Can artificial intelligence prevent cyber fraud?
Omri Kletter, Global VP of Cyber-Fraud and Risk Management at Bottomline Technologies, shares his tips on preventing the most dangerous forms of cyber fraud.
What kind of cyber fraud is it? Fraud means any illegal activity that leads to financial gain. In the virtual world, it's called cyber fraud.
This includes everything from money laundering to identity theft, hacking, and data theft. As digital financial transactions grow, so too do cyber fraud opportunities.
The four main risks of cyber fraud:
1. Account takeover - Fraudsters gain access to someone else's account and withdraw money
2. Application Fraud - Scammers pretend to be customers and apply for loans / credit cards etc with no intention of paying back
3. Employee Fraud - Employees use loopholes within the company to make fraudulent payments
4. Authorized push payment fraud - tricking consumers and businesses into making payments to an external account
Which companies are particularly at risk?
Any organization that processes payments and money movements virtually is vulnerable to cyber fraud. As a provider of payment technology, we see time and again that companies are resting on their old successes when it comes to cyber fraud.
However, a company can suffer enormous financial losses from payment theft and the resulting publicity seriously damaging its reputation.
It is therefore essential for companies to keep checking the end-to-end security of their financial messages and the virtual movement of their funds.
In order to secure our entire Internet environment, we have to protect ourselves against so-called «first-party fraud», where stolen data is used to apply for credit cards, loans, overdrafts or unsecured credit lines that are never repaid.
We must also protect ourselves from fraudsters who pretend they are acting on behalf of a company or from those who try to gain access to the accounts of individuals.
How to protect yourself
There are several ways in which you can reduce the risk of cyber fraud:
· Collect as much data as you can to aid in fraud detection
· Invest in machine learning and artificial intelligence and combine them with traditional analytics to improve automatic fraud detection and subsequent fraud evaluation
· Share best practices and data with partner organizations - corporations, banks and regulators
· Train your employees so they can reliably identify signs of internal and external fraud.
By integrating advanced data analysis, artificial intelligence, machine learning and secure gateway technology, we can help companies protect against all types of cyber fraud .
While we can't always prevent someone from being persuaded into a transaction, by creating authentication logs that detect unusual activity we minimize the risk of push payment fraud.
The use of advanced software platforms to review, monitor and analyze payments also protects against account takeovers, application and employee fraud. It does this by analyzing several indicators, including location and IP addresses, applying behavioral profiles and observing past customer data.
If there are doubts about the authenticity of a transaction, the technology automatically generates an alarm and further checks are carried out before the transaction is approved or the account is possibly blocked.
The best protection
Every company has its own way of handling payments, and each type of payment has its own risk.
The most promising type of fraud prevention is therefore to work closely with payment service providers such as Bottomline in order to map the payment channels from initiation to execution and to create secure gateways at every node.
Tailored payment processes
This also includes the “last mile”, which is particularly susceptible to fraud, as this is where the bank communicates with external parties, such as other payment networks.
The ultimate goal of working closely with Bottomline's customers and partners in the fight against cybercrime is ultimately always the same: to stay one step ahead of the scammers and stop them before they can even strike.