Improving Business Email Etiquette

This is the second post in an occasional series about the importance of technical communication in the workplace.

According to The Radicati Group, Inc., based on a worldwide study in 2015, the number of business emails sent and received per user, per day totals 122, with a circulation of 112.5 billion worldwide. These statistics should reflect how much businesses rely on email communication skills on a daily basis. Because of the massive influx of emails, any employee at your workplace could most likely list three pet peeves of theirs regarding email communication. The following are the answers I got from a few FRG employees:

  • Emails that have a missing subject line or have no content
  • Emails that do not have a clear response to your question
  • Emails that do not get to the point quickly or are superfluous

How do we ensure that we are not the employees that are sending the above types of emails? How do we ensure that we are taking advantage of this easy communication tool to be efficient, productive, and constructive in the workplace? How do we ensure that we are communicating in a professional manner?

Follow these rules (in no particular order) on email etiquette to make sure you are sending correct and understandable information.

  1. Keep it simple. Use succinct sentences that get promptly to the point.
  2. Be professional. If you are not positive the receiver of the email knows who you are, briefly introduce yourself (e.g., state your name, job title, and purpose of email).
  3. Make it standalone. Suspect that the person did not read previous emails in the thread. Refresh their memory first on what the discussion was and then continue.
  4. Read the entire email before sending. Ensure that there are no typos and that the content makes sense.
  5. Make no assumptions. Do not assume that others understand what you are saying. Be clear in your statements/questions.
  6. Be consistent. Include a clear and intuitive subject and body content. Ensure that terms are being referenced the same in email threads to avoid confusion (e.g., Financial Risk Group vs. FRG).
  7. Always consider lists. Use bulleted lists to directly group lists, steps, questions, etc. Use numerical or alphabetical lists for items that need to be in a specific order and bullets for items that do not.
  8. Use parallel structure. Construct sentences so that readers can understand difficult concepts more quickly.
    • Parallel structure is especially important when writing lists. Begin each statement with the same part of speech. For example, if explaining steps in a process, use verbs such as type, click, or close to begin each statement.
    • Parallel structure can be used in comparisons. Repeat the same phrases in order to be clear. For example, the new user interface is more user-friendly than the old user interface.
    • Parallel structure can help define the format and/or layout. Repeat the same format and/or layout to ensure consistent organization. For example, if you include a bolded header for one topic, use a bolded header for each topic.

The above rules can be applied to emails sent to any reader, whether it be a co-worker, boss, client, future employer, etc. It is ultimately important to send clear, understandable statements and questions to ensure you receive a productive and expected response.

Samantha Zerger, business analytics consultant with the Financial Risk Group, is skilled in technical writing. Since graduating from the North Carolina State University’s Financial Mathematics Master’s program in 2017 and joining FRG, she has taken on leadership roles in developing project documentation as well as improving internal documentation processes.

CECL – The Power of Vintage Analysis

I would argue that a critical step in getting ready for CECL is to review the vintage curves of the segments that have been identified. Not only do the resulting graphs provide useful information but the process itself also requires thought on how to prepare the data.

Consider the following graph of auto loan losses for different vintages of Not-A-Real-Bank bank[1]:

 

While this is a highly-stylized depiction of vintage curves, its intent is to illustrate what information can be gleaned from such a graph. Consider the following:

  1. A clear end to the seasoning period can be determined (period 8)
  2. Outlier vintages can be identified (2015Q4)
  3. Visual confirmation that segmentation captures risk profiles (there aren’t a substantial number of vintages acting odd)

But that’s not all! To get to this graph, some important questions need to be asked about the data. For example:

  1. Should prepayment behavior be captured when deriving the loss rates? If so, what’s the definition of prepayment?
  2. At what time period should the accumulation of losses be stopped (e.g., contractual term)?
  3. Is there enough loss[2] behavior to model on the loan level?
  4. How should accounts that renew be treated (e.g., put in new vintage)?

In conclusion, performing vintage analysis is more than just creating a picture with many different colors. It provides insight into the segments, makes one consider the data, and, if the data is appropriately constructed, positions one for subsequent analysis and/or modeling.

