TRILLIUM SOFTWARE INSIGHTS

The Winds of Financial Data Policy Change are Blowing in the UK

By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Change is a difficult fact of life for every organization right now.  At Trillium Software, we’re challenged every day to adapt and innovate in response to radical shifts occurring in the information quality market.  At this point, nearly every industry sector is experiencing a similar metamorphosis in their landscape of business partnerships, competitors and customer profiles.  The realignment, happening in the wake of multiple disruptive waves of financial, policy and business change, is impacting everyone’s traditional marketplace and all our business engagements.  Change is everywhere, and all at once!

One interesting aspect of these new ‘business data’ engagements to note is the change in the makeup of the audience interested in the topic of data quality.  Good executives are always very keen to understand and exploit the opportunities arising from detection and remediation of costly errors, whether they occur on the ‘shop floor’ or in the risk office.  So it’s not unusual to see very senior officers intensely interested in data quality issues once the consequences of poor quality data are made clear to them. 

The impact of gaps in data accuracy elevates dramatically when the ‘corporate focus’ on data moves from instances or rows in operational systems to elements  ‘measured’  in risk or performance metrics.  When it impacts at the level of a key performance indicator, bad data produces measurable financial pain, manifested in costs such as increased risk reserves.

Executive interest and understanding is a cornerstone event in developing the foundation for true corporate data governance.  It is very interesting and exciting to see that ‘transformation dynamic’ for data quality play out in many of the sessions we are privileged to participate in.

Turning briefly to another place where changes to the way data is managed, take a look at the United Kingdom financial services industry.  Data is having a huge impact on the environment in which businesses operate in the UK.  

The link was provided to me by independent consultant Ken O’Connor, a very keen observer, blogger, DQ industry expert and author of an E-book on the issues and challenges of creating a single customer view.  It relates to a new mandatory initiative impacting banks in the UK, which will be expanded to Asia, Europe and other regions around the globe.

For those of you unfamiliar with the back story, here’s a brief outline.  In the UK, banks do not have the same deposit insurance that we have in the US, where deposits in banks are guaranteed by the Federal Deposit Insurance Corp (FDIC)

The Financial Services Authority (FSA) in the UK is attempting to address that gap in depositor protection this year, with a project known as the Financial Services Compensation Scheme (FSCS.)  The scheme was designed to protect depositors in the event of a single bank failure, or a more universal financial crisis.  What it is intended to do is to provide every depositor in the UK access up to £50k within a very short timeframe in the event of one or several bank failure(s). 

The scheme imposes a service fee on each deposit taking institution to create a pool of insurance for payout in the event that the failing institution cannot make this information available and the government must step into an administrative role.  The challenge is that banks are required to create a single view of each customer (consumer, commercial and wholesale) and produce ‘on demand’ a list that balances the totals across multiple accounts.  

The problem is banks do not fundamentally work that way.  Banks usually administer money in separate accounts and often do not know about how customer accounts link together.  They may demonstrate an understanding of their relationship with you because of their excellent customer service, but their accounting systems do not reflect the knowledge they have in their heads.  So there are many deposit-taking institutions who will face an uphill battle to produce anything approaching accurate in comprehensive customer data, especially because so many of their accounting systems are old and data does not match across systems.

As the year winds on, more-and-more banks are coming to the realization that they cannot achieve the required accuracy, so pressure is mounting to find a resolution. 

For those us who live in the ‘data world’ this amounts to pretty ‘high drama.’ And if you have an account in a UK bank, you should probably be interested as well.

This UK financial services situation is a highly volatile situation that is bound to see day-to-day “changes,” twists and turns.  Stay tuned to Ken’s blog and my blog here to learn more over the coming weeks and months.  Companies better wake up soon to these changes or introduce huge risk into their organizations and firms.

How are you going to deal with these policy changes?  Have you already thought about it?  Let me know.

How Do New Federal Standards Measure DQ’s Impact on Risk Calculations?

