How Machine Learning Can Improve Fraud Detection in Real Time

"Machine learning" is a computer science discipline that refers to the ability for machines to learn with data and carry out tasks that would typically require human intelligence. The technology is growing quickly: according to Gartner, more than half of data and analytics services will be performed by machines rather than human beings by 2022, which is 10 percent more than today.

The emergence of machine learning and its implementation into consumer facing applications coincides conveniently with today’s real-time economy. Machine learning drives a decrease in fraud before it impacts the victim, just as our society has become as impatient as ever. In fact, more than 60 percent of people increasingly feel that waiting for something that should happen instantaneously impacts their perception of the underlying brand — which is especially true when it comes to identity or financial fraud.

Real-Time Decision Making Requires Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence (AI) are revolutionizing businesses, brands and even entire industries. They have the ability to drastically reduce labor costs, generate new and unexpected insights, discover new patterns and create predictive models from raw data. They also have the power to operationalize data analytics and enable real-time automated decision making that wasn’t previously possible. When applied to real data in an automated, low latency manner, the results can affect business activities even as they are happening, offering a real competitive advantage for organizations if harnessed and leveraged correctly.

An example of the tangible impact machine learning and real-time data analytics can have on high stakes business events is clearly seen in fraud departments across various industries. Below are several instances proving how the power of machine learning and AI can help prevent the occurrence of fraud.

Preventing Identity Theft and Fraud in Financial Services

Huawei Technologies, leading communications, information, and technology solutions provider, uses a translytical database to perform real-time fraud analysis for credit card and mobile payments transactions. Every time a card is swiped or inserted, or a phone is tapped or scanned, there’s either an authorization or a decline. The decision is driven by a machine learning model that can identify fraudulent behavior based on information from historical fraud data. This training occurs in a big data system that receives exported information from an in-memory translytical database, and the model then gets loaded as stored procedures or user-defined functions into the database multiple times a day.

It is important to emphasize the ongoing training needed in machine learning models. As fraudsters change their methodology all the time, it is vital to constantly update the machine learning fraud model to keep the quality of decisions high and the false positive rate low. All this occurs while live credit card events are streaming with zero downtime. An important distinction inherent to machine learning is a focus on prevention versus detection. Fraud prevention arms banks with information to proactively catch fraud in­stream rather than after the fact, resulting in higher customer satisfaction scores (CSAT) and reduced financial exposure. The ability to stop fraud before it happens is not just a cost saver for the financial institution, it also helps maintain a high brand value by minimizing exposure.

Mitigating Fraud in Digital Advertising

Much like banks, adtech providers must deal with fraud quickly. In this instance, the perpetrators are ad bots, which are malicious code bits that behave like humans to commit fraud. Ad agencies and advertisers lose millions of dollars annually and ultimately take a hit to their brand reputation as a result of Internet fraud rings like Methbots. For instance, these bots can spoof popular video content on which publishers sell ad space and then simulate a human interacting with the video via programmed mouse movements and fake social media information. Another example of malicious behavior in adtech is click fraud, which occurs when a fraudster clicks (manually or automatically) on an advertisement with the intention of inflating click numbers.

To detect and deal with click fraud in real time, advertisers need to monitor each click, detect anomalies, and respond appropriately. The solution must be fast, accurate and flexible enough to keep up with modern fraud attacks. Detecting and stopping this type of fraud requires a database capable of ingesting large streams of both legitimate and fraudulent traffic, and deciding which traffic falls under each category — before authorizing ad spend.

By combining machine learning and AI, companies are able to detect data anomalies in five to ten milliseconds and make decisions based on information as it happens, even anticipating results. Together, AI and machine learning are powerful tools, and adding to that a fast in-memory translytical database, the results are significant advancements within many areas of business. As the ability to predict and prevent becomes more widely adopted, consumer tolerance for fraud will reach zero and will ultimately be the differentiator between success and failure.