In my previous blog, we talked about letting the CAT out of the bag in order to make risk management more effective. The 'T' we talked about previously was 'transactions,' the other two being 'customers' and 'accounts.' With the increasing number of channels of monetary transfers - both bank-regulated as well unregulated, anonymous ones, such as Bitcoin - to scan each transaction, especially in a pre-facto scenario like anti-money laundering (where the decision making is required, prior to approving the fund transfer), becomes too daunting a task.
In a post-9/11 world, which brought financial institutions to focus on monitoring transactions in order to curb finances of terrorist organizations, the regulations and the know-how required to put them in place is still inadequate (Ref: American Bankers Association). However, good data sets can help address this. Considering the fact that it is not only a select few states funding such organizations, but also a list that includes legitimate charitable organizations and individuals as well, acting as fronts and providing monetary sustenance to them, the need for intelligent predictive and prescriptive analytics is evident.
The days of relying on plumbing are over. Banks today need intelligent and integrated platforms. A move towards big data and analytics is an obvious start, but the required ingredient here, is good data that is intelligent and that paves the way for the subsequent application of this inherent intelligence.
The technology architecture, and specifically the product suites of the modern world, allow strong and seamless integration capabilities through which, data can be sourced into a landing zone. For example, Hive can provide users limitless capability to slice and dice the data, build analytical dashboards, and develop management reports using sophisticated suites like Tableau and Microstrategy. This can be the foundation for further intelligent analytics. One way to achieve this is by establishing loopbacks at every step in the integrated chain, so that the data is enriched continuously, and made more meaningful.
One user group of such data is the operational front and the other the associated central organizations like FINCEN. The vision is to provide both these user groups with as much intelligent information as possible, in order to improve the decision making, risk scoring, and monetary tracking; by enhancing the rules and scenarios with the loopback mechanism.
All that has been achieved with the continuous efforts of the financial industry around the world needs to be implemented a bit more intelligently. The enemy in discussion here is smart enough to create fronts that look completely legitimate, and runs a dark world covertly and intelligently. The statistics teams that build scenarios to scan transactions, need more enriched, real-time data, with a loopback into the system, so that the scenarios become more foolproof, assign better risk scores, and generate lesser false positives.
The legitimate organizations involved in such transactions could be visualized throughout their relationship lineage with the bank, and could be chopped off, thereby reducing bank losses, as well as involvement, and consequent liabilities (if any).
There is a dire need to build effective and productive data farms, in banks, that can link CAT, across its internal banking relationships and provide the bigger picture for every entity. While the cost of such a system may be high, the rewards will be even higher. Good data will not only drive better business analytics and revenues, but also catch illegitimate fund placements, predict their behavior through pattern analysis, and prescribe a course of action; thus safeguarding itself and humanity at large.