By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

The May 24th announcement by the Federal Housing Finance Agency (FHFA) of a common framework of standards for data collection and management is welcome news for a lot of reasons.  This framework will be used by all US banks, Fannie Mae and Freddie Mac, and will operate across the US Mortgage industry to facilitate information exchange, data collection, reporting, problem tracking, etc.

However, the following statement within the framework particularly stands out:

(The) common data standards framework will increase efficiency for lenders while enabling the Enterprises to manage risk more effectively.

In light of this, there is now recognition of the direct link connecting data quality and risk calculation in a specific line of business, and this is important for several reasons – most significant is that banks will soon have the tools to measure (and peer review) exactly how much poor data quality adds in costs to their mortgage risk operations. 

Everyone has seen examples and ‘use cases’ measured in billions of dollars, but the idiosyncrasies of each bank’s operations have masked the overall costs to the industry (and its customers)- which are staggering.

It’s tempting to go back and ‘game out’ the scenarios of what might have happened had Fannie Mae and Freddie Mac had ‘the right tools at the right time’ to detect the poor quality of the data that was being used in the essentially corrupt mortgage book they were being fed a couple of years ago.  One imagines that even the most rudimentary data quality metrics tracking the input from NINO (No Income No Asset) loans would have operated as ‘a canary in the mineshaft’ and provided early warning of the home loan market collapse.

Data is a shared resource of a bank’s operations and risk functions – but data coming from multiple ‘downstream’ systems needs common standards and metrics to be ‘fit for purpose’ in establishing accurate risk exposures. The metrics and governance processes s being put in place by the FHFA measure data are designed to lay bare the critical link between problems in data and erroneous risk calculations. 

This new ‘direct connect’ between data accuracy and risk management should function like the “Rosetta Stone” and enable shared understanding of critical data assets at certain key junctures – and establish a much closer alignment between the ‘facts on the ground’ an d the true nature of the bank’s risk exposures.

Cloud Rolls In

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Over and above the stormy conditions that businesses are experiencing in their core markets, a new information paradigm rises overhead – “the Cloud.” 

It promises vast resources and drastically reduced costs, modeled on and leveraging the Web. It offers access to reams of reference data, applications and supporting systems.  I’ll bet you some bright and socially-wired individual out there is going to make “Webillions” creating the first iTunes for organizing the applications and data sources that are already available out there on the Cloud. 

In the context of many promises and risks, the Cloud also threatens to completely change the paradigm for how a business manages data.  And that casts a shadow on the way companies do business now.   

Things will not be like the relatively safe world within a firewall.  It is not just the risk of storing data in a different physical (virtual) environment, but the Cloud introduces other risks too such as application management, access to data, reporting and analysis, and data security. 

Companies will solve the access control problems associated with storing data on the cloud, as the fences around corporate data have been receding steadily for at least a decade now. 

But a new information management structure is emerging –the economics are just too compelling for it not to.  Yet, in order to embrace something as evanescent as the Cloud, organizations must have a precise track of their current trajectory, a strong internal compass, and a picture of what success looks like when you arrive at your planned destination and goals.  That’s a lot like sailing – in low clouds.

It will not be easy, but navigating the Cloud will be a journey worth being aboard. 

In a follow up, I will talk about what some of our “best and brightest” customers are doing to align their data in ways that are highly adaptable to the cloud.

Data Arbitrage – a new way to manage risk for profit

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Arbitrage is one of those concepts that seem incredibly complex and arcane until you see it in action.  It was originally a practice that gained currency (literally) before the advent of internet-speed information exchanges. In an earlier time, the trading price of commodities could vary slightly in different markets. If you knew the price difference, it was possible to simultaneously buy and sell something and realize immediate gain. This concept can be applied to financial markets or even more recreational endeavors.

