Real Time Learning: A Better Approach to Trader Surveillance

An often-heard question in any discussion of Machine Learning (ML) tools is maybe most obvious one: “So, how can we use them?”

The answer depends on the industry, but we think there are especially useful (and interesting) applications for the financial services sector. These consumers have historically been open to the ML concept but haven’t been quick to jump on some potential solutions to common problems.

Let’s look at risk management at the trading desk, for example. If you want to mitigate risk, you need to be able to identify it in advance—say, to insure your traders aren’t conducting out-of-market transactions or placing fictitious orders. The latest issue of the New Machinist Journal by Dr. Jimmie Lenz (available by clicking here) explains how. Trade Desk Surveillance is just one way that Machine Learning tools can help monitor a variety of activities that can cause grief for those tasked with risk management.

Would you like to read more about the possibilities ML can bring to financial services process settings? Download “Real Time Learning: A Better Approach to Trader Surveillance,” along with other issues of the New Machinist Journal, by visiting www.frgrisk.com/resources.

Introducing the New Machinist Journal

Who are the new machinists, and what are their tools?

The machinists of the 21st century are working with Artificial Intelligence (AI) and Machine Learning (ML), turning what has been science fiction into science fact. From learning algorithms that nudge us to buy more stuff to self-driving vehicles that “learn” the highways and byways to deliver us to our destinations safely, AI and ML are attracting considerable attention from a variety of industries.

FRG is currently researching and building machine learning proof-of-concepts to fully understand their practical applications. A new series, the New Machinist Journal, will explore in detail some of these applications in different environments and use cases. It will be published regularly on the FRG website. Volume 1, “What Artificial Intelligence and Machine Learning Solutions Offer,” is an overview of the subject, and is now available for download (click here to read it).

For more information, contact the FRG Research Institute, Research@frgrisk.com

Quantifying the Value of Electricity Storage

ABSTRACT: This research discusses the methodology developed to hedge volatility or identify opportunities resulting from what is normally a discussion constrained to the capital markets.  However, the demand (and the associated volatility) for electricity in the United States has never been more pronounced.  The upcoming paper, “Quantifying the Value of Electricity Storage,” will examine the factors that have led to the growth of volatility, both realized and potential.

There is widespread recognition of the value of energy storage, and new technologies promise to expand this capability for those who understand the opportunities being presented to firms involved in different areas of electricity generation. Objective tools to valuate these options, though, have been limited, as has the insight into when mitigation efforts make economic sense.

In order to answer these questions for electricity generators of all types we have created an economics-based model to address the initial acquisition of storage capacity, as well as the deployment optimization solutions, based on the unique attributes of the population served.

Links to the paper will be posted on FRG’s social media channels.

Turning a Blind Eye to the Risky Business of Incentive-based Sales Practices 

Should you be monitoring your sales activities to detect anomalous behaviors?

The use of sales incentives (commissions, bonuses, etc.) to motivate the behavior of salespeople has a long history in the United States.  We all hope to assume the initial structuring of incentive-based pay is not intended to have nefarious or abusive impacts on its customers but, in a number of recent and well-publicized stories of mistreatment of both customers and customer information, we have discovered that these negative consequences do exist.  Likely, the business practice of turning an administrative blind eye to the damage done to consumers as a result of these sales incentive programs has played an even greater role in the scale of abuse that has been uncovered over the last decade.  In the most recent cases of unchecked and large-scale customer abuse, with particular attention focused on the financial services industry, this business paradigm of tying employee benefits (defined as broadly tying employment and/or income potential to sales) were resolved through arbitration and frequently typecast as “a cost of doing business”.

Today, are you putting your business, and all those associated with its success at risk by turning a blind eye to the effects of your business practices, including your employee incentive programs?  There are new consequences being laid on to corporate leaders and board members for all business practices used by the company, and the defense of not knowing the intricacies and results of these practices does not protect you from these risks.

We have developed a methodology to detect both customer sales and individual product behaviors that are indicative of problematic situations that require additional examination.  Our methodology goes beyond the aggregate sales, which are primarily discussed in the literature, to highlight individuals and/or groups that are often obviated when analyzing such data.

A forthcoming  paper, “Sales Practices: Monitoring Sales Activity for Anomalous Behaviors” will explore these issues, and a resolution, in depth. Visit any of our social media channels for the link.

 

 

 

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