Transactions occur on a non-stop basis in the financial services industry. But with tech conveniences comes potentially harmful consequences, fraud being front and centre of those concerns.
Typical banking institutions handle anywhere from a few hundred to a few thousand transactions per second (TPS). Top payment services, like Visa and Mastercard, can process anywhere from 25,000 to 38,000 TPS. Of course, this entirely depends on the day; a typical Tuesday is not the same as Black Friday, for example.
The nature of the industry’s pace makes thwarting fraudulence expensive and time-consuming. For a problem that needs real-time decision making and the ability to predict future outcomes based on past occurrences, business intelligence in finance tools, AI, and specifically, machine learning, can provide a real leg up for financial companies that can implement self-learning technology into their practices.
Below we’ll take a look at how AI and machine learning are being leveraged by financial services companies to combat fraud.
Real-Time Fraud Payment Analysis
Given the TPS range noted above, it’s impossible for any company to monitor their transactions for fraudulence accurately. However, through new machine learning programs, transactions can be analyzed for fraudulence in real-time. Using historical fraud data, and training the machine learning model from an org’s in-memory database. Since fraudulent techniques are constantly changing, the model is updated several times a day and plugged into the live transactional system. Per DZ Zone, this is how financial institutions can keep the quality of their decisions high and false-positive rates low. But perhaps the most significant benefit here is not detection, but prevention. As eliminating the fraudulent incident ups customer satisfaction, and reduces costs stemming from fraud.
In another case, reported by the Financial Times, machine learning was able to detect fraudulent activity by analyzing a user’s behaviour against their historical norm. In the example shared, the fraudster used the scrollbar while the historical data showed that the user preferred the trackpad. A seemingly small detail like this prevented a fraudulent activity from occurring, offering a positive sign going forward for individuals’ security protections.
Detecting Identity Theft
According to the Insurance Information Institute, a record-high 16.7 million people fell victim to identity fraud in 2017, a total up from a previous record in 2016. And like with fraudulent transactions, cybercriminals’ identity theft-stealing methodologies keep evolving to stay one step ahead.
According to TechBeacon, biometric methods like facial or voice recognition carry huge potential, both for their security strength and ease of use for consumers since they don’t have to remember specific access details, like a password or PII. Machine learning algorithms use the basic shapes of a person’s face, like their eyes, mouth, and nose, to make accurate decisions on whether an image matches.
ID authentication at scale is another promising development. Using sensitive information like driver’s licenses and passports, machine learning models can absorb various details of the IDs, such as microprint text, validation of special paper and ink, magnetic strips and other details. These IDs can be scanned and tested either remotely or on-premise, giving the technology positive long-term potential in curbing identity theft.
Preventing Money Laundering, Denial of Service Attacks and Terror Financing
What do money laundering, distributed denial of service attacks (DDOS) and terror financing all have in common? They can be thwarted through social network analysis (SNA).
In simple terms, SNA investigates social structures through networks and graph theory. People are “nodes,” and relationships or interactions that connect them are “ties, edges, or links.” But this doesn’t involve social networks like social media; instead, SNA takes into account various financial information, like credit cards, merchants, banking institutions, known fraudster history—in addition to transactional details such as call behaviour data, IP address, geospatial data, online transactions and banking information. Machine learning algorithms can use these various links to monitor vast amounts of data for suspicious patterns. SNA can also be a helpful standalone fraud prevention measure or a way to reduce false positives generated by other methods.