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1] Originally I called this bank ACME Bank but when I searched to see if one existed I got this, this, and this…so I changed the name. I then did a search of the new name and promptly fell into a search engine rabbit hole that, after a while, I climbed out with the realization that for any 1 or 2 word combination I come up with, someone else has already done the same and then added bank to the end.

[2] You can also build vintage curves on defaults or prepayment.

 

RELATED:

CECL—Questions to Consider When Selecting Loss Methodologies

CECL—The Caterpillar to Butterfly Evolution of Data for Model Development

CECLData (As Usual) Drives Everything

IFRS 17: Killing Two Birds

Time is ticking for the 450 insurers around the world to comply with the International Financial Reporting Standard 17 (IFRS 17) by January 1, 2021 for companies with their financial year starting on January 1.

Insurers are at different stages of preparation, ranging from performing gap analyses, to issuing requirements to software and consulting vendors, to starting the pilot phase with a new IFRS 17 system, with a few already embarking on implementing a full IFRS 17 system.

Unlike the banks, the insurance industry has historically spent less on large IT system revamps. This is in part due to the additional volume, frequency and variety of banking transactions compared to insurance transactions.

IFRS 17 is one of the biggest ‘people, process and technology’ revamp exercises for the insurance industry in a long while. The Big 4 firms have published a multitude of papers and videos on the Internet highlighting the impact of the new reporting standard on insurance contracts that was issued by the IASB in May 2017. In short, it is causing a buzz in the industry.

As efforts are focused on ensuring regulatory compliance to the new standard, insurers must also ask: “What other strategic value can be derived from our heavy investment in time, manpower and money in this whole exercise?”

The answer—analytics to gain deeper business insights.

One key objective of IFRS 17 is to provide information at a level of granularity that helps stakeholders assess the effect of insurance contracts on financial position, financial performance and cash flows, increasing transparency and comparability.

Most IFRS 17 systems in the market today achieves this by bringing the required data into the system, compute, report and integrate to the insurer’s GL system. From a technology perspective, such systems will comprise a data management tool, a data model, a computation engine and a reporting tool. However, most of these systems are not built to provide strategic value beyond pure IFRS 17 compliance.

Apart from the IFRS 17 data, an insurer can use this exercise to put in place an enterprise analytics platform that caters beyond IFRS 17 reporting, to broader and deeper financial analytics, to customer analytics, operational and risk analytics. To leverage on new predictive analytics technologies like machine learning and artificial intelligence, a robust enterprise data platform to house and make available large volumes of data (big data) is crucial.

Artificial Intelligence can empower important processes like claims analyses, asset management, risk calculation, and prevention. For instance, better forecasting of claims experience based on a larger variety and volume of real-time data. The same machine can be used to make informed decisions about investments based on intelligent algorithms, among other use cases.

As the collection of data becomes easier and more cost effective, Artificial Intelligence can drive whole new growths for the insurance industry.

The key is centralizing most of your data onto a robust enterprise platform to allow cross line of business insights and prediction.

As an insurer, if your firm has not embarked on such a platform, selecting a robust system that can cater to IFRS 17 requirements AND beyond will be a case of killing 2 birds with one stone.

FRG can help you and your teams get ready for IFRS 17.  Contact us today for more information.

Tan Cheng See is Director of Business Development and Operations for FRG.

Top 6 Things To Consider When Creating a Data Services Checklist

“Data! Data! Data! I can’t make bricks without clay.”
— Sherlock Holmes, in Arthur Conan Doyle’s The Adventure of the Copper Beeches

You should by now have a solid understanding of the growth of and history of data, data challenges and how to effectively manage themwhat data as a service (DaaS) is, how to optimize data using both internal and  external data sources, and the benefits of using DaaS. In our final post of the series, we will discuss the top six things to consider when creating a Data Services strategy.

Let’s break this down into two sections: 1) pre-requisites and 2) the checklist.

Prerequisites

We’ve identified four crucial points below to consider prior to starting your data services strategy. These will help frame and pull together the sections of information needed to build a comprehensive strategy to move your business towards success.

