TRILLIUM SOFTWARE INSIGHTS

Regulatory Data Management – The Gift that Keeps on Giving

By Michael Hutnyan, EVP Sales & Solution Strategy, Harte-Hanks Trillium Software

After reflecting on the recent holiday season, now that many of us have recovered from some extensive gift-giving (and are now paying the resulting bills), it occurs to me that my financial services clients are receiving a more troubling gift – one that only promises on-going costs and a possible deviation from growth and service - - new and ever-changing regulatory requirements.

I’m referring primarily to those government-sponsored, mandated regulatory demands that involve a significant data management component. Newly defined regulations such as FATCA and the yet-to–be-unveiled LEI (legal entity identifier), will require an extensive and proactive data management strategy by banks. They’ll need to clearly demonstrate knowledge and control over data and its quality in order to achieve compliance.

Like other data-intensive regulations before them, such as the FSA’s Financial Services Compensation Scheme and the Basel Accords, these new regulations necessitate a detailed readiness assessment and a remediation plan for relevant data defects in order to satisfy regulators’ demands.

Moreover, our clients confirm these regulations are not simply one time events that come and go. It’s widely understood that these regulations will morph and change over time, requiring an on-going understanding of the regulations and a flexible data quality management platform.

Our clients are loathe to invest in the same regulatory compliance effort more than once, and establishing a flexible data management environment that can change as rules change is critical for banks to keep cycles free for the real business of banking. Unfortunately, financial services firms today often find themselves with limited budgets, and only those items that “must be done” (such as complying with regulations) are receiving funding. 

A recent article in Bank Systems and Technology supports this trend. In this feature, Michael Versace of IDC comments that growth in IT spending for risk management (including regulatory compliance) will outpace the growth of overall IT spending and will top 15 percent of total IT spending in financial services in 2012.

So, it appears that our financial services clients will continue to have to do more with less, and deal with receiving the “gift” of a constantly changing regulatory world. My mom always said it is better to give than to receive. I think she was correct in this particular case, but, a data quality management solution that is flexible enough to work within your current financial data systems and calculation models to ensure regulatory compliance – now, that could be the very best “gift” for any bank this year.

How is your financial services organization handling regulatory compliance around data?

 

The Impact of Basel III Readiness & Data Challenges on your Bank

By Shameka Simmons, Banking Attorney, Financial Solutions, Harte-HanksTrillium Software

Banking firms expect that the Basel III framework will be published and implemented in the United States by the end of first quarter 2012. Basel III imposes additional capital surcharges, and mandates liquidity standards, both of which trigger data elements that deserve your attention. Although some US banks have expressed opposition to Basel III capital requirements, the banks are in fact overcapitalized and are trying to prepare for the new capital thresholds demonstrated by the positions on their call reports. 

But, the unspoken question remains…”are banks really ready for Basel III?”

Some banks have already begun internal preparation for implementation of Basel III by participating in qualitative impact studies (QIS) that analyze and measure the affect the new framework may have on a bank’s business model, balance sheet, and product mix. Project setup and working groups will assist with the technical realization and data preparation/data quality management of the new framework relevant to the liquidity ratios. Yet, banks are facing many challenges including the treatment of missing data, comprehensive data consistency, extensive data warehouses, and numerous data sources. 

In particular, the liquidity coverage ratio (LCR) will require banks to have sufficient high-quality liquid assets to withstand a 30-day stressed funding scenario that is specified by regulators. The net stable funding ratio is a longer-term structural ratio designed to address liquidity mismatches. It covers the entire balance sheet and provides incentives for banks to use reliable sources of funding. This liquidity framework mandates consistency in data based on manual or automated calculation of the ratios or different evaluations (book value vs. market value) of assets.

The liquidity framework may introduce a significant challenge to financial services firms because banks will have to integrate their existing liquidity risk systems into their Basel frameworks, which is likely not yet done as U.S. banks, for example, are still anticipating implementation of Basel II

In addition, adoption of a new framework will require a substantial budget for banks to transition and remain in compliance. So, banks may believe they are addressing their financial data issues in advance of Basel III, but they may not be working out the right problems to ensure compliance. A deeper and more specific data assessment may be needed to find the data inaccuracies you may not know about yet. The consequences of mistakes will be costly. 

Is your data ready for Basel III?

