Data Is Big, Did You Know?

Data is big. Big news. Big importance.

How big, you ask? Consider that all the information we have as the human race has been growing since the beginning of time. At the same time, we are enacting more processes every day that add to this growing data, whether on a company or personal level; local or global; text or numerical; in native language or foreign and now digital format, with pictures or video.

Let’s put the sheer volumes of data gathering into some perspective. Back in 2003, it was estimated by Turek, IBM, that between the beginning of time up until 2003, 5 exabytes (5 billion gigabytes) of data was created. By 2011, that same 5 exabytes of data was generated every two days. Forbes in 2015 published “20 Mind-Boggling Facts” indicating that more data was created between 2013 and 2015 than in the entire history of the human race and that “by the year 2020, about 1.7 megabytes of information will be created every second for every human being on the planet”, “growing from 4.4 zettabytes” (in 2015) “to 44 zettabytes, or 44 trillion gigabytes”.

What are the reasons for this exponential growth in data?

A key factor is the evolution of computing. In the space of just one hundred years, we’ve evolved from the very first, basic tabulating systems to the cognitive, sensory systems we see in today’s world. Progress has been fierce and rapid. We would argue there has never been a more significant advancement in the development of humans as that of computing. It’s changed the ways in which we interact with one another, the ways we process information, and, crucially, the ways, and the speed, at which we do business.

The explosion of data occurs across all platforms. We no longer communicate just in binary or text – why would we, when there are so many more stimulating options out there, such as multimedia, visuals, sensors and hand-held devices? The ways in which we generate, and consume, data have grown and grown, whilst at the same time peoples’ attention spans have shrunk to that of a goldfish, leading to the introduction of even more mediums of communication and data generation and the need for tools, such as machine learning and artificial intelligence to process the large amounts of data.

The consequences of Big Data 

Companies, consequently, find themselves having to deal with significant amounts of disparate data available from multiple sources, internally and externally; whether it be from clients, employees, or vendors, from internal operations or growth or caused by mergers, acquisitions etc.

All of this requires significant time spent reconciling and processing data and the use of tools (such as business intelligence, knowledge graphs and machine learning, etc.) to analyse it. How do you make sense of all of it? How do you interpret it in a way that allows you to use it to build a more efficient business model? Who can help you with this? Forbes in 2015 estimated that “less than 0.5% of all data is ever analysed and used”, creating a significant business opportunity.

In this six-blog series, we’re going to talk about the challenges that companies face in managing data and the type of tools available for managing the data. We’re going to tell you why it’s important; and we’re going to explain the benefits of getting a proper handle on your data management.

For this, we’re going to draw on Dessa Glasser, Principal at the Financial Risk Group, working with Virtual Clarity on data strategies, for her knowledge of data management strategies and tools, including the use of Data as a Service (DaaS) – to manage and provision data. Dessa, the former CDO of JPMorgan Chase Asset Management and Deputy Director of the Office of Financial Research (US Treasury), has a wealth of experience in implementing solutions in risk, data and analytics across financial and non-financial firms in both the private and public sector, including enacting operational efficiencies and change management via implementing such tools as DaaS.

Dessa Glasser is a Principal with the Financial Risk Group, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

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).

Interested? Visit the website or 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.

Forecasting Capital Calls and Distributions

Early in his career, one of us was responsible for cash flow forecasting and liquidity management at a large multiline insurance company. We gathered extensive historical data on daily concentration bank deposits, withdrawals, and balances and developed an elementary but fairly effective model. Because insurance companies receive premium payments from and pay claims to many thousands of individuals and small companies, we found we could base reasonably accurate forecasts on the quarter of the year, month of the quarter, week of the month, and day of the week, taking holidays into account. This rough-and-ready approach enabled the money market traders to minimize overnight balances, make investment decisions early in the morning, and substantially extend the average maturity of their portfolios. It was an object lesson in the value of proactive cash management.

It is not such a trivial matter for investors in private capital funds to forecast the timing and amount of capital calls and distributions. Yet maintaining adequate liquidity to meet obligations as they arise means accepting either a market risk or an opportunity cost that might be avoided. The market risk comes from holding domestic large-cap stocks that will have to be sold quickly, whatever the prevailing price, when a capital commitment is unexpectedly drawn down; the opportunity cost comes from adopting a defensive posture and holding cash or cash equivalents in excess of the amount needed for ongoing operations, especially when short-term interest rates are very low.

FRG is undertaking a financial modeling project aimed at forecasting capital calls and distributions. Our overall objective is to help investors with outstanding commitments escape the unattractive alternatives of holding excess cash or scrambling to liquidate assets to meet contractual obligations whose timing and amount are uncertain. To that end, we seek to assist in quantifying the risks associated with allocation weights and to understand the probability of future commitments so as to keep the total portfolio invested in line with those weights.

