Amongst all industries impacted by fraud, banking is undoubtedly the hardest-hit. While digitization and multi-channel banking have totally transformed the industry, the shift has also brought about an unprecedented rise in fraud.

Fraud threats have been growing both in sophistication and scope, compelling credit unions to determine where and how to deploy resources to shield from possible breaches, even as they manage their daily operational challenges. So what more can credit unions do to protect themselves from fraud?

Given the slackening pace of growth preceded by a spike in quantitative easing and flush liquidity in many countries, severely impacted corporate borrowers’ debts servicing, and consequently, there’s a higher potential for loan defaults. This whitepaper examines critical early warning indicators and scenarios, why multidimensional inputs are crucial to LEWS efficiency and the need for an innovative approach to it.

The bank customer of the day has a new avatar. Easy, instant access to wider information, extensive knowledge about choices available and privileged treatment by organizations in other industries have made them remarkably well-informed and demanding. They do not hesitate to quickly shift to whosoever serves their needs best.

The use of hybrid fraud detection models to ensure high fraud detection rates with low false positives is a vital aspect of enterprise fraud management. A ‘hybrid’ fraud detection model comprises 4 primary techniques are used to accurately risk score every transaction and to advise the right action of whether to approve, decline or challenge the transaction in real-time with sub-second response time.

Risk as a Service with a focus on Enterprise Financial Crime Risk Management (EFCRM) is fast becoming a core component in a bank’s overall risk management framework. EFCRM-centric RaaS addresses a spectrum of areas ranging from transaction monitoring to fraud management to money laundering to CFT (combating the financing of terrorism) programs to cyber-security.

In the world of financial transactions, rule based heuristics are often employed to detect fraud, rather than to detect anomalies. The cat and mouse chase of financial institutions and fraudsters is an ongoing battle that has costed the global economy close to half a trillion dollars. Given the fundamental shortcomings of rule based heuristics, how does machine learning help in fraud detection?

AI-driven regulatory technology goes beyond helping banks improve compliance and cut costs and actually helps lighten the growing regulatory burden.

Brexit will have a far-reaching impact on the UK and Europe across all sectors including the financial sector. This paper takes a quick look at financial fraud in the context of Brexit and the key points which CROs must be aware of.

Synthesize holistic wisdom from core systems and not just from channel silos. From depth of analysis, ease of configuration/ implementation and cross channel fraud detection to insider fraud detection and real-time high availability, a synchronized Enterprise Fraud...

Credit cards have evolved from magnetic strips to chip-and-pin, chip-and-choice, and chip-and-signature cards. EMV is now the de facto global standard for the chip technology embedded in financial payment cards. In the fourth quarter of 2012, there were 1.62 billion...

Banks and financial institutions are seized with newer forms of threats to the safety and security of their data, a critical asset for any organization. In the age of Internet of Things, criminal activities and data theft have also gotten smarter and savvier, with...