Prerequisites:

1: View data as a strategic business asset

 In the age of data regulation including BCBS 239 principles for effective risk data aggregation and risk reporting, GDPR and others, data, especially that relating to an individual, is considered an asset that must be managed and protected. It also can be aggregated, purchased, traded and legally shared to create bespoke user experiences and engage in more targeted business decisions. Data must be classified and managed with the appropriate level of governance in the same vein as other assets, such as people, processes and technology. Being in this mindset and appreciating the value of data and recognizing that not all data is alike and must be manged appropriately will ultimately ensure business success in a data-driven world.

2: Ensure executive buy-in, senior sponsorship and support

As with any project, having executive buy-in is required to ensure top down adoption. However, partnering with business line executives who create data and are power users of it can help champion its proper management and reuse in the organization. This assists in achieving goals and ensuring project and business success. The numbers don’t lie: business decisions should be driven by data.

3: Have a defined data strategy and target state that supports the business strategy

Having data for the sake of it won’t provide any value; rather, a clearly-defined data strategy and target state which outlines how data will support the business will allow for increased user buy in and support. This strategy must include and outline:

  • A Governance Model
  • An Organization chart with ownership, roles and responsibility, and operations; and
  • Goals for data accessibility and operations (or data maturity goals)

If these sections are not agreed from the start, uncertainty, overlapping responsibilities, duplication of data and efforts as well as regulatory or potentially legal issues may arise.

4: Have a Reference Data Architecture to Demonstrate where Data Services Fit

Understanding the architecture that supports data and data maturity goals, including the components that are required to support the management of data from acquisition through distribution and retirement is critical. It is also important to understand how they fit into the overall architecture and infrastructure of the technology at the firm.  Defining a clear data architecture and its components including:

  • Data model(s)
  • Acquisition
  • Access
  • Distribution
  • Storage
  • Taxonomy

are required prior to integration of the data.

5. Data Operating Model – Understanding how the Data Transverses the Organization

It is crucial to understand the data operations and operating model – including who does what to the data and how the data ownership changes over time or transfers among owners. Data lineage is key – where your data came from, its intended use, who has/is allowed to access it and where it goes inside or outside the organization – to keep it clean and optimize its use. Data quality tracking, metrics and remediation will be required.

Existing recognized standards such as the Global Legal Entity Identifier (LEI) that can be acquired and distributed via data services can help in the sharing and reuse of data that is ‘core’ to the firm. They can also assist in tying together data sets used across the firm.

Checklist/Things to Consider

Once you’ve finished the requirements gathering and understand the data landscape, including roles and responsibilities described above, you’re now ready to begin putting together your data services strategy. To build an all-encompassing strategy, the experts suggest inclusion of the following.

1:  Defined Data Services Required

  •  Classification: core vs. business shared data and ownership
    • Is everyone speaking a common language?
    • What data is ‘core’ to the business, meaning it will need to be commonly defined and used across the organization?
    • What data will be used by a specific business that may not need to be uniformly defined?
    • What business-specific data will be shared across the organization, which may need to be uniformly defined and might need more governance?
  • Internal vs external sourcing
    • Has the business collected or created the data themselves or has it been purchased from a 3rd party? Are definitions, metadata and business rules defined?
    • Has data been gathered or sourced appropriately and with the correct uniform definitions, rules, metadata and classification, such as LEI?
  • Authoritative Data Sources for the Data Services
    • Have you documented where, from whom, when etc. the data was gathered (from Sources of Record or Sources of Origin)? For example, the Source of Origin might be a trading system, an accounting system or a payments system. The general ledger might be the Source of Record for positions.
    • Who is the definitive source (internal/external)? Which system?
  • Data governance requirements
    • Have you adhered to the proper definitions, rules, and standards set in order to handle data?
    • Who should be allowed to access the data?
    • Which applications (critical, usually externally facing) applications must access the data directly?
  • Data operations and maintenance
    • Have you kept your data clean and up to date?
    • Are you up to speed with regulations, such as GDPR, and successfully obtained explicit consent for the information?
    • Following your organization chart and rules and requirements detailed above, are the data owners known, informed and understand they are responsible for making sure their data maintains its integrity?
    • Are data quality metrics monitored with a process to correct data issues?
    • Do all users with access to the data know who to speak to if there is a data quality issue and know how to fix it?
  • Data access, distribution and quality control requirements
    • Has the data been classified properly? Is it public information? If not, is it restricted to those who need it?
    • Have you defined how you share data between internal/external parties?
    • Have the appropriate rules and standards been applied to keep it clean?
    • Is there a clearly defined process for this?
  • Data integration requirements
    • If the data will be merged with other data sets/software, have the data quality requirements been met to ensure validity?
    • Have you prioritized the adoption of which applications must access the authoritative data distributed via data services directly?
    • Have you made adoption easy – allowing flexible forms of access to the same data (e.g., via spreadsheets, file transfers, direct APIs, etc.)?

