Fraud is getting more sophisticated
As online payments have gone up rapidly, so have cases of fraud. Identity theft is one such emerging area of fraud that is becoming a major concern. The most common forms of this are real name theft, synthetic theft, and account takeover. Fraudsters use stolen personal data of consumers to open fake accounts that are then used for illegal activities. Identity theft is tough to detect, and it puts businesses and people at risk.
The explosion in digital payments has further increased the number of points that fraudsters can hack. Fraud detection systems now have to analyze and authorize a larger volume of payments. This is putting a lot of pressure on these systems as they deal with huge amounts of data.
The growing appeal of digital currency like cryptos also raises new security issues. The anonymity they bring to users also makes them an ideal choice for money launderers and ransomware payments.
The boom in e-commerce and online transactions has increased the scope of work for fraud detection teams. Real-time monitoring and the use of AI and Machine Learning needs to be at the core of fighting fraud. Legacy systems are limited in what they can achieve, especially as fraudsters continue to have access to advanced tools and tricks.
Why legacy systems and methods are not enough to detect fraud
When it comes to detecting advanced fraud, legacy systems and methods often fall short.
The current payment ecosystem supports a wide range of payment methods and currencies. This creates a lot of dynamic data. And you have to analyze it in real-time. A siloed approach and manual management severely limit the ability to analyze this data and stop advanced fraud. Legacy systems are also tough to scale up.
Legacy systems that are threshold-based are unable to adapt themselves to changing scenarios and data. Hence, they throw up a lot of false positives. This creates more errors that have to be fixed manually and requires more effort and money. Legacy systems also struggle to identify false negatives, which leads to missing out on genuine fraud cases.
Legacy methods also involve a lot of manual tracking. Thus, businesses have to invest more time and resources to fight fraud. Also, with partial data available to teams, these efforts are plagued by biases and errors.
How AI systems can give you the much-needed edge
AI is already playing a crucial role in fighting payments fraud. They can perform complex functions in real-time and significantly reduce the effort and cost related to fraud management.
1. Real-time monitoring of dynamic data
AI can work with large volumes of complex payments data, analysing them and detecting anomalies. AI systems monitor data from varied sources and can correlate them to track any changes. AI algorithms can this way detect suspicious activities and fraud patterns.
2. Reduced false alarms
AI systems use machine learning to reduce the number of false positives, false negatives, and alert storms. Thus, your teams are free to focus on those alerts which need their time and resources. AI systems correlate data from varied sources, which makes it possible to know when a transaction is legit, thereby reducing false declines. AI systems can also detect false negatives and actual instances of fraud, no matter which methods fraudsters use.
3. Improved detection through a continuous feedback loop
Team members working on AI systems constantly share feedback with the algorithms. This enhances the systems’ ability to learn about and detect newer fraud patterns and types.
4. Lowered costs of fighting fraud
Efficient AI systems that prevent fraud lead to significant cost savings. Not only do they keep your revenue from fraudsters, but they also remove the costs related to the manual investigation of fraud. Managing fraud also brings long-term cost savings from reduced customer churn and stronger customer loyalty.
How can a payments partner help?
Before using AI in your payments, you have to talk to the right payments partner. The right partner can offer you tools and advice that match your business needs. They should be able to provide you with bespoke payment fraud prevention tools that are most relevant to your business. Thus, you will be able to enable or disable the fraud prevention tools as per your needs.
Look for a payments partner who uses advanced fraud prevention tools. These could include Ai and machine learning, advanced analytics, biometrics, etc. Added features such as risk profiling and automated chargeback management would be good to have.
Also, look for a payments partner who can work with you to reduce non-payment and payment failures. They should also give you the capability to analyze customer behaviour and prevent non-payment issues.
Finally, these AI systems should be easy to integrate and configure with your existing systems. This will help you to scale your capabilities when required and with minimal costs.
Gowri Shankar is the IT Application Security Manager at Novalnet with versatile knowledge in Programming and System/Security architecture. Having 11+ years of experience in the financial services industry, Cybersecurity, Payment Card Industry Data Security Standard (PCI DSS). Certified in Advanced Payment Card Industry Security Implementer (CPISI 2.0), Secure Software Lifecycle Professional (CSSLP) from (ISC)².