Fraud Prevention

Superheroes fighting criminals and saving the planet from devastating repercussions of evil is a novel concept. But those are stories for the kids, and the cutthroat competitive world has no superheroes to fight crimes like fraud and prevent catastrophes like substantial financial losses; or does it?

Technology has advanced by leaps and bounds in the past few decades. And so have the methods of fraud. But one of the gifts that we now have at our disposal to fight fraud is Artificial Intelligence. AI has been the dream and fascination of humankind for quite some time now, but now that it is here, many institutions are exploring its possible uses. One of the many fields in which AI has already made a mark and is progressing rapidly is fraud prevention and security.

Artificial Intelligence has been used as a data science to uncover intricate and complex patterns as well as hidden insights. So it’s no wonder that now, machine learning algorithms have evolved and has given rise to more accurate as well as efficient systems along with a forever expanding “dataverse” or Big Data. The advancement and implementation of machine learning in the consumer-oriented applications accord well with the real-time economy of today.

With lightning-fast transactions and millions of dollars in the space, the people are becoming more and more intolerant of financial fraud. And with machine learning in the scene, fraud prevention is easier than ever. Even though it is work in progress, with many new techniques of anti-hacking and expert security systems coming up, the financial sector is looking at a brighter future of revolutionized performance gains.


Characteristics of fraud

Artificial intelligence, as well as the data analytics, has a tangible impact on high stakes business events, especially in the fraud departments across various industries. There are many reasons why nothing short of artificial intelligence is ever enough for fraud prevention.

1. Fraud generally has a long tail distribution, and there are just too many unique cases which one needs to pursue.

2. Slow learning counter measures can’t keep up with quickly changing fraud patterns. The techniques of swindlers keep changing with advanced techs.

3. Implementation of over-intrusive countermeasures penalizes good customers and thus reflects poorly on the brand or business. Scam artists usually mimic good customer behavior, and this works as a plus for them.

Ways to put Artificial Intelligence to use

AI addresses each of the traits mentioned above of fraud and then some more when it comes to countermeasures against fraud. Many techniques are trending amongst various businesses.

1. Third party data: The application of third party data can help accomplish the goal of boosting sales and beating fraud. It also helps to uncover hidden insights and optimize the performance of any platform. Moreover, when applied to real data in an automated, low latency manner, the results can affect business activities even as they are happening in real-time, offering a competitive advantage for organizations if harnessed and leveraged correctly. It is leveraging scientific innovation to solve problems in the real world.

2. Translytical database: Artificial Intelligence also has the power to operationalize data analytics and enable instantaneous automated decision-making. Using a translytical database to perform real-time fraud analysis for credit card and mobile payments transactions is also an efficient way to thwart attempts at fraud. The decision to authorize or decline every time a card is swiped, say, is driven by a machine-learning model that can identify fraudulent behavior based on information from historical fraud data. It is powered by the Big Data system and the constant growth of consumer as well as transactional data. This is used to identify credit card transaction patterns, which are irregular for specific customers. Huawei Technologies already uses the translytical database to perform concurrent hoax analysis for credit card and mobile payments transactions.

3. Behavior learning: Other, more sophisticated models are also proposed and tested out by various companies. For instance, deep learning models which crunch the numbers to map user behaviors are much more effective anti-fraud measures as compare to the traditional algorithms which raised false positive flags for anything that falls out of a given set of parameters. This ensures that as the life circumstances and spending habits of any user changes, the model would automatically adjust what the system views as potentially fraudulent transactions. Additionally, this should increase customer satisfaction by limiting the number of times that a customer is barred from completing a transaction due to an incorrect flagging. It would also reduce the operational overheads of the financial institution by successfully preventing unnecessary interactions with such customers. Mastercard is currently implementing this.

4. Mapping the thought process: Taking AI a step further regarding fraud prevention, the future generations are already looking at advanced algorithms, which try to map the way people think. These are known as Convolutional Neural Networks and are based on the visual cortex. This infinitely smart technology can read a customer’s expenditure data and determine whether he performed a particular transaction or it was an act of fraud by someone else. This solution has a lot of potential not the least of which is curbing cybercrime and reducing the economic losses radically.

5. Detecting fraud in adtech: Not only banks but adtechs and digital advertising companies must also deal with fraud quickly. Internet fraud rings can cost ad agencies and advertisers millions in addition to the tarnish on their brand reputation. To deal with click fraud and other malicious behavior in real-time, adtech agencies need to monitor each click, check for anomalies and respond appropriately. Since this is a bit too much to handle for any human, a database capable of ingesting large streams of both legitimate and fraudulent traffic is required. Specialized AI algorithms are capable of deciding which traffic falls under each category, before authorizing ad spend.

6. Scoring the predictions: When it comes to application frauds, the game levels up and the risks are higher. When an inbound application is made to run past an AI predictive model, it siphons through copious amounts of data and ranks the transaction from highest to least likelihood of fraud. The deep learning models also provide an added layer of resiliency and longevity to the robustness of this score. These self-learning techniques, which are sensitive to complex, multi-variate attributes predicting a swindle are also known as adaptive analytics. It means that models can continuously learn about new behavior patterns by using the actual outcomes and feedback that fraud analysts provide in worked cases. These scores adjust and hone the attribute weights, mechanically refresh algorithms, and adjust final scores predictions accordingly. The advantage of this method is that once the system is through with the report, fraud analysts can review it and record insights for further machine learning.

Artificial Intelligence imparts the necessary transparency to pass regulatory muster while maintaining an accuracy of prediction. Since 1997, when the Deep Blue beat the chess grandmaster Garry Kasparov, AI has been growing and scaling new heights. It has now been adopted into the daily lives of the common crowd. Even though there is a lot of speculation regarding the pros and cons of using AI as the personal bodyguard of our data, there is no denying the significant leaps it has made in many areas of business. As the AI’s ability to predict and prevent becomes more widely adopted, the future generations will never have to worry about fraud and the consequent losses.


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