Nowadays, the banking industry is facing an acute problem of fraud. The problem is global, and no country is fully protected. Fraudsters have become experts in hijacking online sessions: they steal client credentials and use malware to swindle funds from unaware account holders. In his book “Future Crimes” Marc Goodman explains that “criminals are often the first to exploit emergent technologies and turn their complexity against their users”. According to Financial Fraud UK report, in 2016 financial fraud losses across payment cards, remote banking and cheques resulted in astonishing £768.8 million, an increase of 2% compared to 2015. At the same time, prevented fraud totaled £1.38 billion in 2016. The anti-fraud measures undertaken by the banks and card companies helped to save up to £6.40 in every £10 of attempted fraud transaction.
Data Analysis Software
One of these options is the use of data analysis software which, in most cases, guarantees an impeccable fraud detection. Modern systems allow fraud examiners to analyze business data and check how well the internal control system is operating. As the result, they can designate transactions that denote fraudulent activity or the elevated risk of fraud. There is a spectrum of analysis measures that can be applied to tackle fraud. It ranges from contextual situations for a singular fraud investigation to a repeatable analysis of financial processes susceptible to criminal activity in the first place.
If the risk of fraud is really high, financial and banking institutions can employ a constant or continual approach to fraud detection. It works particularly well in situations where preventive controls are not practicable or efficient. The majority of modern financial service companies have increased management requirements for information as the audit adjustment is moving from the conventional cyclical approach to a risk-based and longstanding model. To disclose fraudulent activity, a lot of banks use special transaction monitoring systems. By and large, they represent domestically produced software which demands an operator intervention. However, traditional security systems can function well for detecting individual point-of-sale, real-time fraud. But that is only the tip of an iceberg.
There is a list of analytical techniques used to detect fraud. The most effective among them are:
- session stealing
- man-in-the-middle
- key-loggers
- phishing
In order to avoid any type of such attacks, banking institutions are advised to undertake a number of security measures:
- they must do their best to stop machine-resident and web-based attacks from fraudulent transactions in progress.
- they shouldn’t forget to vindicate online banking clients from session-based transaction attacks.
Our advice here is to smoothly test the efficiency of fraud-screening models and rules and update them when testing reports point out the need. Ideally, the system automatically stores the investigation outcome for further use in future. Software models, which are continually refined readily adapt to brand new knowledge. Auto-generated network graphs allow strategists catch symptoms and patterns which lead to reformed controls and new monitoring practical methods. This mixture of visibility and adaptation prevents both arising and future threats. The perspectives for the future also go beyond the scope of any single company. As more companies choose automated and integrated fraud management systems, the potential is here to make up a vast consortium of banking institutions sharing their collective experiences in order to get better fraud detection percentage.
AI Technology and Fraud Prevention
It’s fair to say that AI has become quite a buzzword in various fields of business. The financial services industry is no exception. Originally introduced in the 1950s, AI has gained a new wave of popularity just recently due to the variety of reasons. One of them is, obviously, the adoption of new standards in security.
Such systems are trained to recognize potential fraud through supervised training, when the variety of random samples is manually classified as genuine or fraudulent. Subsequently, the algorithm learns from these manual classifications to determine the legitimacy of future activities on its own.
Within several years, the strategic use of AI and machine learning will become an integral part of banking organizations’ security principles. AI can save banks considerable money by eliminating complex fraud cases and protecting their brand. Here at Elinext, we offer a variety of software development services to achieve success in such a market environment. With nearly 300 professionals, we approach challenges from every angle to quickly grasp the data, workflows, compliance requirements, and math behind securities, trading and investments. Contact us at [email protected].