Shameka Simmons is a Banking Attorney with Harte-Hanks Trillium Software.  She offers a wealth of knowledge about the inner workings and processes of financial institutions, retail/commercial lending, and complex loan transactions.  She gained this expertise through her experience at GMAC (now Ally Bank), the Legal Division of the FDIC Enforcement division, and her active role in the Illinois Attorney General subpoena/investigation of Countrywide at the height of the foreclosure crisis.

Changing Wrong to Right – Making the Case for Data Governance

By Nigel Turner, Vice President, Information Management Strategy, Harte-Hanks Trillium Software

In my last blog, our data quality (DQ) protégés of LA’s most famous fictional detective Philip Marlowe had been investigating a number of key suspects which they believed had been hurting the business. They had pounded the mean streets, interrogating witnesses to the crime. Evidence had been gathered. It was now time to act. The next step was to make the case to the Business to prosecute the data and try to right the wrongs that had been perpetrated.  

No data quality improvement project should ever be started without a business case.   DQ improvement is never an end in itself. Before deciding to invest in DQ, it’s vital to demonstrate to the business that the problem to be addressed is worth tackling, and if successful, will deliver real benefits to the organisation. 

From my time in British Telecom, where many cases were assembled and presented for business endorsement, a number of general lessons emerged.

First, before preparing a case based on the analysis and evidence gathered earlier, ensure that the case has a business sponsor. This is a clear sign that someone in the business cares about it and feels it worthwhile to pursue. The sponsor should have sufficient influence and authority to champion the case, gather support and break down inevitable barriers that others will put up. 

It also needs, at least, the initial support of key stakeholders who will be affected by the proposed improvements, and / or who might be needed to make the changes required, especially if those changes demand alterations to business processes, people’s objectives and so on.

Second, always try to present more than one option for action. For example, your investigations might have uncovered inconsistencies between billing and sales data which had been causing loss of revenue. You might lay out two improvement strategies. One might be a relatively inexpensive option with tentative or reduced risk and benefit, such as introducing an additional manual check of data before transferring data from Sales to Billing. 

The second option might be a full blown automated DQ tool-based validation and remediation solution, which would initially be more costly but which would deliver more enduring longer term benefits. Make sure you include a recommended option, but give the business some leeway. In any and all cases always include a ‘Do Nothing’ option. Here doing nothing might mean that billing and sales sources become increasingly out of step, with potential revenue losses increasing gradually over time. It also shows that doing nothing is not a viable choice, now or into the future. 

Third, whatever the core rationale of the business case (whether it’s enhancing business performance, customer perceptions, conformance to regulation etc.) always try to include quantitative financial benefits. DQ improvement almost always requires material investment and costs, so your case will be more compelling if you can show it will positively impact the bottom line. Even regulatory drivers usually have some underpinning or tangential financial benefits, e.g. avoiding the risks of hefty fines from a regulator if you get it wrong. 

Fourth, remember that each business case is unique. Every DQ problem I have come across is caused by a unique combination of people, process and technology shortcomings. The benefits of improving it, and the costs involved in doing so, are therefore always specific to that case. If you can, enlist the help of a finance expert who has worked on business cases before to help you assemble the case and to validate any financial benefits. He or she will know what works and what doesn’t work in your organisation. 

Once you’ve gone through these steps you are ready to present the case to the business and IT people who you need to make it become a reality. In the next blog, I’ll highlight some techniques for winning support within your stakeholder community.  Putting a strong case together is a necessary but not sufficient step. You now need strong advocacy to convince the judge and jury.

Philip Marlowe has done his job and is working on his next investigation. It’s now time to call in Perry Mason.

 

Data Quality Becomes More Crucial for Business Intelligence

 By Michele Goetz, Vice President of Product Marketing, Harte-Hanks Trillium Software

Boris Evelson of Forrester Research provided his Top 10 Business Intelligence Predictions for 2012. 

There are two predictions that really stood out as key data quality pitfall areas:

“…No management or control is not acceptable, but too much control does not work. Finding the right win-win combination that combines the flexibility and agility that self-service brings with behind-the-scenes monitoring and adjusting will become the name of the game.

“Exploration (without preconceived notions, prebuilt fixed data models, or up-front specific questions in mind) will be the new bread and butter of BI suites in addition to reporting, querying, OLAP, and dashboards/data visualization.”

