TRILLIUM SOFTWARE INSIGHTS

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.