Fraud Prevention and Detection with Machine Learning In Finance and Insurance

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The first mention of credit cards dates back to 1887 (in Edward Bellamy’s novel A Look Back), followed by a mention of artificial intelligence and big data (in many works of Isaac Asimov).

Cyberspace and cyber hacking were described even later – almost at the end of the 20th century. Why do you think we started the article with this short excursion into the literature and history of technology? Yes, you’re right, technologies are developing in the order described by the authors of science fiction books, and today financial fraud is one of the global problems.

In this article, we will describe how fraud prevention with ML can help at least reduce the impact of malicious attempts on business and individuals.

Global Financial Fraud Statistics and Why Fraud Detection with ML Is Possible

Global financial fraud statistics are disappointing.

  • 47% of companies became victims of financial fraud in the past two years;
  • Millennials are the most attractive financial fraud objects – since they are the most active online shoppers and personal data sharing lovers. The last factor simplifies identity theft. 
  • In 2020, financial organizations bet on face recognition devices  -it is expected that they will be able to bring 20% more profits per year. 
  • Nilson predicts that the losses from credit card fraud only will amount to $35 billion in 2020.

What is more, credit card fraud has no boundaries and the following infographics with pressing trends and geographical locations of the fraud victims prove it. 

By the way, there is research proves that 85% of credit card fraud is related to card not present issues. This means that the only way, in this case, is to work with data and identify anomalies earlier then the fraud attempt will happen. 

Previously, detection systems worked on the basis of a single set of rules – and were able to detect cases of fraud only after they had already happened. Moreover, if a certain fraudulent strategy did not fit into the rules already known to the system, then the system considered them legitimate. It simply did not know that it could be otherwise.

In the case of machine learning, a certain set of fraudulent scenarios also exists, however, it works to get ahead. In other words, the algorithm detects an attempt to steal data or money before everything happened – carefully analyzing the factors, data, and events that preceded this assumption. And in most cases, it turns out to be right.

What ML Can Do for Finances and Insurance

As we have said, machine learning systems work in real-time. Here are the opportunities that they open up for the financial and insurance sectors.

Smart Loans Issuing

Loans are always risky ventures for banks – and one of the key sources of income. It is possible to use machine learning to reduce the riskiness of each loan by analyzing user data, as well as assumptions about intentions. For now, there are some developments for retail that determine the user’s intentions by the expression of his face. This idea would also be a reasonable solution for the banking sector to strengthen anti-fraud protection.

Behavior Analysis With Anomaly Detection Feature

In fact, every action that performs a machine learning algorithm is based on user behavior. However, user behavior is not the only decisive factor. Since these systems are capable of covering a huge amount of historical and current data, the conclusion about whether a certain act is legal or illegal is made taking into account a combination of factors.

As soon as a combination of factors indicates an anomaly, the system sends an alarm. Yes, the result can also be falsely positive, but this is exactly the task that is now in the focus of the attention of big data specialists and developers.

Money Laundering and Terrorism Financing Prevention

According to the statistics, 2-5% of the global GDP is laundered income. It sounds like a little indicator if to link it to percent, but in real money,  this sum may reach from 800 billion to 2 trillion dollars.

As for terrorism financing, there is no clear data on how much money is spent to organize and support attackers. However, storing money in bank accounts is one of the stages of terrorism financing process – in addition to gathering, moving and using. 

In both cases, there is the only way to detect if there is a probability of such issues. It can be done only with the complex analysis of great data arrays that will take into account the identity of a client, his transaction history, the list of countries he is constantly visiting or recently visited and much more information.

And by the way, the example of China proves this. In 2013, only four people were arrested for promoting terrorism – compared with 341 in 2016.

Credit Card Fraud Prevention

Credit card fraud is the most common type of financial fraud – even despite the fact that already everyone knows how to protect money and identity from attackers.

There are several methods for preventing credit card fraud:

  1. face recognition cameras near ATMs and in physical points of sale
  2. real-time anomaly detection  – when the systems are tracking each action of the user and conclude on whether his action is normal-typical or not
  3. additional security measures, for example, the request to enter the password or pin code one more time if the user enters the online banking system from a new IP. 

Top Use Cases to Take a Notice

Now let’s look at the practical ways of using machine learning in the financial and insurance sectors.

ML Applications in Insurance

Since the insurance industry is based on assumptions about events that may or may not happen, the most important thing that machine learning can do in combination with artificial intelligence (and most likely even the Internet of things) is to analyze the user’s intentions at the time of initiation and signing of the insurance contract.

Plus, it is also possible to determine how “problematic” a particular customer will be based on data from his previous insurance policies and other personal information.

ML Applications in Banking 

  • JP Morgan Chase developed a system that processes all contracts that were signed by the bank with companies and individuals. The first goal is to streamline the workflow, the next goals are the formation of data arrays on the basis of contracts to make qualitative assumptions about the needs of customers and prevent fraud.
  • Wells Fargo has created a system for analyzing client behavior. A strong feature of the system is its predictability, but so far only on the basis of behavioral factors.
  • Master Card advanced the farthest and introduced the Treat Scan System. It is a lifelong learning system that learns through hundreds of fraudulent scenarios and applies this knowledge to recognize new attempts.

Brief Questions and Answers

Here are some more things you need to know about ML and its applications. 

  1. Why ML is better for fraud detection compared with old methods? 

As we have said, the most important advantage of machine learning is that these systems are capable of preventing, rather than notifying, when a case of fraud has already occurred. For your customers, this means a safer experience and a high degree of trust in your company.

  1. What industries is it suitable for?

Machine learning technology, and its ability to recognize financial fraud, is suitable for all areas of activity where there is cash flow. This means that it is suitable for all areas without exception, including even state and non-profit structures.

  1. Are there any risks?

In a world devoid of absolute guarantees, there are always risks. The most important risk is an ethical one, associated with the fact that many users are unhappy with the fact that financial and other companies collect information about them and track their behavior in real-time.

The second risk is that the more valuable data you have about your customers, the more attractive you are to scammers. The only thing that remains is to soundly assess the risk indicator on each side of the weigher, and also to choose a reliable partner like the SPD Group to integrate machine learning systems into your business processes.

Conclusion

The fight against fraud is like a vicious circle – the more intelligent systems we offer for protection, the more cunning tricks hackers come up with to crack them. However, it would be unreasonable not to protect yourself and your clients at all. This requires a robust approach and a reliable vendor for the development of AI and ML fraud detection systems.