Caroline Hermon is Head of Fraud Prevention Solution Sales. She has a particular focus on exploring best practices to help organisations protect revenue by increasing their effectiveness in use of data, and ensure that they get fast, accurate analytical results. She is keen to ensure that her customers can manage their fraud prevention process and protect their customers and employees so that analytics delivers rapid value. Away from work yoga, swimming and travel keep me out of trouble!
Recent money-laundering scandals have shaken public trust in the banking sector. How can banks rethink their approach to AML? The BBC’s recent Panorama documentary, “Banking Secrets of the Rich and Powerful,” is an uncomfortable watch for anyone working in the banking sector. While all banks have anti-money laundering (AML) teams
Why fraud prevention and anti-money laundering teams can learn a lot from each other—and how convergence can benefit both.
Cybercriminals are licking their lips at the prospect of businesses moving more of their services online. Machine learning can help your company keep the fraudsters at bay.
Companies face a stark choice between optimising customer experience and minimising fraud. But what if they could do both?
Banks see open source as a hotbed of innovation – and a governance nightmare. Do the rewards outweigh the risks? Open source software used to be treated almost as a joke in the financial services sector. If you wanted to build a new system, you bought tried and tested, enterprise-grade
Analytics can help today’s leaders transform banking for the digital age without compromising on efficiency It’s no secret that challenger banks and fintech companies are changing the banking industry as we know it. The main issue for today’s banking leaders is not that customers are switching to new high-street providers,
We like to think of analytics as a logical discipline, where investment decisions are consistently rational and predictable. Not so. Customer organisations are driven by all the usual complex and sometimes contradictory forces. Business users want analytics to help them make good customer facing decisions as quickly as possible, while
I’ve had several meetings lately on data management, and especially integration, where the ability to explore alternatives has been critical. And the findings from our internet of things (IoT) early adopters survey confirms that the ecosystem nature of data sources in IoT deployments means we need to expand the traditional
Data monetization, at its simplest, is the process of turning data into bottom-line value for a company -- often through improving efficiency and/or customer experience, and building customer loyalty as a result. This may sound simple, but in practice, it’s anything but. Good data, advanced analytics and real-time decision making
While the growth of big data is an issue that preoccupies both the public and private sector, it’s also high on the UK government’s legislative agenda. All eyes are currently on the authorities, as we wait with bated breath to see what’s next in the quest to manage data escalation.
Sizing is a topic that solutions managers typically leave until the end after decisions about the application have been settled. But there are often many variables that can impact the final size requirement. We have seen across our customer base that sizing and the number of environments has been determined
