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Minimizing financial institutions from the risk of regulatory fines

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February 3, 2016

Earlier today, Hewlett Packard Enterprise (HPE) said it released a new product designed to help banks and financial institutions minimize the risk of regulatory fines.

Dubbed Investigative Analytics, the new software aims to help keep heavily regulated financial companies compliant using machine learning to understand what constitutes risky and non-risky behaviour.

Investigative Analytics, which runs either as a SaaS service or as a managed service on the customer’s own server, will analyze information from different data sources, including email, instant messaging, and financial product trading systems, according to HPE.

The newly created enterprise services company noted that the solution will also analyze voice archives as well.

The innovative system uses machine learning to create a baseline showing normal and so-called clean search patterns in regulated activities such as stock trading, for example.

It can then spot various anomalies or red flags by measing new activity against a set of “key risk indicators” (KRI) already in the system.

These KRIs include such things like the social distance between parties in a trade. You might be suspicious if a trader kept doing the same kind of deal with someone he went to school with, for example.

And of course, there's always the timing of such communications (i.e. “Why is one trader always conducting a particular kind of trade outside office hours?”)

Joe Garber, v.p. of marketing in Information Management & Governance at the company said-- “HPE has had a high degree of success in identifying a multitude of issues which were previously unknown using current communication supervision techniques. Current results show this approach to be up to 200 times more successful in catching true issues.”

And that’s all A-OK, but baselining typically requires a base set of data that you know to be mostly accurate. But what happens if the trading data that a customer uses for baselining is stuffed with several rogue trades and suspicious stock activity such as insider trading?

“Overall, baseline behavior comes from several reviewed sets of data which have been assessed as part of the regulatory compliance workflows which have been in operation for well over ten years and more,” said Garber.

“High confidence 'clean sets' based on historic audit records are used to establish a confident index,” added Garber.

The HPE software is based on other systems in the analytics stable including IDOL, HPE's search and data analytics platform, and Vertica, its analytics and data warehousing service developed in 2015.

Other companies have been getting in on the market for automatically detecting financial anomalies as well. In December 2015, Software AG released the latest version of its Apama Capital Markets Foundation toolset.

Unlike HPE’s offering, Apama is a developer platform that allows financial institutions to build their own enterprise analytical applications and simply folds in the compliance analytics of that financial institution.

It’s no surprise that software vendors and service providers are all clamouring for a piece of this action. The market for financial compliance is ver healthy, especially in light of the global 2008 financial meltdown.

A 2014 Everest Group report on IT outsourcing in capital markets found that 89.2 percent of the deals had a regulatory compliance component, and that about 81.9 percent of them had an analytics element.

Source: Hewlett Packard Enterprise Inc.

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