Spring is around the corner, and for those of you who equate that with the start of baseball season, you may also know that the concept of arbitrage was used in a fantastic baseball book by Michael Lewis titled Moneyball. In it, he explained how the principles of arbitrage were applied by the small-market Oakland Athletics professional baseball team to achieve a completely different goal – winning baseball games.
 
If you haven’t read the book, it highlights how Billy Beane, the team’s general manager, implemented a series of key metrics that could be applied to the player performance statistics captured throughout the major and minor league baseball system to find undervalued talent. Statistics are everywhere in baseball, but the idea that you could develop statistical metrics to spot and exploit something as seemingly arbitrary as performance on a baseball diamond to win major league baseball games was a stroke of genius. It enabled the Oakland A’s to consistently outperform teams in its division and across the major leagues every year.

One of the great pleasures of my work is that it gives me the opportunity to travel and meet the kind of people who have the same level of insight and intelligence as Billy Beane. Only for them, the challenge is to develop metrics that spot misalignments in financial and customer data that can be exploited to improve operational performance.
 
The same problem exists with data management as in baseball…statistics are everywhere, but you have to be creative and brilliant to develop the metrics that will bring out benefits and minimize risks, resulting in measurable ‘wins.’
 
That’s really what is happening in some of the leading financial services firms I’ve talked with.  Creative minds have begun to spot where data gaps and inaccuracies incur punitive risk reserves. They are finding ways to spot and resolve these gaps, enabling their organization to free up badly needed investment capital that can be pumped back into the investment economy.
 
These days, no available resource is more important than a firm’s maximum investment capital. It’s the equivalent of adding a number one starter to your pitching rotation or a potent number four hitter in the lineup. 
 
I am going to enjoy watching this ‘season’ unfold.

For Financial Institutions, the Devil is in the Details…and the Details are in the Data

 By Richard Wilson, Vice President, Product Strategy, Harte-Hanks Trillium Software

Now that the Financial Crisis Inquiry Commission (FCIC) has begun hearings in Washington, questions are being asked that will require firms to perform granular research and provide transparency:

Were the financial statements your firm issued covering performance throughout 2005-08 accurate?

What information did you disclose about your firm’s exposure to derivatives and nonprime mortgages? When did senior executives disclose that information to the markets? How did they measure the accuracy of that information?

What sources and documents were used to determine risk exposure and capital adequacy?

What measures and what actions were recommended, and what changes did the firm actually implement, in response to the identification of this risk?

Accuracy in capital adequacy and risk reserve data will play an important role in documenting the integrity of business practices employed by financial institutions when they are called on to answer these questions. More important, metrics about the key data used to support Value at Risk or other Key Performance/Risk Indicators will bring to light critical facts about the problems, inadequate measurements and poor practices of the firms involved in the catastrophic failure of the financial markets.  

Beneath all the spreadsheets and business intelligence reports used to tally profit and loss, value at risk, and risk reserve adequacy used by financial executives to make critical decisions, there are ‘stubborn facts’ that exist in the data.  And those facts will tell an important story about the business practices of the firms coming under scrutiny.

Answers to questions like the ones mentioned above will require firms to produce accurate documentation around communication, process and supporting data. Providing accurate responses to inquiries about the complex combination of information and data will be crucial to the reputation of a financial services firm and its officers – and may even determine who spends time in jail.

The devil is in the (data) details.  What does your financial data tell you?  What does it say about you?

Let me know your thoughts by offering comment.

Bank Data on the Hot Seat for Poor Regulatory Reporting

By Steve Bailey, Director, Financial Services Solutions, Harte-Hanks Trillium Software

Regulatory requirements for enterprise risk reporting are critical considerations for banks, especially in the current political climate.  So, as a result, many organizations that I work with are capitalizing on their improvements in data quality principally to achieve better risk management and reduced financial exposure.

Once business leaders understand how data quality and data management affect the bottom line of their business, data quality suddenly becomes a top priority.  That’s what we’re seeing on an increasingly regular basis.

