New and up-to-date technology is critical to mitigating risk successfully and combating fraud more efficiently. Fraud detection and prevention systems, for example, analyze data, identify suspicious behavior, and signal the threat of fraud before it happens.
The 2019 True Cost of Fraud report suggests that merchants have to pay $3.13 for every fraudulent dollar. That number rose by 6.5% from 2018 when the cost of fraud was $2.94. These kinds of costs demand a solution that can tackle this problem effectively.
According to Allied Market Research, the global fraud detection and prevention market is forecasted to grow from $15.83 billion in 2017 to $40.61 billion by 2023 (at a CAGR of 17%). The growth within this space is driven by an increase in fraudulent activities worldwide.
Leading the fraud detection and prevention space are the domains like artificial intelligence, blockchain, and security testing. All these technologies and methods can be used separately or together to fight the internal (coming from employees) and external threats of financial crime.
Smart Algorithms Are Better at Fighting Fraud Than Humans
Sophisticated algorithms have proven to be far better at detecting unusual behavior quickly. Advanced and complex forms of payment fraud, for example, can be detected by analyzing oceans of historical and real-time data. AI algorithms can be trained to identify patterns and anomalies that may indicate the presence of fraudulent activities.
According to The AI Innovation Playbook, financial institutions with over $100 billion in assets are most likely to have deployed AI technologies to combat financial crime. As much as 72.7% of companies in this asset category are already using AI to engage in payment fraud detection.
When you add machine learning into the mix, cybersecurity and fraud detection protocols can become much more powerful. This is because smart algorithms are much better than humans when it comes to analyzing large-scale data sets and monitoring enterprise infrastructure in real time. ML algorithms can observe, learn, and identify fraudulent or abnormal behavior and ensure regulatory compliance in a matter of seconds.
There are two popular approaches to fraud detection:
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Rules-based Approach |
ML-based Approach |
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Slow and lengthy processing of data |
Real-time data processing |
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A lot of manual work required |
Automatic detection of every possible anomaly |
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Multiple verification steps required, which can negatively impact customer experiences |
Only a minimal number of verification steps necessary |
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Only obvious fraudulent activities detected |
Identification of patterns and hidden fraudulent activities |
As you can see from the above, the ML-driven model is far more sophisticated than the rules-based approach. ML for fraud detection also comes with the benefit of fewer false positives, making it easier to maintain compliance.
Distributed Ledgers Make Data Immutable and Impossible to Manipulate
One of the primary enablers of fraud is the lack of transparency in payment systems. It’s a growing problem in the business world that can be easily solved with next-generation technology, namely, blockchain. It has all the fraud prevention capabilities that can address many problems enterprises face in the current threat landscape, effectively.
While the blockchain may be world-renowned for cryptocurrencies like Bitcoin, smart contracts and distributed ledger technology can do so much more. The blockchain, by nature, is a highly transparent decentralized (shared) ledger. So it’s resistant to tampering, and only verified contributors can store and view the data in a robust security environment.
Especially in the financial sector, transactions can be complicated by factors like currency denominations, collateral, the time required for settlements, third-party mediations, and more. With every human interaction during this multi-step process, it can potentially create vulnerabilities that can enable fraud.
However, with blockchain, all this information can be shared in real time. The distributed ledger can only be updated when all its stakeholders agree, so this approach can significantly reduce one’s risk exposure to fraud.
The benefits of this cutting-edge technology haven’t gone unnoticed. Recently, Commerzbank started testing blockchain-based machine-to-machine payment solutions. It’s the first bank in Germany to develop a blockchain-based payment solution.
In this successful pilot test, Commerzbank processed payments between an electric charging point and a Daimler truck system. In this scenario, Commerzbank issued Euros on a blockchain and provided Daimler trucks with “cash on ledger” (or money on the decentralized ledger) to process the payment without any human intervention.
As more industries go through complete digital transformation, this type of autonomous machine-to-machine interaction will become the norm. So financial institutions such as banks will also need to be well-placed to serve the demands of the marketplace, securely.
Continuous Security Testing Is Critical to Staying a Step Ahead of Bad Actors
Engaging in regular security testing is the most straightforward approach that companies can embrace to boost security. As bad actors are continuously innovating and coming up with sophisticated tools to breach enterprise systems, businesses need to take a proactive approach to fraud prevention.
It’s important to stress-test your infrastructure to identify any present vulnerabilities. Mastercard Threat Scan, for example, simulates fraudulent attacks against the issuers’ payment systems to identify any potential cracks and holes in the system.
However, it’s vital to note that there’s no silver bullet when it comes to security. Even the security tools we depend on, like antivirus software, need to be tested both thoroughly and regularly. That’s why leading cybersecurity software provider, Kaspersky Lab, engaged a1qa engineers to conduct functional tests on their new web portal to ensure that it worked as desired.
By engaging in security audits and automating repeatable tests, Kaspersky Lab was able to better understand the security posture of their latest offering.
As we enter a new decade of hyper-digitization, security audits, security testing, and cybersecurity workshops will need to become a core part of the corporate culture.
Final Thoughts
When it comes to successfully fighting fraud, there’s no turnkey “one-size-fits-all” type of solution. It will require a proactive approach from all stakeholders leveraging new technologies like artificial intelligence, ML, and blockchain. Staying ahead of bad actors will also demand a cultural shift. So both SMEs and corporate giants will need to engage in active security testing and cybersecurity workshops to stay a step ahead of fraudsters.
