Artificial Intelligence and Machine Learning are paving the way to a
melange of financial services, products and transactions online. It is
allowing a course into the digital realm that was otherwise unexplored.
The implementation of advanced technology has led to an astounding
increase in businesses that specialize and embed financial
transactions online. This shift has concurrently ushered an increase
in online fraud that is now proving to be a challenge to every
business and customer who engages in online transactions.
The loss caused by fraud is not only borne by the victim but also
puts the reputation of the financial institution associated with it at
stake. Moreover, financial regulators charge a hefty penalty from
financial institutions for allowing their platforms to be used for fraud.
Fintech face several risks but the top areas are-
- Forging credit reports and legal documents
- Skimming debit and credit cards
- Counterfeiting of personal information
- Fraudulent payment transactions
- Money laundering
- Forging account statements for tax and loan benefits
- Synthetic identity fraud
- Loan Frauds
Fintech uses APIs to peruse credit history and ascertain customer
relationships with banks to determine the scope of financial
assistance. They use scoring models that involve special algorithms to
calculate the creditworthiness and authenticity of the applicant.
Several indicators determine the scope of fraud like country code,
geolocations, BINs, transaction amount and patterns, expenditure
pattern, etc. Following are the key AI techniques used by fraud
- Data mining to signify patterns
- Expert systems to create rules for detecting fraud
- Pattern recognition to detect approximate cluster or pattern of
- Machine learning techniques to automatically identify unusual
patterns in datasets
- Neural networks to learn suspicious patterns from samples
Fraud Detection through Machine Learning
There are two methods of detecting fraud through Machine Learning-
- Supervised algorithm- Organized data is fed in the system tagged
with labels of fraudulent and non-fraudulent activities. The program
learns the patterns between the two for interpretation on future transactions.
- Unsupervised algorithm- Unorganized data with minimal sorting is
fed to the system. The program understands the data, sorts it and
finds anomalous behaviours on transactions in detecting the patterns
for fraudulent activities.
Fraud detection is the first step to the prevention of fraudulent
activities. Here are methods to prevent fraud-
- Face detection technology- Procure a 360-degree facial image of
customers to ascertain their identity.
- Customer behaviour- It is advisable to use predictive analytics to
understand customer behaviour and patterns to observe changes or
- Biometric security- Biometric acts as an additional barrier to
already existing security. Forging biometric is tougher and incurs a
cost to fraudsters thereby preventing fraudulent activities.
- Keep your customers informed- Educate your clients on basic do’s
and don’ts while transacting or availing online financial
assistance. For instance, customers must never share their OTP or
CVV number with any third party.
- KYC- Fraudsters are often well equipped and can forge legal
documents. Hence, it is imperative to cross-check before onboarding
- Reporting process- Machine learning and neural network assist in
generating reports of suspicious activities. File Suspicious
Transaction Report (STR) as and when the system highlights.
An example of how fraudsters are leveraging technology
Banks allot their customers a unique number at the time of issuing a
card. The first 4-6 digits of the said card number is a Bank
Identification Number (BIN). This code is specific to each bank and
the digits that follow are unique to each customer.
Fraudsters identify BIN and leverage technology to try combinations
to ascertain a legitimate unique customer number. With the help of
artificial intelligence and trial and error method, they obtain CVV
and the date of birth associated with the said card number.
Fraudsters then proceed to make transactions on the card. In the case
of an already issued card, the transactions are complete. If not, the
banks are highlighted this fraudulent activity.
Interestingly, both the parties, banks and fraud have evolved and are
leveraging technology. While technology is a boon, it is also a bane
that requires a constant upgrade.