How AI is transforming Fraud Prevention in Banking and Finance

Frauds are the biggest challenge for the finance industry and their customers which results in huge losses each year. Credit card is the most common type of frauds and as per one report, there were 270,000 cases of credit card fraud in 2019. Most of the frauds happen with retail customers, but corporations and sometimes even banks find themselves at the direct receiving end of fraud that results in huge losses.

The finance industry is now utilizing state of art artificial intelligence technologies to intercepts these frauds as early as possible and prevent them from taking place.  Let us see some of the key areas where AI is being used extensively for detecting or preventing financial frauds.

Fraudulent Transactions

Cybercriminals often steal bank account details or credit card details by various means and use them to do fraudulent transactions and siphon off the money from the victim’s accounts. If the transactions are not stopped in a timely manner, it becomes difficult to recover money and are often lost forever.

Banks are now deploying machine learning models that can detect suspicious transactions in almost realtime, stop it immediately from happening and alert the authorities. Companies like Teradata and Datavisor provide specialized AI-based financial fraud detection solutions to banks. In fact, Datavisor claims that its solution can detect 30% more frauds with 90% accuracy. Such security implementation of AI has become an essential part of banking platforms for all the major banks now.

Phishing Scams

Cybercriminals target vulnerable people by sending them links over emails that are appeared to look like the mail from their bank. When people click on the link, criminals are able to obtain their sensitive banking or card details that they use for fraudulent transactions. As per the latest report, of cybersecurity company ProofPoint, they got user submission of 9.2 million suspicious emails in 2020. And unfortunately, around 30% of the phishing emails are opened by vulnerable targets.

Though banks are not involved here directly to prevent such phishing scams, but credit goes to email companies like Google who now have advanced machine learning models that alert the user that this is a phishing mail and should not be clicked or they just sent it to spam box instead. Gmail’s machine learning blocks more than 10 million spam and malicious emails every minute. This humungous figure is a good indication that if it was not for artificial intelligence, more people would be receiving phishing emails leading to financial frauds.


False Insurance Claims

It is not uncommon for insurance companies to get false insurance claims from people and companies. If such claims are not identified in advance, insurance companies might end up paying insurance money to the fraud claimant. A report suggests that insurance fraud results in a loss of at least $80 billion yearly across all lines of insurance and the US alone saw a loss of $34 billion in 2019. One-third of the insurers believe that fraud consists of up to 20% of the claim costs.

Advanced machine learning models help insurance companies to figure out such possible fake claims which they cherry-pick for closer scrutiny and inspections. Often these claims are indeed proven to be false thus preventing a huge loss for insurers. AKSigorta, a Turkish insurer uses advanced predictive analytics that decides within 8 seconds whether a claim requires investigation or not. This AI solution has helped them to increase fraudulent claims detection by 66%.

Detecting Anomalous Transactions

Sometimes certain bank transactions differ from regular transactions but are very difficult to come to the notice of bank staff. These are called anomalous transactions and they may not always be fraudulent ones. But sometimes they actually are and at times are also linked to money laundering.

Machine learning models are superior in spotting such anomalous transactions. They can detect such transactions in realtime and send OTP to the user’s registered mobile no. to confirm they only have initiated the transaction.

During audits also, machine learning models can be used to detect the anomalous transaction in the records that otherwise might have gone unnoticed. Often such audits discover internal foul play. Capgemini claims that there fraud detection solution can help to reduce investigation time of possible fraud by an impressive 70%.

Surveillance and Security

Surveillance cameras have become smart, thanks to the advancement in computer vision and IoT. Banks now deploy these advanced surveillance cameras on their premises and inside ATMs that can detect suspicious activities and quickly alert the authorities so that they can prevent them. Uncanny Vision specializes in providing such intelligent surveillance cameras for banks and ATMs.

Bank and other financial companies are also using biometric security features in their applications such as fingerprint, retina scan, facial recognition so that even if the phone falls into the hands of criminals the apps can be authenticated only by using owner’s biometric features. Some of the popular banks that have implemented biometric securities in recent years include RBS, Wells Fargo, Bank of America, Barclays.


Even with the best of the technologies and processes, unfortunately, criminal-minded people are often one step ahead. Though AI cannot prevent all types of frauds, especially those that result out from internal corruption, it is surely playing a great role to reduce and prevent the overall fraud.

Disclaimer:  The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance, and MBA in electronic business. Trehan is Senior VP at Deltec International The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries, and/or employees.