Fraud exists in all forms. Not just linked to payments in banking and financial services. In fact, there is a whole lot like identity theft, insurance fraud, content abuse, fake user accounts, to name a few. Materializing in all of these, fraud simply is ever expanding, taking in new forms and growing more deceptive day by day to bypass the conventional regulatory mechanisms put in place.
Fraud may very well go unnoticed for a long time. Potential moles both inside or outside an organization can employ such practices covertly. When a company experiences losses other than what is analyzed or quantified into reports, they become aware of fraud actually taking place either financially or by some other ways.
Measures to identify and resolve incidences of fraud turn out to be largely ineffective, if they are dealt at a later stage as the damage may get irreversible with significant losses. Due to that, the immediate liability of organizations is to detect fraud the moment it occurs and flag it. As of today, there is no other solution more apt and proactive than AI to deal with fraud and eliminate it altogether.
Global Fraud is Escalating, So are Advancements in AI
Every year, companies post their net losses on account of fraud, which exceeds billions.
Reeling under financial fraud and threats to cybersecurity, the global economy has lost close to $600 billion or 0.8 percent of global GDP, according to a joint report by McAfee and the Center for Strategic and International Studies (CSIS).
Organizations in the finances industry, retail, insurance, healthcare, manufacturing etc. have long been susceptible and crippled by fraud. Banking and payment services are the worst hit, as they fell victims to a range of fraudulent schemes like credit card misuse, identity theft, tampering of checks and illicit money laundering.
Globally, transactions have quadrupled, so is the rise in fraud. Meanwhile, fraudsters continue employing novel tactics to bypass defensive measures for targeting institutions and their customers with malicious intent. The Association of Certified Fraud Examiners in its 2018 Report to the Nations estimates more than $7.1 billion in total losses owing to occupational fraud and abuse.
Fraudulent practices continue unabated even in this digital age. Accenture states that digital convergence has led to a simultaneous rise of ‘digital fraud’ that affects card payments. With the surge in global transactions and vertical growth in technology, so is the rise of financial fraud and cybercrime.
Fraudsters compromise data through phishing and impersonation scams. Organizations have deployed countermeasures to thwart such illegal malpractices and detect fraud early on, which however turned out to be only partially successful. With fraudsters employing novel techniques to break in, there is a dire need for real-time fraud detection, which is where AI comes to the rescue.
Advancements in AI brought algorithms that more powerful with increased capabilities. As the need for safe and secure transactions grow, financial institutions around the world are looking to AI. In fact, McKinsey identifies financial services as high adopters of AI. In finance, AI has found breakthrough roles in areas such as fraud detection and risk assessment.
The Case of Mastercard and HSBC : How AI helped them Counteract Fraud
AI adoption is swiftly penetrating into the financial industry. One could assume the reason why. Banking and financial services have been the most vulnerable to fraud. They have suffered huge losses, which considerably affected their reliability and loyalty across millions of customers that rely on their services.
Mastercard, the multinational financial services company turned to AI technology to help address the pressing issue of financial fraud. They deployed their own AI solution known as AI Express and Decision Intelligence to help themselves and other companies combat and manage fraud, offer credit risk protection, facilitate genuine transactions, reduce false declines as well as aid in their anti-money laundering efforts.
“If data is the oil that powers the digital economy, artificial intelligence is the refinery. Mastercard has gained significant experience in the application of AI at scale in a mission critical environment. For companies looking to take advantage of the technology today, AI Express offers quick results along with the know-how to move forward with a full-fledged artificial intelligence deployment.”
Ajay Bhalla, chief security solutions officer at Mastercard
HSBC, Europe’s largest banking and financial services institution partnered with the AI startupAyasdi to deal with the rising fraud and money laundering activities.
Andy Maguire, Chief Operating Officer of HSBC states, “Anti-money laundering checks is a thing that the whole industry has thrown a lot of bodies at because that was the way it was being done. However, AI technology can help with compliance because it has the ability to do things human beings are not typically good at like high frequency high volume data problems.”
