Artificial Intelligence (AI) is having a huge impact on many industries, enabling us to go about our business in a more efficient and intelligent manner.
One area where we are already seeing AI have a massive impact is with regard to fraud detection and prevention. According to the FTC, there were 2.8 million fraud reports submitted by customers in 2021, representing an increase of almost 27 percent when compared with 2020.
This statistic alone shows why we need to be doing everything in our power to prevent fraud, and fraud detection AI solutions could be the perfect answer here.
With that being said, continue reading to discover everything you need to know about AI and how it is transforming fraud detection within the financial services sector.
Common types of financial fraud
Many different types of fraud have been identified within the financial services field. Some examples are as follows:
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Identity theft - The FTC has reported identity theft as the most common type of complaint consumers lodge. Identity theft has a massive impact on both financial institutions and their customers.
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Phishing scams - A phishing scam involves a criminal using scam phone calls, text messages, or emails to trick their victims into doing the wrong thing, for example, clicking on a bad link that downloads malware onto their computer.
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Unauthorized transactions - Credit card or banking transactions that an account holder did not make or approve are a nuisance for both consumers and banks alike. In fact, approximately 80 percent of mobile banking users are worried about credit card fraud.
What is Artificial intelligence (AI)?
Artificial Intelligence (AI) involves leveraging machines and computers to mimic the decision-making and problem-solving capabilities of the human mind.
This is achieved via four different branches of artificial intelligence; self-awareness, theory of mind, limited memory, and reactive machines.
How do fraud detection and AI work?
In fraud detection, leading software utilizes machine learning, which involves a collection of AI algorithms that are trained with your historical data to suggest risk rules. You can then enforce these rules so that certain user actions are either permitted or blocked. For instance, you can block any transactions that appear fraudulent or suspicious log-ins.
When training the machine learning engine, previous cases of non-fraud and fraud must be flagged so that you do not experience false positives while also enhancing your risk rules’ precisions. The longer an algorithm runs, the more accurate the rule suggestions are going to be.
The different ways that AI is transforming financial services in terms of fraud detection and management
There are a number of ways that AI is helping financial services get a better hold on fraud detection and management. So, let’s dive a little bit deeper.
Improve your financial risk assessments
The very foundation of AI is about learning from past data or experiences. Because of this, AI has been successful in reforming approximately 70 percent of risk analytics in the financial sector.
It has achieved this because AI has applied automation to areas that demand analytical intelligence and clear thinking.
AI is providing the financial services sector with much more thorough and progressive risk assessments. By evaluating past data, AI is able to make recommendations regarding credit offerings and loans, as well as running verification checks so that approvals are not granted to hackers and criminals.
Preventing false insurance claims
Aside from the points, we have mentioned so far, AI is proving to be effective in battling fraud by preventing false insurance claims. It is not uncommon for insurance providers to receive false claims for businesses and individuals.
If the insurance company is not able to determine these claims in advance, they may end up paying out on the claim, which can result in thousands or even millions of dollars lost.
Advanced machine learning is proving beneficial because it is making it easier for insurance agencies to determine any false claims. These claims will be identified by the AI system, meaning they can be further inspected by a human representative.
One insurance company called AKSigorta has revealed that by using an AI solution they have been able to enhance the detection of fraudulent claims by as much as 66 percent.
Boost your support for customers with security concerns
You will also find it is a lot easier to help customers who have queries or feel that their accounts have been compromised. AI chatbots, for example, have become incredibly common nowadays, and they have proven to be an excellent time-saving and satisfaction-enhancing resource.
While it is true that bots may not be able to get to the nitty-gritty of a fraudulent transaction query, they can ensure people end up with the right advisor, as well as assist with some of the more standard procedures, for example, blocking and replacing a lost credit card.
Identify strange behavior
The main aim of any financial business is to ensure that risks are reduced as much as possible. Fraud is at the top of the list in this regard.
Artificial intelligence uses past spending behaviors to gather critical information that can be used to determine any suspicious or odd activity on a person’s account. This is done by scanning, studying, and mapping previous transactions.
This can be anything from common credit card fraud to directing a large number of funds to an account or something along those lines.
Detecting anomalous transactions
Sometimes there are bank transactions that may not appear the same as standard transactions but bank staff struggle to notice them. This is what we call anomalous transactions.
They are not always fraudulent transactions. However, they can sometimes be linked to money laundering, which is why they require a further inspection so that you can make sure you do not unintentionally facilitate money laundering activities.
AI and machine learning models are effective in locating these anomalous transactions. They can find these transactions in real-time, and they will send an OTP to the registered mobile number of the user so that they can confirm whether or not they initiated the transaction.
If they did, all is well and good. If they did not, you can take steps to block their card and issue them with a new one so that they do not suffer any financial losses. Or, if the purchase is being made through your store, you can block it from going through.
Throughout an audit, AI and machine learning can also be utilized to determine any anomalous transactions in the records that may have not been spotted otherwise. It is not uncommon for such audits to uncover foul play.
Preventing phishing scams
Hackers target vulnerable individuals by sending them links via text messages and emails that appear to have come from their bank account or credit card company.
When someone selects the link, criminals will get their card details or banking information, and they will then use this to conduct fraudulent transactions.
You may assume that people are able to easily identify these sorts of emails, so they will not click on them. However, these emails look incredibly realistic, and so many people do fall victim to them. In fact, statistics show that every 3 in 10 phishing emails are opened by the vulnerable targets who receive them.
Although banks do not have a direct responsibility to reduce phishing scams, we are seeing that email providers are stepping in. For example, Google is using AI and advanced machine learning to ensure that users are alerted whenever they receive an email that looks like spam.
In fact, did you know that Gmail’s machine learning blocks over 10 million malicious and spam emails every 60 seconds?
This is a monumental figure, and it helps you to get some sort of understanding of just how many people would fall victim to fraud if Google and other email clients had not made the most of AI as part of their defense against this.
Final words on how AI is transforming fraud detection in financial services
As you can see, AI can be used to assess large numbers of transactions so that fraud trends are uncovered, which can then be used for the purpose of detecting fraud in real-time.
When an AI model suspects fraud, transactions can be rejected altogether or they can be flagged to a member of your team who can investigate them further. Either way, this ensures that financial services teams are making a proactive effort to keep fraud incidents as minimal as possible.
It is also worth noting that AI is only going to get better and more accurate as time goes on. This is for two reasons. One, technology is improving all of the time. Two, AI gets more intelligent as it learns more about your business.