In this article, Solution Architect Virendrasinh Gohil and Business Integration Architect Paul Lashmet explore two types of distributed technologies and the fit-for-purpose of each to external and internal facing business scenarios of custody services in the financial services industry.

The automated nature of algo trading can improve business outcomes but magnify risks. If risk stewards do not have the right tools to effectively oversee a business that is running faster and faster, operational losses, regulatory fines, and oft-occurring reputational risk could increase. That's why it is important to look to risk stewards, to ensure opportunities are not missed.

Client: Cloudera. Published on September 19, 2019 on Medium

Criminal networks are pervasive in their determination to identify and exploit business vulnerabilities. Their ability to constantly evolve means new approaches are required to disrupt the cycle of financial crime.

This story is the second in a series and describes how the financial services industry has evolved its approach to combating financial/economic crime as outlined in my introductory story: “The Pervasiveness of Financial Crime”. In the third and final story in this series, I will describe how we can execute proactive solutions by moving towards the next generation financial crimes platform.

Client: Cloudera. Published on September 12, 2019 on Medium

Financial Crime is both pervasive and rapidly-evolving. Surges in human trafficking, synthetic identity fraud and crime at the edge of the banking network require new approaches to financial crime which work beyond the neat boundary lines of business functions. This story is the first of three about moving towards the next generation of financial crimes platform.

Client: Hazelcast

We were happy to have participated in the writing and research for this white paper about the global #payments landscape, current challenges, and solutions provided by #inMemory technology.

Clients: Fusion Risk Management and Salesforce (Lightning Platform).

First Published on TabbForum, September 25, 2019

By leveraging automated workflows, collaboration, and artificial intelligence via a modern operational risk platform, firms can transition from a reactive program to a proactive one of prediction and prevention. But enterprise risk is full of organizational silos that respond to disparate business functions and technology teams operating throughout the organization. The next step is to scale up by connecting internal and external risk functions into a unified enterprise-wide risk management program.

Clients: Squirro and Salesforce (Lightning Platform).

First Published on TabbForum, March 01, 2019

On December 12, 2018 and then again on January 16, 2019, we convened two groups of senior executives from some of the world’s leading financial services institutions to explore how we would integrate machine learning methods directly into business processes. In these sessions we used the AI capabilities of Squirro and Salesforce Einstein to demonstrate.

Clients: Arcadia Data.

First Published on Information Management, May 23 2018.

To demonstrate robust and defensible processes, Data Privacy Officers (DPOs) must proactively master the following data relationships for the General Data Protection Regulation (GDPR) : 1) Data Processors; 2) Customers; and 3) Employees.

Client: Arcadia Data.

This post was co-written with John Thuma.

Banking surveillance is often fragmented in which distinct surveillance teams monitor customer interactions, electronic communications, market activity, voice recordings, building sensors, video feeds, and social media within silos. This limits the ability to spot sophisticated risk activities that cross multiple locations, lines of business, and functional areas.

Client: Arcadia Data.

Asset management firms increasingly leverage alternative data to enhance their investment strategies and gain an informational edge over the rest of the market. It is becoming the normal course of business to use new types of data in alternative ways and soon “alternative data” will simply be referred to as “data.” The ability for one firm to distinguish themselves from another depends on why they selected certain data sets over others and how they enable their subject matter experts to put alternative data to use.

In our previous post, based on the report, “Put Alternative Data to Use,” by Greenwich Associates and commissioned by Arcadia Data, we considered the types of alternative data used and desired by the buy-side community, how they measure return on investment, and how deeply they analyze it.

In this post, we expand on that research to understand the approaches used by hedge funds and asset managers to select data sources and how their people use it. Finally, we respond to reported roadblocks by describing the keys to a successful alternative data strategy.

Client: Arcadia Data.

The primary purpose of the Consolidated Audit Trail (CAT), a rule under the Securities and Exchange Act, is to arm regulators with the data they need to effectively conduct market surveillance and investigations into suspicious trading activities across all national exchanges. The difference between this and current trade reporting regimes is that it covers more than just trade data. It also requires information about the client and how the trades are broken up and allocated to their accounts which will enable regulators to understand the full life cycle of a trade and with whom it has been transacted. If your organization lacks a comprehensive and current view of all of your data and your internal surveillance processes are fragmented, you run the risk of external parties (the regulators) knowing more about your business than you do.

Client: Arcadia Data.