AI Brings in Effective Fraud Detection and Reduction of False Declines
Transactional and customer data amounts to huge volumes. Sifting through them can reveal insights and patterns that indicate whether any of the transactions are conducted genuinely. Manually assessing through these huge troves of transactional data is time-consuming, error-prone and costly. AI being entirely data-driven can plunge into both transactional and customer data to analyze scores of customer behavior to detect any suspicious activity indicating fraud.
Anti-fraud AI systems are not limited to a set of parameters but continually learns from the huge troves of ongoing and past transaction data of customers. This is the case with conventional fraud detection tools, which flags transactions as false positive or negative if the transaction does not adhere to the set parameters. AI-based fraud detection tools, however, work differently by comparing the records of transactions with that of other users belonging to the same category to find similarities and flag for fraud.
Another way AI works in detecting fraud is by simulating situations in which fraud or other malpractices may occur. It conceives a lot of possible scenarios based on customer transaction data and other inputs to discover how fraudsters attempt to make illicit transactions. If the AI system detects fraud, it then puts into effect standard procedures to double check such as by requesting additional verification from the user or stop the transaction completely.
Deploying such AI solutions have enabled banks and financial service institutions to bring down cases of financial fraud and money laundering to a large extent. As we have pinpointed the case of Mastercard, their innovative AI solution have protected its customers from fraud as well as prevent the rate of false declines. AI fraud detection tools employed by Mastercard have successfully enabled the firm to effectively shield its customers’ vital information from getting into the hands of fraudsters.
Real-time Fraud Detection in AI Goes a Long Way
AI is fraud detection in real-time from ongoing transactions and multifarious fund flows. Real-time fraud detection does away with many attempts at manipulating or stealing information at customer touch points thereby thwarting such attempts effectively. AI systems can analyze flows of transactions in real-time and pinpoint any attempts of fraud, while also preventing false positives and false negatives in transactions.
Besides, it relies on machine learning, yet another subset of AI to continuously train models to look for patterns in customer behavior when initiating transactions or from past history. Data scientists can train the ML systems to closely monitor payments done on a large scale and identify any abnormalities that signal fraud. In areas of risk modeling, the combination of AI and ML works in tandem to dynamically adapt to new threats and work on stopping them effectively.
Past transactional data prove to be of much assistance in AI fraud detection systems since it can train them appropriately With the proliferation of customer data, AI and ML systems can quickly sift through customer transaction records to build a repository of behavioral patterns that help in the early detection of fraud or other financial malpractices.
Looking to the Future of AI-based Fraud Detection
AI is evolving at a breakneck speed. Today, we have algorithms that can make real-time split-second decisions that proves instrumental in fields from healthcare to self-driving cars. And, tomorrow, AI will simulate the cognitive abilities of the brain to bring about better decision making and faster processing of information. Together, all these new developments in AI will have a measurable impact by enhancing the processes and operations of the financial industry.
New developments in AI will result in faster, smarter and more powerful algorithms capable of analyzing the spending data of a large number of customers in less time. The algorithms can continually assess how a customer carries out their transactions through a credit card or some other means. This data can then be used to ascertain whether the transaction is genuine or if somebody else is using the card to transfer or withdraw money.
Developments in AI like neural networks have paved the way for more accurate and quick detection of fraud and financial crime. As banking is becoming more diverse so is the number of customers, which ultimately results in the generation of massive amounts of customer data. Fortunately, these new algorithms can handle such enormous data in record time and detect any anomalies for early prevention.
Employing AI based fraud detection systems can help banks and financial institutions not only eliminate fraud but decrease their costs and augment their performance. Besides, by deploying such measures, the customers are always protected and their transactions made ultra secure, which will ensure their loyalty towards the company throughout.
As we see it, AI will radically transform the financial industry by stopping financial crimes, improve transaction speed and security, which leads to a satisfied customer base and better reliability of the company.
I have been programming since 2000, and professionally since 2007. I currently lead the Open Source team at Fingent as we work on different technology stacks, ranging from the “boring”(read tried and trusted) to the bleeding edge. I like building, tinkering with and breaking things, not necessarily in that order.
Hit me up at: https://www.linkedin.com/in/futuregeek/