In the midst of preparing for a presentation last week, I entered the term “financial crimes” into my internet search engine. I’ve probably done this same search a hundred times, but seemingly never took notice of the staggering number of results. Over two million of them!
Among those results are a stunning number of definitions, news reports, and general articles. But with so many links to seemingly unconnected terms such as check fraud, credit card fraud, medical fraud, insider trading, bank fraud, health care fraud, tax evasion, bribery, identity theft, counterfeiting, and money laundering – it must appear to the uninitiated that an understanding of ‘financial crimes’ requires an Einstein-like intelligence pedigree.
To those involved in the daily prevention / detection / and investigation of financial crimes, however, the term can be effectively boiled down to:
1) Intentional deception made for personal gain, and
2) The illegal process of concealing the source of those gains.
Everything else – all that other noise – simply falls underneath that definition, and only a cohesive combination of human intelligence and technology can take a bite out of those crimes.
Of course, most companies that are targets of these crimes invest heavily in different forms of technology for enterprise fraud management and anti-money laundering systems. There are dozens of vendors in this market with varying levels of functionality and service offerings.
The problem with too many of those offerings, however, is that they do not account for organizational truths such as functional (and data) silos, data quality issues, changing criminal tactics, human limitations, and big data.
A complete enterprise solution for financial crimes management must include automated processes for:
Customer Onboarding – Knowing the customer is the first step an organization can take to prevent financial crimes. A holistic view of an entity – customers, partners, employees – provides a very clear view of what is already known about the entity including their past interactions and relationships with other entities.
Flexible Rules-Based Alert Detection – A robust rules-based alert detection process must provide out-of-box functionality for the types of crimes outlined at the beginning of this article. At the same time, it should be flexible enough for an organization to modify or create rules as criminal activities evolve.
Predictive Analytics – Expected by analysts to become a 5.25B industry by 2018, predictive analytics ensures that big data is scrutinized and correlated with present and past historical trends. Predictive analytics utilizes a variety of statistics and modeling techniques and also uses machine information, data mining, and Business Intelligence (BI) tools to make predictions about the future behaviors including risk and fraud.
Social Network Analysis – Also known as Fraud Network Analysis, this emerging technology helps organizations detect and prevent fraud by going beyond rules and predictive analytics to analyze all related activities and relationships within a network. Knowing about shared telephone numbers, addresses or employment histories allows companies to effectively ‘cluster’ groups of suspected financial crime perpetrators. The key here, however, is context. Many technologies can build these networks and clusters for review, but precious few can provide the key “what does this mean” element that business users require.
Investigation Management and Adjudication – Incorporating key elements of enterprise case management, collaboration, link visualization, information dissemination and knowledge discovery, this layer of functionality is designed to uncover insights which aid in investigating complex incidents. The result ought to be actionable visualization of critical entities, and documented results for potential litigation and regulatory compliance.
Anti-Money Laundering (AML) and Regulatory Compliance – With record fines being assessed to financial institutions globally, AML compliance is very clearly a major requirement within a financial crimes management solution. The oversight requirements grow almost daily, but at a minimum include out of box functionality for suspicious activity monitoring, regulatory reporting, watch list filtering, customer due diligence, Currency Transaction Report (CTR) processing, and the Foreign Account Tax Compliance Act (FATCA) compliance.
Now, there are clearly many more dynamics than can be summarized here but hopefully the point is made. The only way that organizations can continue to drive fraud and money laundering out is via a happy marriage between skilled financial crimes professionals and the flexible/adaptable technology that empowers them.
Doug Wood, Xanalys Ltd