Unfortunately, fraudulent activities are commonplace in many industries — be it in healthcare, digital marketing or, for example, around credit cards. The damage this causes to the economy as a whole is enormous. Preventive measures, however, are particularly costly, not least because such activities are difficult to detect early. In addition, these measures have a high error rate, which can have a further negative impact on business. A new and increasingly popular way to navigate this fraud prevention dilemma is the use of artificial intelligence. Early detection of fraud through AI is a new, highly efficient method of mastering this challenge — and it keeps getting better.
Fraudsters are extremely efficient at exploiting even the smallest weakness in a system and can cause considerable damage. Hackers are particularly after personal data — preferably credit card details. But companies also need to protect themselves against fraudulent activities by customers themselves — insurance companies in particular are challenged here, for instance when it comes to claims or the unjustified use of medical services.
The effort required to detect such activities in time is very high — and on top of that, the error rate of manual strategies is usually high, which can ultimately be damaging to the business overall: for example, when customers are wrongly suspected and thus alienated. This can result in frustrated customers moving to the competition. The dilemma is obvious: fraud prevention is a necessity on the one hand. On the other hand, most conventional prevention measures cause high error rates with negative consequences for the company.
The answer to this complicated fraud prevention problem is the use of artificial intelligence.
The key to effective fraud prevention lies in machine learning, and therefore in the availability of user data required for it. Based on data of the highest possible quality, certain machine learning algorithms are able to analyze user behavior and determine which customers are likely planning or currently carrying out fraudulent schemes. To illustrate the problem of correctly identifying such activities manually, one has to keep in mind the extremely high level of creativity with which fraudsters generally operate: Given the high potential gains awaiting perpetrators who outsmart your system, they are willing to use every conceivable method and exploit every security gap, however small, until it leads to their goal. It is extremely difficult — if not impossible — to assemble a team that develops countermeasures with the same motivation to stay ahead of the fraudsters, especially since no comparable "rewards" await them in case of success.
The obvious answer to this challenge is the use of artificial intelligence. It is able to identify potentially suspicious users based on subtle anomalies in user behavior and alert your fraud detection team. This allows the team to work far more efficiently and concentrate on cases that the AI applications have already filtered out from among many thousands.
The most valuable thing companies have stored in their databases is usually their customers' credit card data. Hackers are of course aware of this fact and concentrate precisely on it — because hardly anything else can be turned into money so easily. Although spectacular data thefts repeatedly come to light, many companies take too few measures to protect themselves against such attacks. Yet thanks to modern AI applications, good answers are available today:
AI-based fraud detection is significantly more efficient at detecting fraudulent activities in your network than manually performed fraud prevention. The evaluation of existing data shows that fraudulent behavior always exhibits comparable patterns in user behavior. And it is precisely such anomalies in user behavior that are virtually impossible to discover through manual work.
The areas of application for fraud detection and fraud prevention are by no means limited to e-commerce. One area that benefits particularly is healthcare. Health insurers and insurance companies, for example, can profit enormously from AI-based fraud prevention.
Year after year, such institutions and companies suffer high expenses due to cases of fraud, which often add up to additional costs in the billions. Thanks to machine learning, a dramatic increase in efficiency in the early detection of such illegal activities can now also be achieved in this area. Many cases of fraud are very difficult to detect manually and require a great deal of manpower. Artificial intelligence, on the other hand, can identify or predict anomalies in user behavior much faster, in less time and with greater accuracy. This is also done here by analyzing user data: if an AI algorithm is fed with the corresponding data, it can filter out the typical patterns that are conspicuous in fraudulent activities. Since such anomalies usually appear at an early stage of fraud cases, it becomes possible in many cases to treat individual cases with the necessary skepticism at an early stage — potentially avoiding high expenses from lengthy proceedings with considerable personnel costs.
Ultimately, this allows insurers to help those who really need it with greater accuracy. As a consequence of using such AI applications, it becomes many times more difficult to abuse and exploit healthcare services in the first place. The result is lower costs for the service providers and, ultimately, lower prices passed on to customers through saved expenses — a win-win situation for honest customers and providers alike.
Another large area where fraud detection is a major topic is insurance. Faked claims cost insurers billions every year and, not least, drive up the prices for those very policies. As in the two cases already mentioned, an appropriate AI application can also search claims data for anomalies that indicate probable potential fraud. In this way, insurers too can save enormous costs through AI-based fraud prevention.
Fraud detection, or fraud prevention, is one of those fields in which artificial intelligence can be particularly effective. In addition to the major areas mentioned, such as credit card fraud or abuse in healthcare, the use of this technology can bring considerable benefits in many other industries. Take advantage of the efficiency of such an application — with the help of our tailor-made solutions.
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