In each of these instances, analysts and consumers of data may not have the quality controls in place to gather and get the insight necessary to make informed decisions on what the data tells them. Data quality programs still have not reached an enterprise-wide state to bridge gaps across silos of data. Even when paired with master data management initiatives, quality is sporadic to highly-specific business requirements and business units. Open up the doors to data exploration across multiple and possible un-regulated data stores and you can see where consumers will come up against data reconciliation issues and poor data quality.

However, all is not lost. Data quality solution capabilities have come a long way from back office middleware for data warehouse integration. Trillium Software has specifically focused on quality controls at the point of exploration and consumption in environments that analysts and business consumers live in. Whether controls are introduced through a SOA deployment, or assessment and analysis are instituted within business intelligence activities, poor quality data introduced into self-service intelligence and data exploration don’t have to impede insight.

 

Illuminating the Mean Streets - Investigating & Quantifying Data Quality Problems

By Nigel Turner, Vice President, Information Management Strategy, Harte-Hanks Trillium Software

In my previous series of blogs I focused on how one large global organisation, British Telecommunications (BT), recognised the need to take action to address data quality (DQ) barriers to the development of its business, and started to identify candidate cross-organisational DQ problems that could be addressed within the broader context of an enterprise-wide data improvement initiative. 

Now, I plan to highlight how this long list of potential problems was narrowed down to a specific list of initial improvement projects, how business cases for action were made, and how these cases won the backing of BT’s senior executives. This particular blog focuses on how the list of DQ suspects was further narrowed down. Key to this was a detailed investigation of the problems.

It was the time to use forensic techniques. It was time to call in the detectives. 

One famous fictional investigator, Raymond Chandler’s Philip Marlowe, would have recognised the challenge. As he walked alone through the mean streets of LA, Marlowe would learn that dirty data quality deeds had been done, that unspeakable crimes involving data had been perpetrated. His job was to establish the facts, and put things right. As one of Chandler’s most famous Marlowe novels, the ‘Big Sleep,’ memorably stated, “The streets were dark with something more than night,” so investigation was needed to throw light on the data quality problems, and to point the way to a brighter future.

Like all good detectives our DQ investigators started with what they knew. This was the list of 50+ candidate data quality themes and problems uncovered by earlier analysis. It was now time to identify the prime suspects. But the investigators could not do this alone. Expert help was needed. 

So they called in a group that understood the damage these suspects had caused.  This group was known as “The Business.”  A workshop with these business stakeholders was set up. Many of the business people invited had been identified in the first phase of investigations; others were suggested by them.

The investigators provided the list of suspects and let The Business decide on their fate. They used a combination of established techniques – Force Field Analysis, Benefits Management, and the Boston Grid – to help the Business decide which cases to focus on first. There was much wailing and gnashing of teeth but eventually a shortlist was drawn up. The Business also agreed to meet again when the detailed analysis was complete. 

The investigators did what they were told and conducted a detailed examination of the prime suspects. They used two primary tools. First, they interviewed key people impacted at all levels in the organisation, recorded process and people problems caused by the data, and did a first cut assessment of the financial and other impacts (e.g. regulatory compliance) of these problems. In parallel with this, they put their magnifying glasses on the data itself, first identifying the key sources where the delinquent data was held.

Using data profiling tools they then assessed the scale and scope of the data problems in these key sources. They also put together an initial improvement proposal, embracing people, process, technology and the data itself. 

Some general rules emerged from this process. First try to express the impact of DQ problems on the business by their financial impact. This is best done by estimating the costs of poor quality data. What processes fail when data is incorrect or missing?  How much does each failure cost?  How often do these failures happen? What are the estimated costs of failure in six months, one year’s time?  

Second, what other damage is done? How do these failures damage the company’s image and brand? How do they jeopardise the organisation’s ability to meet its legal and / or regulatory obligations?  How are customer perceptions affected?  How is the morale of the organisation’s people impacted?

The end result of these investigations was a detailed analysis of a small number (3-5) of key DQ related problems. The prime suspects had been rounded up and interrogated. The next step was now to decide what their fate would be.  It was time to call in the Business again. See my next blog for what happened next…

Capitalism or Favoritism?

By Shameka Simmons, Banking Attorney, Financial Solutions, Harte-HanksTrillium Software

Shameka Simmons is a Banking Attorney with Harte-Hanks Trillium Software.  She offers a wealth of knowledge about the inner workings and processes of financial institutions, retail/commercial lending, and complex loan transactions.  She gained this expertise through her experience at GMAC (now Ally Bank), the Legal Division of the FDIC Enforcement division, and her active role in the Illinois Attorney General subpoena/investigation of Countrywide at the height of the foreclosure crisis.