2: Build or Acquire Data Services

 To recap, are you building or acquiring your own Data Services? Keep in mind the following must be met and adhere to compliance:

  • Data sourcing and classification, assigning ownership
  • Data Access and Integration
  • Proper Data Services Implementation, access to authoritative data
  • Proper data testing, and data remediation, keeping the data clean
  • Appropriate access control and distribution of the data, flexible access
  • Quality control monitoring
  • Data issue resolution process

The use and regulations around data will be constantly evolving as will the number of users data can support in business ventures. We hope that this checklist will provide a foundation towards building and supporting your organization’s data strategies. If there are any areas you’re unclear on, don’t forget to take a look back through our first five blogs which provide more in-depth overviews on the use of data services to support the business.

Thank you for tuning into our first blog series on data management. We hope that you found it informative but most importantly useful towards your business goals.

If you enjoyed our blog series or have questions on the topics discussed, write to us on Twitter@FRGRISK.

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

 

RELATED:

Data Is Big, Did You Know?

Data Management – The Challenges

Data as a Service (DaaS) Solution – Described

Data as a Service (DaaS) Data Sources – Internal or External?

Data as a Service (DaaS) – The Benefits

Is Your Business Getting The Full Bang for Its CECL Buck?

Accounting and regulatory changes often require resources and efforts above and beyond “business as usual”, especially those like CECL that are significant departures from previous methods. The efforts needed can be as complex as those for a completely new technology implementation and can take precedence over projects that are designed to improve your core business … and stakeholder value.

But with foresight and proper planning, you can prepare for a change like CECL by leveraging resources in a way that will maximize your efforts to meet these new requirements while also enhancing business value. At Financial Risk Group, we take this approach with each of our clients. The key is to start by asking “how can I use this new requirement to generate revenue and maximize business performance?”

 

The Biggest Bang Theory

In the case of CECL, there are two significant areas that will create the biggest institution-wide impact: analytics and data governance. While the importance of these is hardly new to financial institutions, we are finding that many neglect to leverage their CECL data and analytics efforts to create that additional value. Some basic first steps you can take include the following.

  • Ensure that the data utilized is accurate and that its access and maintenance align to the needs and policies of your business. In the case of CECL these will be employed to create scenarios, model, and forecast … elements that the business can leverage to address sales, finance, and operational challenges.
  • For CECL, analytics and data are leveraged in a much more comprehensive fashion than previous methods of credit assessment provided.  Objectively assess the current state of these areas to understand how the efforts being put toward CECL implementation can be leveraged to enhance your current business environment.
  • Identify existing available resources. While some firms will need to spend significant effort creating new processes and resources to address CECL, others will use this as an opportunity to retire and re-invent current workflows and platforms.

Recognizing the business value of analytics and data may be intuitive, but what is often less intuitive is knowing which resources earmarked for CECL can be leveraged to realize that broader business value. The techniques and approaches we have put forward provide good perspective on the assessment and augmentation of processes and controls, but how can these changes be quantified? Institutions without in-house experienced resources are well advised to consider an external partner. The ability to leverage expertise of staff experienced in the newest approaches and methodologies will allow your internal team to focus on its core responsibilities.

Our experience with this type of work has provided some very specific results that illustrate the short-term and longer-term value realized. The example below shows the magnitude of change and benefits experienced by one of our clients: a mid-sized North American bank. A thorough assessment of its unique environment led to a redesign of processes and risk controls. The significant changes implemented resulted in less complexity, more consistency, and increased automation. Additionally, value was created for business units beyond the risk department. While different environments will yield different results, those illustrated through the methodologies set forth here provide a good example to better judge the outcome of a process and controls assessment.