DFAST and CCAR require bank holding companies to report, in detail, how they would respond to hypothetical market scenarios that represent macroeconomic shocks like a housing meltdown or a stock market crash. The data used by each company to predict losses and create a response plan must be actual data, not approximated. Using the commercial real estate market as an example, this post explains how a location-based BI on Hadoop solution provides analysts and portfolio managers with a flexible way to use microeconomic real estate market factors as the foundation to a robust and actionable response to regulators.

Client: Arcadia Data.

An alternative data strategy is a collaborative, iterative, and exploratory process that is driven by domain subject matter expertise. That is our take from the research survey that we commissioned from Greenwich Associates to understand how buy-side portfolio managers, chief investment officers, and fund managers “Put Alternative Data to Use.” Our primary focus was to discover the “how” and “why” of implementing an alternative data strategy, because that is the basis from which to determine which tools and processes would work the best, and to identify the personas who would use them. Even within the buy-side community, we found that asset managers and hedge fund managers are distinct personas, and have differing perspectives on the use of alternative data.

In this part one of a two-part series, we consider buy-side perspectives as they relate to the types of alternative data being used, the measurement of return on investment, and how deep they want to get into the data.

Clients: Arcadia Data and Pitney Bowes

This post was co-written with JJ Medina, Managing Director ~ Enterprise Software Solutions for Financial Services at Pitney Bowes.

Commercial Real Estate (CRE) is at a risky inflection point. The rate of charge-offs (when the value of loans are removed from books and charged against a loss reserve) are essentially at zero while delinquency rates are also at a historic low (see charts below). Low charge off and low delinquency rates along with increased competition for new loan business are tempting banks to relax CRE lending standards and give out risky loans. This is a major cause for headache among regulators as the CRE market continues to overheat and property values surpass pre-bubble levels.

Co-written with Peter Memon, Managing Director – Global Head of Emerging Technology at Synechron. This article was originally posted on Synechron

Solutions to complex analytical problems often rely on the craftsmanship of highly-specialized experts in data science, machine learning, and other branches of artificial intelligence.  The extent to which advanced analytics creates business value today is still limited (especially for businesses like financial services) because of a few key reasons, all of which also drive up the cost of monetizing a company’s data in the end. 

This post was co-written with Richard Harmon Ph.D., Director, EMEA Financial Services at Cloudera.

A growing number of asset management firms have been integrating alternative data sources with conventional market data, enriching trading strategies with additional insights as a means to gain an informational advantage and an edge on the rest of the market. But the informational advantage that can be gained through the use of alternative data will diminish as those sources become generally understood and adopted. To avert this alpha decay, it is critical to understand how alternative data will contribute to your strategy and make certain that it is applied for the right reasons.

Active management is fundamentally about uncovering opportunities before they are priced in by the broader market. Over the past 5 years a growing number of asset management firms have been integrating alternative data sources with conventional market data, enriching trading strategies with additional insights as a means to gain an informational advantage and make more informed investment decisions in order to get an edge on the rest of the market.

The informational advantage that can be gained through the use of alternative data will diminish as those sources become generally understood and adopted. In other words, the alpha decays at an accelerated rate as more firms process and analyze similar alternative data sources. But if your competitor’s implementation is poor, no ground is lost.

To avert alpha decay, it is critical to understand how alternative data will contribute to your strategy and make certain that it is applied for the right reasons (fit for purpose). It is also critical to ensure the integrity of the underlying data and to optimize the models that use it, thus accelerating an informed decision-making process.

This post was first published on TabbForum.

The alternative data economy provides an opportunity for organizations to create new revenue streams by repurposing their data assets for the financial services industry. This post is geared to organizations that are evaluating their place in the alternative data ecosystem as originators of data. It describes how to position data assets in a way that enables prospective users to access it, assess its quality and put it to use, creating new value.

This post was first published on the Arcadia Data website.

Alternative data for financial services, such as satellite imagery, logistics data, and social media feeds, has been getting a lot of attention recently. Like any trending topic, its uses and benefits can be hyped up a bit but if the right plumbing and creativity is in place, those benefits can be realized. This post helps to understand the plumbing by describing the component parts of the alternative data ecosystem. This framework will help you to navigate your way from an inkling of an idea to an actionable strategy while the window of opportunity is still open. See the webinar, “Put Alternative Data to Use in Capital Markets” for a demonstration of this process.

This post was first published on Arcadia Data’s website.