the consumption of graph algorithms. Once training is completed, we have a model specified which can detect fraud in milliseconds. It is timely improved, updated, and the new model is uploaded so that the system will detect the latest fraud techniques every time.
Benefits of Machine Learning (ML) graphs.
Let’s see the benefits of fraud detection using machine learning graphs.
Faster and efficient detection
The machine can quickly identify if the user has drifted from their regular app behavior. For example, if there is a sudden spike in the amount that the user has shopped for from a specific site, it could be an anomaly. Hence, approval from the user is needed for a go-ahead.
With machine learning graphs, the analyst’s team is enabled to work faster and with great accuracy. The power of data and insights are provided and everything goes in flow with accuracy.
Better Prediction with larger datasets
Machine-learning graphs improve 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.
Tools like Neo4 and Python libraries such as NetworkX help optimally utilize resources even with large datasets. With cloud storage, solutions can be scaled to millions of users in real-time.
Cost-effective detection technique
As less manpower is required, with machine learning at the core, the team is less burdened and more efficient. They just have to monitor and optimize machine learning fraud detection algorithms to meet the end user’s requirements.
The Way Forward
As graph learning has received broad attention in almost all fields, it offers a compelling approach to fraud detection too. Graph learning led to significant improvement in fraud detection but still, more work is required to enhance the scalability and real-time character of the system. A more efficient way to store massive graphs and conduct distributed training and real-time serving is required.
With a graph database, financial entities can see their data in “graphs” and more easily visualize patterns and opportunities to better predict when and where fraud might occur. To understand how machine learning graphs can be applied to your business problems, connect with ODA’s machine learning expert.