 

 Legacy EnvironmentAutomated Environment
Reporting OutputNo daily available manual controls for risk reportingDaily in-cycle reporting controls are automated with minimum manual interaction
Process SpeedCredit run 40+ hours
Manually-input variables prone to mistakes
Credit run 4 hours
Cycle time reduced from 3 days to 1 for variable creation
Controls & AuditMultiple audit issues and Regulatory MRAsAudit issues resolved and MRA closed
Model ExecutionSpreadsheet driven90 models automated resulting in 1,000 manual spreadsheets eliminated

 

While one approach will not fit all firms, providing clients with an experienced perspective on more fully utilizing their specific investment in CECL allows them to make decisions for the business that might otherwise never be considered, thereby optimizing the investment in CECL and truly ensuring you receive the full value from your CECL buck.

More information on how you can prepare for—and drive additional value through—your CECL preparation is available on our website and includes:

White Paper – CECL: Why the expectations are different

White Paper – CECL Scenarios: Considerations, Development and Opportunities

Blog – Data Management: The Challenges

Data as a Service (DaaS) – The Benefits

Let’s start with a succinct summary of the benefits of DaaS.

Data as a Service (DaaS) is one way to consistently deliver and effectively manage data from multiple sources across the firm, both internal and external. It can be used as one “logical” and centralized, authoritative (golden) source for critical data used across the organization.

It is an efficient way to deliver data that can also improve speed to market on requests for new and additional data, either from internal parties or regulators or substitute sources.

DaaS can thus be used effectively to achieve the following:

  • Reduce the cost of supplying non-proprietary external data needed across the firm
  • Quickly deliver internal, proprietary data to groups that need to share data
  • Deliver a single view of the data across Finance, Risk and the Business to meet business and regulatory demands
  • Provide a 360-degree view of clients for firms with complex client relationships and service organizations
  • Deliver a definitive record of a firm’s products across the organization

At the same time, the quality of the data can be monitored and reported centrally, along with federated (decentralized) data ownership. This allows ‘data owners’ to be responsible for defining and maintaining the data that they generate and know best, allowing others to ‘share it’. Examples include definitions of a firm’s products by the marketing groups or analytic calculations, such as Risk-Weighted Assets or capital calculations from Finance and Risk groups. Transparency of the data is increased and reuse of data is facilitated.

Critically, the quality of data can be significantly improved when DaaS is implemented within a firm. Central data monitoring, access and updating by the Sources of Record makes sure the data is sourced from the owners on a timely basis. Sharing of data and reuse, with multiple eyes on the same data, allows for quick resolution of errors and can save companies potential embarrassment.

All of this leads to three key benefits for firms:

  • Agility: Firms become more agile as they can quickly implement changes and roll out new data because of the unified data models, transparency, and simplicity of the process.
  • Flexibility and Cost Efficiency: New applications and necessary regression testing – which verifies that software previously developed still performs the same way after it’s been interfaced with other software – is easier as definitions, structures and data models are already known and often enhanced and extended.
  • Transparency: Firms utilize unified data models, definitions, metadata, tools, and support. They can leverage the specific experience of data owners and providers to access data closer to the source and increase transparency and benefit from enhancements.

All of the above seems so logical, so sensible. And it is. As we’ve seen, however, it’s not the logic behind the DaaS process which trips people up; it’s mastering the practical implementation of the process.

In the next blog, we offer a check list of things to consider when you’re developing a DaaS solution for your firm.

 

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

 

Questions? Comments? Talk to us on Twitter @FRGRISK.

Related:

Data as a Service (DaaS) Data Sources – Internal or External?

As mentioned previously, Data as a Service (DaaS) can be used to provide a single source of authoritative (or golden) data for use in a firm’s critical applications, particularly when data is needed from multiple sources or it is ‘siloed’ in the organization.

DaaS provides the ability to use a single mechanism to deliver data from both internal or external data sources in a consistent format and to monitor its quality and use. The choice of whether to utilize internal or external providers depends on the proprietary nature of your data or unique requirements.