In other words, we want to make proactive cash management possible for private fund investors.

As a first step, we have formulated some questions.

  1. How do we model the timing and amount of capital calls and disbursements? Are there exogenous variables with predictive power?
  2. How do the timing of capital calls and disbursements correlate between funds of different vintages and underlying types (e.g., private equity from venture capital to leveraged buyouts, private credit, and real estate, among others)?
  3. Do private funds’ capital calls and distributions correlate with public companies’ capital issuance and dividend payout decisions?
  4. How do we model the growth of invested capital? What best explains the returns achieved before money is returned to LPs?
  5. What triggers distributions? 
  6. How do we allocate money to private funds keeping an eye on total invested capital vs. asset allocation weights?
    1. The timing of capital calls and distributions is probabilistic (from #1). 
    2. Diversification among funds can produce a smooth invested capital profile.  But we need to know how these funds co-move to create distributions around that profile (from #2).
    3. Confounding problem is the growth of invested capital (from #3).  This growth affects total portfolio value and the asset allocation weights.  If total exposure is constrained, what is the probability of breaching those constraints?

We invite front-line investors in limited partnerships and similar vehicles to join the discussion. We would welcome and appreciate your input on the conceptual questions. Please contact Dominic Pazzula at info@frgrisk.com if you have an interest in this topic.

IFRS 9: Evaluating Changes in Credit Risk

Determining whether an unimpaired asset’s credit risk has meaningfully increased since the asset was initially recognized is one of the most consequential issues banks encounter in complying with IFRS 9. Recall the stakes:

  • The expected credit loss for Stage 1 assets is calculated using the 12-month PD
  • The ECL for Stage 2 assets (defined as assets whose credit risk has significantly increased since they were first recognized on the bank’s books) is calculated using the lifetime PD, just as it is for Stage 3 assets (which are in default).

To make the difference more concrete, consider the following:

  • A bank extends an interest-bearing five-year loan of $1 million to Richmond Tool, a hypothetical Virginia-based tool, die, and mold maker serving the defense industry.
  • At origination, the lender estimates the PD for the next 12 months at 1.5%, the PD for the rest of the loan term at 4%, and the loss that would result from default at $750,000.
  • In a subsequent reporting period, the bank updates those figures to 2.5%, 7.3%, and $675,000, respectively.

If the loan were still considered a Stage 1 asset at the later reporting date, the ECL would be $16,875. But if it is deemed a Stage 2 or Stage 3 asset, then the ECL is $66,150, nearly four times as great.

Judging whether the credit risk underlying those PDs has materially increased is obviously important. But it is also difficult. There is a “rebuttable presumption” that an asset’s credit risk has increased materially when contractual payments are more than 30 days past due. In general, however, the bank cannot rely solely upon past-due information if forward-looking information is to be had, either on a loan-specific or a more general basis, without unwarranted trouble or expense.

The bank need not undertake an exhaustive search for information, but it should certainly take into account pertinent intelligence that is routinely gathered in the ordinary course of business.

For instance, Richmond Tool’s financial statements are readily available. Balance sheets are prepared as of a point in time; income and cash flow statements reflect periods that have already ended. Nonetheless, traditional ratio analysis serves to evaluate the company’s prospects as well as its current capital structure and historical operating results. With sufficient data, models can be built to forecast these ratios over the remaining life of the loan. Richmond Tool’s projected financial position and earning power can then be used to predict stage transitions.

Pertinent external information can also be gathered without undue cost or effort. For example, actual and expected changes in the general level of interest rates, mid-Atlantic unemployment, and defense spending are likely to affect Richmond Tool’s business prospects, and, therefore, the credit risk of the outstanding loan. The same holds true for regulatory and technological developments that affect the company’s operating environment or competitive position.

Finally, the combination of qualitative information and non-statistical quantitative information such as actual financial ratios may be enough to reach a conclusion. Often, however, it is appropriate to apply statistical models and internal credit rating processes, or to base the evaluation on both kinds of information. In addition to designing, populating, and testing mathematical models, FRG can help you integrate the statistical and non-statistical approaches into your IFRS 9 platform.

For more information about FRG’s modeling expertise, please click here.

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.

 

 

 

IFRS 9: Modeling Challenges

Calculating expected credit losses under IFRS 9 is easy. It requires little more than high school algebra to determine the aggregate present value of future cash flows. But it is not easy to ascertain the key components that are used by the basic equation—regardless whether the approach taken is “advanced”  (i.e., where PD, LGD, and EAD are modeled) or ”simplified” (also called “intermediate”). The forward-looking stance mandated by IFRS 9 makes the inherently difficult process of specifying these variables all the more complex.

For the sake of brevity, let’s consider only the advanced approach for this discussion. There are two immediate impacts on PD model estimation: the point-in-time requirements and the length of the forecast horizon.

