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article imageQ&A: How AI is helping businesses address fraud cases Special

By Tim Sandle     Apr 21, 2019 in Business
Within healthcare there have been some recent serious cases of fraud, as well as across other businesses. One such affected company was Walgreens. According to expert, JJ Lopez Murphy, applying AI can help to address these issues.
The way that artificial intelligence can assist with catching patterns in distributed, incomplete data and enabling businesses to identify discrepancies that point to fraudulent cases. Within healthcare, this can help to save healthcare providers time and money when it comes to spotting intricacies.
To understand more about the benefits that artificial intelligence can potentially deliver, Digital Journal spoke with JJ Lopez Murphy, AI and Big Data Tech Director at Globant.
Digital Journal: What is the state of artificial intelligence in the U.S.?
JJ Lopez Murphy: Undoubtedly, the U.S. is one of the most advanced and a leading country in terms of AI development, together with (and currently racing against) China, and in a lesser extent with the European Union and India.
AI has made a great impact and has gained a penetration/acceptance level in different industries, with a wide set of applications. Most of the companies that have adopted and embraced AI are mainly high-tech companies or large companies where scale can better justify the investment and handle a higher risk. The current challenge large cloud providers are trying to solve is making AI easier to adopt and at the same time, making it more cost effective for small- and medium-sized companies.
AI is currently at the top of the hype cycle, in a position that other already consolidated technologies, such as Big Data, IoT or Blockchain, were at a few years ago.
DJ: How can AI be used to detect fraud?
Murphy: Fraud detection has gotten more and more complex to achieve due to the increasing sophistication and diversification of threats and scams. Fortunately, the continuous improvement of AI over the last few years has enabled the development of new strategies to approach fraud detection. Some advances in Deep Learning, particularly the development of Generative Adversarial Networks (GANs), have made it possible to evaluate the robustness of fraud prevention mechanisms by faithfully mimicking hundreds or thousands of user interactions, thus enabling solutions that range from screening commercial transactions to helping to understand how organized crime works.
It is worth mentioning that the strengthening of AI has also helped to boost traditional fraud detection mechanisms. For instance, it is now possible to apply Machine Learning to automatically (and with great precision) detect anomalous behaviors by creating models that understand and predict each client’s expected action, thus identifying even the most subtle inconsistencies in behaviour.
DJ: How can new ways of analyzing data help avoid fraud?
Murphy: GANs are an amazing tool to test fraud detection models that allow for a faster review than was possible in the past due to their tremendous capacity to generate large volumes of data.
The tools that are now used on a daily basis by Data Scientists allow for the review of larger volumes of data in less time. Organizations have their eyes on AI as a means to streamline the data analysis process. As it stands, 80 percent of businesses believe AI can immediately improve their operations, and nearly half (48 percent) of decision makers believe that AI surfacing consumer insights from massive data sets can make the most immediate improvements. For example, Long short-term memory neural networks are great for classification and predictions based on time series, which are fundamental for fraud detection analysis.
DJ: Where else can artificial intelligence be leveraged in business strategy?
Murphy: AI can be really helpful in augmenting humans for better decision making. For instance, there are AI-based solutions that assist medical doctors in identifying and predicting cancer or evaluating the severity of other affections (like psoriasis), thus augmenting our human capabilities.
Through the use of existing data and building AI products on top, you can enable new streams of revenue. We have been generating huge amounts of data over the last decade, but there are many correlations in that data that have not yet been explored, and that can lead to new service offerings that would satisfy customer’s needs even when those needs are not explicit. For example, data coming from mobile phone could provide greater understanding into how people move throughout a city. Applying AI to this data can help in better city planning, including new mobility options like changing a street direction, adding bicycle lanes, etc.
At the same time, AI can be used to build augmented versions of organizations, focusing more on internal processes to enable profound company transformations. It can reshape key areas, structures and processes. For instance, it can analyze employees’ soft and hard skills, and then help managers create unique opportunities for personal and professional growth, build teams based on complementary employee strengths and encourage cross-team collaboration. It can also help rethink core departments, such as recruiting and operations.
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