AI is used in fraud detection in various ways, such as it can help in Real-time detection, developing fraud scores, and implementing KYC measures.
FREMONT, CA: In the last 60 years, the financial services industry has undergone an impressive transformation. Traditional paper-based institutions that required in-person transactions have been replaced by Automated Telling Machines (ATMs) and mobile and online banking.
In response to this online shift, banks and other financial institutions are facing new challenges when it comes to creating safe and secure customer experiences. TransUnion found that global online fraud attempts for financial services increased 149 percent between Q4 of 2020 and Q1 of 2021.
The following are some common types of banking fraud:
Unauthorized transactions: Banks and consumers continue to be irritated by non-authorized banking or credit card transactions. Approximately eight out of ten mobile banking users are concerned about credit card fraud, according to a Forbes article. Statistica also predicts that fraudulent payments made with payment cards worldwide in 2021 will total $32 billion, rising to $38.5 billion by 2027.
Identity theft: Consumer complaints about identity theft are among the most common, according to the FTC. Approximately $13 billion was lost to identity fraud in 2020 alone, according to Javelin's 2021 Identity Fraud Survey.
Phishing scams: During the year 2020, the FBI reported that Americans lost more than $54 million to phishing scams. Phishing scams can cause unauthorized transactions, account takeovers (ATO), data breaches, or identity theft for both consumers and corporate employees.
In order to help identify and prevent fraud, banks now have a secret weapon at their disposal, which is artificial intelligence (AI).
AI is being used by banks in a variety of ways for fraud detection, such as:
Creating purchase profiles: A financial institution must first understand what typical customer behavior looks like in order to detect fraud accurately. With machine learning, banks can create and slot customers into a variety of profiles based on past financial and non-financial transactions. It is useful to have profiles because they provide an up-to-date picture of account activity and can help predict future behavior. With real-time updates to their profile after each transaction, a single account could be classified into hundreds of different profiles based on their activity. AI analyzes transactions to determine whether they fit a pattern or deviate sufficiently from the norm to warrant flagging.
Fraud investigation: A machine learning algorithm can analyze hundreds of thousands of transactions per second. A neural network takes this capability a step further by making decisions in real-time. It is possible to cull the unmanageable number of flagged transactions by using these technologies and provide a concise list of those that require further investigation by a human counterpart. Agents must be equipped with the proper tools to increase efficiency when investigating and prosecuting fraud claims, which can take a great deal of time. Augmented intelligence can help teams prioritize and streamline investigations.
Know Your Customer (KYC): With AI-backed KYC measures, ID and documentation can be verified, fingerprints can be matched, and even facial recognition can be done almost instantly. The powerful tool strikes the right balance between customer security and convenience.