External providers are appropriate when data is ‘commoditized’, or readily available and accessible, where there is no competitive advantage to generate the data. Securities Master and other reference data, like currency codes and exchange tickers, are examples of external data that can be provided via DaaS. Here you can rely upon a third party to provide the information in a usable form that can be plugged into the DaaS framework and delivered to users.

It may also make sense to turn to an external source when a third-party data provider has a particular expertise in data, such as a specialized data feed of prepayment or economic data, vendor indexes, or universal identifier information, such as the Legal Entity Identifier (LEI). Independence requirements for items like securities pricing and valuations will often dictate use of an external service, too.

The benefit of using external providers for data delivered via DaaS is that it frees your internal teams up to focus on derived data and value-added activities, such as analyzing the data and developing products.  It also be used to ensure independence for items like third party pricing.

Communication and transparency with your external provider are essential, though. External providers have to be made aware of what exactly a company needs to ensure it is accurate. Both internal and external data subject to regulatory scrutiny, for example, usually must be 100% correct, understandably, while data for internal use only – such as a client’s dining preferences or where unstructured data is used to identify trends for marketing and product development, might not need the same level of scrutiny (although any personal data still requires security and privacy protection.)

If using an external data, don’t get so dependent on that supplier that you can’t substitute one for another— you may need to switch suppliers down the road because of changes in needs or to improve data quality. Instead, adapt it to your own interface so that another provider can be inserted easily, if need be, in your own format. That is the power of DaaS. A standardized framework will make additions and substitutions easier. It can also assist in tracking the use of data as required to meet regulatory requirements, such as the Global Data Protection Requirement (GDPR).

Regardless of whether you use an internal or external DaaS service, companies have to be prepared to give a clear definition of the data and metadata to ensure the correct use and interpretation of the data and to assist in its use and management. This will assist answering queries from regulators or third parties, including data providers.

 

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

Questions? Comments? Talk to us on Twitter @FRGRisk.

Related:

Data as a Service (DaaS) Solution – Described

Data as a Service (DaaS) can be used to provide a single source of authoritative (or golden) data for use in a firm’s critical applications. Here, a logical layer of the data (often in-memory for quick access) can serve up data that has been verified, defined, and described with metadata from source systems. This provides data that is readily understood and has unique and unambiguous meaning with the context in which these data is known and used.

Source systems can be tapped in real time to ensure that all changes are accurately and immediately represented in the data service.  This source system can be internal or external to the firm, depending on the need by the receiving party.

The authoritative data can then be served up to multiple users at the same time, delivered in a format that they prefer (e.g., file transfer, online access, download into other systems or spreadsheets), giving them quicker access to information in a format that they can readily use.

By cleaning the data, describing it and distributing it from a central logical location to users and applications, data quality checks can be performed and efficiencies gained. Given that ‘all eyes’ are on the same data, any data quality issues are quickly identified and resolved.

DaaS offers the flexibility to provide access to both internal and external data in an easily consumable form. Access to a multitude of authoritative data in a consistent format can be extremely useful in timely delivery of new applications or reporting, including regulatory reports, and will be quicker than waiting for a single physical source for this data to be built.

This is particularly useful when data are needed by multiple parties and when data is ‘siloed’ in an organization. How many versions are there? How many platforms? Don’t forget, data generation has vast potential.

The more complex your data needs, the more likely that a DaaS solution will benefit you.

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

Questions? Comments? Talk to us on Twitter @FRGRisk

Related:

Data Management – The Challenges

Does your company suffer the challenges from data silos? Dessa Glasser, Principal with the Financial Risk Group, who assists Virtual Clarity on data solutions as an Associate, discusses the challenges of data management in our second post for our blog series.

In our previous blog, we talked about the need for companies to get a handle on their data management. This is tough, but necessary. As companies develop – as they merge and grow and as more data becomes available to them in multiple forms – data silos occur, making it difficult for a ‘single truth’ of data to emerge. Systems and data are available to all , but often behavior among teams are different, including the ‘context’ in which the data is used. Groups have gathered and enhanced their own data to support their business, making it difficult to reconcile and converge to a single source for business critical data.

