Fraud management: detection and prevention in banking industry
The banking industry all over the world is facing an acute problem of fraud cases detection and prevention. Financial service companies are worthful drives for sophisticated criminals due to the vast quantity of assets under management and several challenges relating to online customer identification. Fraudsters have become experts in hijacking online sessions, thieving client credentials as well as using malware to swindle funds from unaware account holders. In the face of such targeted attacks, banking service institutions are up against the conflicting objectives of offering clients higher convenience without opening a gate to acts of crime.
For faultless fraud detection banking industry institutions are advised to use data analysis software. Why? Because data analysis system allows fraud examiners and auditors analyze an organization's business data. It helps them make sense of how well internal control system is operating and designate transactions that denote fraudulent activity or the elevated risk of fraud. There is a spectrum of analysis that can be applied to detect fraud. It ranges from time point conducted in contextual situation for singular fraud exploration or investigation, through to repeatable analysis of financial processes where fraudulent activity is much more likely to arise.
Eventually, where the risk of fraud is really high and the probability is as well, financial and banking institutions can employ a constant or continual approach to fraud detection — particularly in those situations where preventive controls are not practicable or efficient. The majority of modern financial service companies have increased management requirements for information and nowadays the audit adjustment is moving from the conventional cyclical approach to a risk-based and longstanding model. Technology thereby offers a list of solutions, distinguishing by the sophistication and size of the audit organization. To disclose fraudulent activity, a lot of banks also use special transaction monitoring systems — mostly domestically produced, niche software which demands operator intervention. However, traditional security systems can function well for detecting individual point-of-sale, real-time fraud. But that is only one slice of the fraud pie and not the biggest one, either.
Continuing the topic of high importance of data analysis in fraud management in banking industry it is worth saying that there is a list of analytical techniques used to detect fraud. Here there are the most effective of them:
- Classification — to find patterns among various data elements.
- Statistical parameters calculation (standard deviations, averages, low/high values, etc.) — to detect outliers that could reveal fraud.
- Numbers stratification — to disclose unordinary (redundantly high or low) entries.
- Joining random diverse sources — to denote matching values (such as addresses, names, and account numbers) where they shouldn't exist.
- Duplicate testing — to note duplicate transactions such as claims, payments or finance report items.
- Gap testing — to find out any missing items in serial data where there should be none.
- Entry dates validation — to estimate inappropriate or suspicious times for postings or information entry.
- Numeric values summation — to identify control sums which may have been falsified.
Legitimate online banking system customers who use their own confirmed devices to realize online transactions may still become the victims of fraud. Among some of the schemes used for cheating are session stealing, man-in-the-middle, key-loggers, phishing and many other malware based attacks. In order to prevent the customers from any type of above-described attacks all banking institutions should consider some security measures. First of all, they are to reduce potential for fraudulent wire transfers and other transactions. Secondly, they must do their best to stop machine-resident and web-based attacks from fraudulent transactions in progress. And finally, they shouldn't forget to vindicate online banking clients from session-based transaction attacks.
For more essential prevention of future losses, fraud management programs will have to get self-training and adjustable to a fast-moving environment and advancing fraud techniques. Financial and banking institutions can already smoothly test the efficiency of fraud-screening models and rules — and update them when testing reports point out the need. Ideally, the system automatically captures the investigation outcomes and reuses them in future. Software models thereby readily adapt to brand new knowledge and are continually refined. 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 allows banking institutions better apprehend arising threats so they can open ground to prevent substantial damages before they come into life. 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 which can engage their collective experiences in order to better fraud detection across the whole industry.
Elinext Group projects:
Industries and Technology Areas:
Industries: financial services
Technology Areas: software development, fraud management software
Elinext is a custom software development and consulting company focusing on web, mobile, desktop and embedded software development, QA and testing. Since 1997, we have been bringing digital transformation to mid-sized and large enterprises in Banking and Finance, Insurance, Telecommunications, Healthcare and Retail. Our key domains include enterprise software, e-commerce, BI and Big Data, e-learning and IoT.