Improved regulatory reporting turns out to be a terrific by-product of improved data quality.  For the business, that improvement leads to new data quality initiatives. Through more in-depth data quality, managers are able to demonstrate knowledge about the data that goes into their risk rating models.  As a result, it becomes possible to accurately report on data quality standards to internal and external auditors.

So, as I see it, enterprise data management is critical to financial services firms and their efforts to better operate and comply with new levels of regulatory oversight.

If data quality is managed correctly, financial services firms will be competitively positioned to bring new product offerings to the market and provide a better customer experience. Technology that fosters data quality and governance allows firms a far greater ability to access more timely data. Creating enterprise level risk management in compliance with regulatory reporting requirements makes the data quality challenge even more complex for these financial service firms.

Given the current regulatory oversight that banking firms have to manage under, best practices of data governance require that the implementation team understand the business policies, and collaborate with technology teams to specify, identify, validate, and manage data. As a result, the responsibility for demonstrating provably correct accuracy shifts from IT to the business users.

Technology teams must continue to promote best practices for systems, security, and platforms as the business begins to work with IT to improve best practices for information accuracy, information quality and data integrity. I refer to this concept of business best practices and data governance as ‘data veracity.’ Data veracity captures the business’ responsibility for data accuracy and it transcends the traditional standard metrics for IT data quality.  Data veracity to me means better regulatory reporting.  What can data veracity mean to you and your business?

The Future Looks Brighter with Financial Data Intelligence & Governance

By Steve Bailey, Director, Financial Services Solutions, Harte-Hanks Trillium Software

As financial organizations look to brighter economic times, it’s a guarantee that governments and international regulatory agencies will introduce new legislation in the aftermath of the global freeze-up of credit markets.  If Sarbanes-Oxley is an example of legislation introduced in response to the collapse of Enron and Worldcom, then what will NEW legislation and reporting requirements look like?  Scary to think about!

Everyone knows that responding to the new demands of compliance will require complex combinations of legal experts, business analysts and implementation teams.  It could cost millions of dollars - and may eventually be perceived to have no appreciable business value at all.  Frightening! 

However, many of the world’s leading financial services organizations recognize that by taking steps TODAY to establish better information and data quality management processes, they can achieve returns with measurable business value immediately.  Even more important, they would position themselves to respond more quickly and at less cost than their industry peers to a new world of “oversight and transparency.”

Take one of my financial services clients for example, who revealed that due to a data quality error, the institution was forced to implement a compliance-mandated Risk Weighted Average, making it a policy requirement to set aside capital reserves of $800K on a $10 million loan.  But, through a Data Intelligence & Governance solution engagement, the data error was detected and corrected, resulting in an immediate $600K INCREASE in capital for use in other critical projects.  These types of real-world, tangible benefits can be experienced by any financial organization that really gets serious about data quality NOW.

The reality for most banks today is they’ve built webs of non-integrated compliance systems, each with extremely limited focus, typically centered on fulfilling a single compliance initiative at a time. Managing this hodge-podge of systems and data repositories adds multiple costly development steps to every business initiative, while creating silos of information and data that could defy data quality efforts.  And, consider the myriad of data that underpins KPI calculations, customer credit measurements, agency debt ratings, and accounts receivables, and you have an extremely difficult compliance challenge.  All these issues add up to hefty risk exposure.

This begs the question – how can firms address these data problems now in order to comply and protect their businesses from even greater risk exposure? 

I believe financial institutions need to consider a flexible, best-fit solution that leverages an enterprise technology platform and employs a consulting engagement model that empowers business owners to become proactive participants in the validation, measurement, and management of all data.  This type of solution would provide foundation processes for enterprise governance of risk and modeling data, and ensure data is matched and linked to enable business users such as GRC teams to correctly validate, aggregate and model risks as required by new regulation. 

What compliance and data quality challenges is your organization facing now?  Care to comment?  Would love to learn more.