How AI Can Help Combat Fraud
How AI Can Be Used to Detect Fraud
Given the digital nature of the business world today, fraud identification, prevention and elimination are usually at the top of every financial institution’s list of priorities. Still, many of them perform these processes manually, following outdated, rule-based methodologies that involve review and analysis of potentially fraudulent events by human staff. Not only is this way of identifying incidents time-consuming, inaccurate and expensive, but it is also generating many false positives, which also need to be investigated and accounted for, further increasing inefficiencies.
Machine learning can solve many of these issues, helping identify and process risky transactions at the fraction of a second, thanks to big data, advanced algorithms and processing power. Machine learning can be both supervised and unsupervised, and financial institutions should ideally use both in building a holistic fraud prevention and identification strategy. That is the only long-term solution to keeping up with hackers and fraudsters who are becoming increasingly more sophisticated, agile and technologically advanced.
Advantages of Applying Machine Learning and AI to Combatting Fraud
Unlike manual review processes, machine learning and AI improve over time as algorithms self- learn to accurately recognize legitimate events that require intervention by a human expert, effortlessly distinguishing them from false positives. Computer software that utilizes AI is capable of quickly and efficiently analyzing terabytes of data, identifying pattern deviations and potential red flags in a matter of seconds, thus achieving unprecedented scale. Although AI would not replace the need for human experts altogether, it can be a significant aid in their daily work, driving efficiency and accuracy.
Furthermore, relying on artificial intelligence in fraud prevention means that potential anomalies that fall outside of the preset rules will not be missed, as machine learning algorithms are ‘smart enough’ to find unpredictable and unexpected patterns in the data they receive, too. This is something that today’s rule-based, manual processes are largely uncapable of, making more complex attempts at evading fraud detection currently untraceable.
Machine Learning and AI Applications in Detecting Financial Fraud
As fraud detection software based on AI becomes more advanced and user-friendly, we will see this type of technology being used in virtually all industries, processes and transactions with an inherently high risk of fraudulent attempts. On top of AI, blockchain can provide an added level of security in verifying financial transactions, and the combination of the two is becoming a viable alternative for financial institutions willing to embrace the latest technologies in fraud detection and prevention.
Payment Fraud Detection
From daily e-commerce payments to financial transactions in online marketplaces, detecting unauthorized payments, stolen identities, insufficient funds, or other types of fraud can be achieved more easily with the help of machine learning. Identifying suspicious activity before transactions can proceed is cheaper and more effective than trying to reverse them once they have taken place.
AI and Banking Fraud Detection
Today, most b2b and b2c banking is done digitally online, making this a lucrative field for fraudsters and scammers to operate in. Machine learning software provides the most reliable way to sort through the huge volumes of transactions that take place each second, flagging the potentially sinister and ignoring the trivial ones.
AI and Financial Fraud Detection
Online transaction due diligence, identity identification and personal data protection are critical for all e-commerce vendors, e-wallets, cryptocurrency wallets, and financial institutions who provide financial services online. AI software can be used to reliably automate many of these checks, helping marketplaces run more smoothly and securely.
AI and Tax Evasion Prevention
As anti-money-laundering laws and financial oversight worldwide tighten, tax evasion schemes are becoming more sophisticated and harder to detect by the human eye. On a global level, governments and tax authorities have made significant strides toward combatting tax evasion and fraud in the past few years. Certain jurisdictions have been blacklisted by banks and financial institutions worldwide, making it increasingly more difficult to siphon funds through offshore entities and bank accounts on island nations in the Caribbean.
Those looking to reduce their tax bills, however, have gotten increasingly more creative, exploiting every loophole that has been left open. This makes it difficult for tax inspectors and auditors to detect some of the subtler ways to reduce an organization’s profit – for example – fictional expenses or invoices with inflated amounts. Though these can be thoroughly investigated during a tax audit, this is a very laborious and time-consuming process that cannot be performed en masse – thus missing out on numerous opportunities to detect tax evasion.
Enter machine learning and AI. Thanks to big data and sophisticated algorithms capable of mining and processing it, big accounting firms often charged with government-commissioned audits are now able to automatically flag audit-worthy accounting events and entities for further investigation. For instance, artificial intelligence can be used this way for detecting discrepancies between the volumes of raw goods purchased and those sold on paper. The possibilities are endless, and so far we have only scratched the surface of the fraud prevention potential of artificial intelligence.
Have a fraud-detecting AI application in mind that you’d like to adopt in your organization? Drop us a note so we can advise you on the best option for your industry.