This complication is magnified because:

  • New technology brings in large amounts of data – both structured and unstructured
  • Each source has its own glossary of terms, definitions, metadata, and business rules
  • Unstructured data often needs tagging to structured data to assist firms in analytics
  • Structured and unstructured data require metadata to interpret the data and its context

As Dessa Glasser notes, “The problem is not getting the data, the problem is processing, interpreting and understanding the data.”

Companies can also be hindered by the ‘do it yourself’ mentality of their teams, whereby individuals who want systems implemented immediately will often construct a process and data themselves, rather than waiting for IT to deliver it, which either takes time or may not be not available on a timely basis.

 These cross-over efforts undermine a firm’s ability to effectively use the data and often leads to:

  • Data sources being available in multiple forms – both internal and external
  • The costly and manual reconciliation of incorrect data and difficulty aggregating data
  • The inability to generate business insights from the data – more time is spent processing, and reconciling the data, rather than analyzing it

Meanwhile, clients are demanding a holistic view of the services they’re buying into, and management and regulators, when they ask for data, want to know the full relationship with clients across the firm and a holistic view of all aggregated risk positions, which is hard to pull together from numerous teams who work with and may interpret the data differently. Companies must present a cohesive front, regardless of each team’s different procedures or context in which the data is used.

All of the above are prime examples of why the governance and management of data is essential. The end goal is one central, logical, authoritative source for all critical data for a company. It is important to treat data as a business asset and ensure the timely delivery of both well-defined data and metadata to the firm’s applications and business users. This can be done by developing a typical data warehouse to serve up the data, which often can take years to build. However, this can also be facilitated more quickly by leveraging advances in technologies, such as the cloud, data access and management tools, and designing a Data as a Service (DaaS) solution within a firm.

So, how to go about it?

Tune in next month to blog 3 where we’ll discuss.

Dessa Glasser is a Principal with the Financial Risk Group, and an independent board member of Oppenheimer & Company, who assists Virtual Clarity, Ltd. on data solutions as an Associate. 

Questions? Comments? Talk to us on Twitter @FRGRisk

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Current Expected Credit Loss (CECL) a New Paradigm for Captives, Too

The ramifications of CECL on Financial Institutions has in large part focused on Banks, but as we addressed in a recent paper, “Current Expected Credit Loss: Why the Expectations Are Different,” this new accounting treatment extends to a much larger universe.  An example of this are the captives that finance American’s love affair with cars; their portfolios of leases and loans have become much larger and the implications of CECL more significant.

As with other institutions, data, platforms, and modeling make up the challenges that captives will have to address.  But unlike other types of institutions captives have more concentrated portfolios, which may aid in “pooling” exercises, but may be inadvertently affected by scenario modeling.  A basic tenet for all institutions is the life-of-loan estimate and the use of reasonable and supportable forecasts.  While some institutions may have had “challenger” models in the past that moved in this direction, captives have not tended to utilize this type of approach in the past.

The growth of captives portfolios and the correlation to a number of macro-economic factors (e.g. interest rates, commodity prices, tariffs, etc.) call for data and scenarios that require a different level of modeling and forecasting.  Because FASB does not provide template methodologies or calculations it will be necessary to develop these scenarios with the mindset of the “reasonable and supportable” requirement.  While different approaches will likely be adopted, those that utilize transaction level data have the ability to provide a higher level of accuracy over time, resulting in the goals laid out in the new guidelines.  As might be imagined the ability to leverage experience in the development and deployment of these types of models can’t be overemphasized.

We have found that having the ability to manage the following functional components of the platform are critical to building a flexible platform that can manage the changing needs of the users:

  • Scenario Management
  • Input Data Mapping and Registry
  • Configuration Management
  • Model Management

Experience has taught that there are significant considerations in implementing CECL, but there are also some improvements that can be realized for institutions that develop a well-structured plan. Captives are advised to use this as an opportunity to realize efficiencies, primarily in technology and existing models. Considerations around data, platforms, and the models themselves should leverage available resources to ensure that investments made to address this change provide as much benefit as possible, both now and into the future.

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