Recent advances in computing and storage systems have empowered a new wave of Artificial Intelligence (AI) applications that take advantage of deep neural networks and deep learning (DL). In the past decade, the deep learning requires very large amounts of training datasets, which were hardly available and very expensive to manage. Nowadays, DL is used in a variety of applications, which expose exceptional performance and enable functionalities that were not possible few years before. As a prominent example, Google’s Alpha AI has recently defeated Korean grandmaster Lee Sedol in GO, who is considered a genius in this particular game.
One of the main characteristics of AI techniques is their ability to learn patterns in massive data sets, as a means of enabling systems that require minimum or even no human intervention. To this end, AI experts have to build quite complex analytics models, which are trained and evaluated using very large amounts of data. Based on these models, AI disrupts entire business domains by undertaking complex problem solving, increasing automation and eliminating human-mediated, error-prone processes. In these ways, it optimizes business processes, thereby ensuring that tasks are carried out in a safer and in a more reliable fashion.
The detection and alleviation of fraudulent transactions are one of the primary applications of AI in areas such as banking, insurance, and retail payments. Fraud detection and prevention is typically based on the automated discovery of high-risk fraud-related patterns across very large volumes of transactional datasets, including streaming transaction data that feature very high ingestion rates.
There are different types of patterns that are indicative of potential fraud. As a prominent example, human (e.g., customer) behavior patterns can lead an AI agent in altering a risk profile towards increasing the potential risk. In particular, transactions that occur in particular geographical areas might raise suspicion and increase risk. Likewise, transactions linked to high-risk parties (e.g., merchants without a good track record or a bad reputation) can be an indication of fraudulent activity. Other factors that can raise suspicion include the IP addresses of the parties involved in a transaction, relationships to suspicious persons or activity in social media, as well as the emergence of one or more transactions with unusual monetary value. An AI analytics model will typically consider all the different fraud-related indicators based on different weights and scores. To this end, the use of large amounts of training data enables the specification of parameters and weights for complex neural networks that can effectively score and classify fraudulent transactions.
One of the main trends in using AI for fraud detection is a shift towards predictive and preventive detection. The latter refers to the timely detection of fraud-related patterns i.e. before any fraudulent transaction occurs, as a preventive measure. Such a proactive detection requires systems that can automatically collect customer-related datasets (e.g., credit card transactions, mobile payment), while at the same time analyzing them nearly in real-time. Hence, real-time AI requires a real-time technical architecture, which can handle streams of high-velocity at a very low latency.
Another success factor for AI-based fraud detection is the presence of domain experts in the data scientist team. Such experts must have a strong knowledge of the fraud domain, which will help them build analytics models with appropriate parameters and weights for the fraud detection task at hand. Accordingly, they can improve the AI learning process through tweaking parameters and refining weights based on feedback from the models’ evaluation using real-life datasets. In this way, experts can develop fraud analytics algorithms with optimal performance. Moreover, they can use the evaluation results towards improving their knowledge about fraudulent attempts, through observing and detailing the characteristics of normal purchasing behaviors, while at the same time differentiating them from fraudulent processes.
AI scientists that specialized in fraud detection employ the following best practices towards developing and deploying effective solutions:
Like in many domains, AI in fraud detection promises to deliver exceptional automation and intelligence. This could allow the development of systems that are very effective in detecting fraudulent transactions, including predictive analytics systems that are able to identify fraud-related patterns and indicators even before actual fraud occurs. AI systems are currently used in conjunction with human intelligence in order to reduce manual tasks, save time and reduce costs. For example, an AI system could automatically classify 99% of potentially suspicions transactions and act upon them by asking for extra verification, leaving to humans the rest 1% of borderline cases. That’s certainly part of how AI is disrupting the finance, retail, and insurance sectors.
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