In my first article, I looked at the main areas covered by the 4th Anti-Money Laundering (AML) Directive from the EU. This post covers the amendments to that directive introduced by the 5th AML Directive, and suggests how organizations can address the requirements of the two directives. The 5th AML Directive:
From national security agencies, law enforcement organizations looking to terrorism and criminal activities, internal security, audit and compliance departments, to hospitals and public health organizations guarding against disease outbreaks, there are many common needs and constant challenges, e.g.: Detect an event of interest in the early stages. Investigate suspicious events
This is the seventh and last part of the blog post series “A practical guide to tackle auto insurance fraud”. In the first six articles of the series we drilled down to: Data Management and Data Quality as the basis for fraud detection analytics. Business Rules and Watch lists techniques
This is the sixth of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first five articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists
This is the fifth of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first four articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists
In the first three articles of the series we drilled down to Data Management and Data Quality as the basis for fraud detection analytics, to Business Rules and Watch lists techniques that play always a crucial role for claim handlers and fraud investigators and to Advanced Analytics which add a
This is the third of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. In the first two articles of the series we drilled down to Data Management and Data Quality as the basis for insurance fraud detection analytics and also to the Business
This is the second of the seven parts of blog post series “A practical guide to tackle auto insurance fraud”. While Data Management and Data Quality are the basis for every analytical journey, and this becomes even more true for fraud detection analytics, the domain knowledge and business expertise plaid
Welcome to the 1st practical step for tackling auto insurance fraud with analytics. It is obvious why our first stop relates with data, the idiom “the devil is in the details” can easily be applied in the insurance fraud sector as “the devil is in the data”. This article analyses
I am more than glad to invite you to join me in a series of posts related to a practical guide for tackling auto insurance fraud in the new era of data science and advanced analytics. Insurers are used to face a constant threat, a powerful enemy that never rests.
The technology breakthrough during the last years have brought an increase in insurance fraud and, as a consequence, they are changing the landscape in the sector. From Pricing Comparison Websites (aggregators), to Telematics and Usage Based Insurance, to Internet of Things, the increasing demand for Cyber Insurance and new Peer
All global researches and surveys show that all kind of fraud, like procurement fraud, is rising and empowered by new dimensions in matter of vulnerabilities and attack methods. From traditional check tampering and skimming methods to cyber crime attacks, fraudsters become more sophisticated, with a powerful arsenal of technology in
Lately there seems to be a surge in the term machine learning. Much like big data a few years ago, machine learning is the new buzzword -- and the two terms actually go hand in hand. With increasing volumes of data now stored in distributed environments such as Hadoop, it's