PD estimates need to reflect point-in-time (PIT) rather than through-the-cycle (TTC) values. What this means is that PDs are expected to represent the current period’s economic conditions instead of some average through an economic cycle. Bank risk managers will have to decide whether they can adapt a CCAR (or other regulatory) model to this purpose, determine a way to convert a TTC PD to a PIT PD, or build an entirely new model.

The length of the forecast horizon has two repercussions. First, one must consider how many models to build for estimating PDs throughout the forecast. For example, it may be determined that a portfolio warrants one model for year 1, a second model for years 2 to 3, and a third model for years 3+. Second, one should consider how far into the forecast horizon to use models. Given the impacts of model risk, along with onus of maintaining multiple models, perhaps PDs for a horizon greater than seven years would be better estimated by drawing a value from some percentile of an empirical distribution.

 

Comparatively speaking, bank risk managers may find it somewhat less difficult to estimate LGDs, especially if collateral values are routinely updated and historical recovery rates for comparable assets are readily available in the internal accounting systems. That said, IFRS 9 requires an accounting LGD, so models will need to be developed to accommodate this, or a process will have to be defined to convert an economic LGD into an accounting one.

Projecting EADs is similarly challenging. Loan amortization schedules generally provide a valid starting point, but unfortunately they are only useful for installment loans. How does one treat a revolving exposure? Can one leverage, and tweak, the same rules used for CCAR? In addition, embedded options have to be taken into account. There’s no avoiding it: estimating EADs calls for advanced financial modeling.

As mentioned above, there are differences between the requirements of IFRS 9 and those of other regulatory requirements (e.g., CCAR). As a result, the models that banks use for stress testing or other regulatory functions cannot be used as-is for IFRS 9 reporting. Bank risk managers will have to decide, then, whether their CCAR models can be adapted with relatively minor modifications. In many cases they may conclude that it makes more sense to develop new models. Then all the protocols and practices of sound model design and implementation come into play.

Of course, it is also important to explain the conceptual basis and present the supporting evidence for PD, LGD, and EAD estimates to senior management—and to have the documentation on hand in case independent auditors or regulatory authorities ask to see it.

In short, given PD, LGD, and EAD, it’s a trivial matter to calculate expected credit losses. But preparing to comply with the IFRS 9 standard is serious business. It’s time to marshal your resources.

IFRS 9: Classifying and Staging Financial Assets

Under IFRS 9, Financial Instruments, banks will have to estimate the present value of expected credit losses in a way that reflects not only past events but also current and prospective economic conditions. Clearly, complying with the 160-page standard will require advanced financial modeling skills. We’ll have much more to say about the modeling challenges in upcoming posts. For now, let’s consider the issues involved in classifying financial assets and liabilities.

The standard introduces a principles-based classification scheme that will require banks to look at financial instruments in a new way. Derivative assets are classified as “fair value through profit and loss” (FVTPL), but other financial assets have to be sorted according to their individual contractual cash flow characteristics and the business model under which they are held. Figure 1 summarizes the classification process for debt instruments. There are similar decisions to be made for equities.

The initial classification of financial liabilities is, if anything, more important because they cannot be reclassified. Figure 2 summarizes the simplest case.

That’s only the first step. Once all the bank’s financial assets have been classified they have to be sorted into stages reflecting their exposure to credit loss:

  • Stage 1 assets are performing
  • Stage 2 assets are underperforming (that is, there has been a significant increase in their credit risk since the time they were originally recognized)
  • Stage 3 assets are non-performing and therefore impaired

These crucial determinations have direct consequences for the period over which expected credit losses are estimated and the way in which effective interest is calculated. Mistakes in staging can have a very substantial impact on the bank’s credit loss provisions.

In addition to the professional judgment that any principles-based regulation or accounting standard demands, preparing data for the measurement of expected credit losses requires creating and maintaining both business rules and data transformation rules that may be unique for each portfolio or product. A moderately complex organization might have to manage hundreds of rules and data pertaining to thousands of financial instruments. Banks will need systems that make it easy to update the rules (and debug the updates); track data lineage; and extract both the rules and the data for regulators and auditors.

IFRS 9 is effective for annual periods beginning on or after January 2018. That’s only about 18 months from now. It’s time to get ready.

IFRS 9 Figure 1

IFRS 9 Figure 2

 

 

 

Risk Premia Portfolio Case Study

See how FRG’s VOR (Visualization of Risk) platform works for a major U.S. foundation: download a case study that explores how we customized VOR application tools to help them with their day-to-day portfolio management activities, as well as their monthly analysis and performance reporting.

The study shows how FRG was able to leverage its econometric expertise, system development capability and logistical strength to empower the foundation’s specialized investment team. Read the study, and learn more about VOR, here.

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