Is “Too Big to Fail” still a common reality? Absolutely! Why else would the government allow the country’s second largest national bank (by assets) to transfer its risky security instruments to its retail bank?

This type of conduct is predictable for a troubled bank…that’s why regulations exist to prevent it, due to the risk this type of transfer poses to the deposit insurance fund (DIF). Sections 23A and 23B of the Federal Reserve Act represent an effort to reconcile the objectives of banks’ interactions with their affiliates.  The goal of the act is to protect the bank’s deposits and maintain economic separation between the bank and its affiliate subsidiaries. This is critical to encourage better risk management practices between financially affiliated organizations and to discourage non-bank subsidiaries from benefiting from the government guaranty provided to banks.

But, in the earlier case, shifting risky security instruments to a retail bank indirectly helps to insure the risk generated from investment banking operations, because most deposits in retail banks are insured by the FDIC.  Many would argue that this practice is not fair.  I take the position that (a) it may not be permissible under the law, (b) it may be construed as an act of favoritism for the subject institution, and (c) too big to fail is still a reality in this economic climate.

In light of all this, it becomes easier to justify why these transfers drive the need for the type of LEI solution that Trillium provides its financial services clients, due to the numerous counterparties involved and the risk exposure of different legal entities. In the rare event of a failure of this bank, the LEI solution can help regulators quickly ascertain counterparty identities involved in the transfer and the correct legal entities for an immediate wind down and liquidation of assets.

What do you think?  Is it favoritism or capitalism?

 

Are Increased Capital Levels a Result of Regulatory Uncertainty?

By Shameka Simmons, Banking Attorney, Financial Solutions, Harte-HanksTrillium Software

Shameka Simmons is a Banking Attorney with Harte-Hanks Trillium Software.  She offers a wealth of knowledge about the inner workings and processes of financial institutions, retail/commercial lending, and complex loan transactions.  She gained this expertise through her experience at GMAC (now Ally Bank), the Legal Division of the FDIC Enforcement division, and her active role in the Illinois Attorney General subpoena/investigation of Countrywide at the height of the foreclosure crisis.

Despite the recent financial crisis and mounting litigation against banks due to mortgage-backed securities, global banks are somehow overcapitalized and less leveraged. The burning questions are “how and why?” 

I think I might be able to answer these questions in one word…REGULATIONS!

Yes, I said regulations. Even though most of the domestic regulations from Dodd-Frank are still in-the-making, and Basel III has yet to be promulgated, banks already understand the expected regulatory capital thresholds for domestic and global standards, but absolutely no idea as to how they will be applied. I opine that banks are holding extra capital because they are truly uncertain of what the future may bring, relevant to regulations.

In particular, banks are dreading the designation of systemically important financial institution (SIFI) by regulators. If designated as a SIFI, a bank will face even more capital requirements (such as capital buffers) due to the risk it poses to other financial institutions and the overall economy. 

So, can regulatory uncertainty actually be a good thing? If regulatory uncertainty causes banks to tighten their purse straps and reduce lending, it’s probably not a good thing due to the impact it has on the economy and consumer spending. But, if it forces banks to better prepare for crisis by maintaining additional capital beyond the current regulated requirement, and make them more risk-averse when pursuing opportunities, then it is an EXCELLENT thing…for regulators…but not so much for the banks, as the money could be utilized for other purposes.

Need I say a double-edged sword?

 

What Works When Planning to “Operationalize” Data Governance

By Michael Stiffler, Strategic Consultant, Data Governance, Harte-HanksTrillium Software

In case you missed it, we presented our thoughts around “operationalizing” data governance in a recent Trillium Software-sponsored webinar. I wanted to stress a couple of key takeaways:

1. Data governance is an iterative program.  You need to set the expectation that the program will evolve, especially as the company's business requirements evolve. For instance, you may have to adjust your framework to include other areas that are now important to the business. Or perhaps you understand the corporate culture a little better now and need to adjust your approach to increase your chances of being successful.

2. Don't do data governance just for the sake of doing data governance. You need to understand the key business drivers, and tie data governance to activities that will actually increase business value. What business pains can be mitigated through data governance? How should they be prioritized? These are questions you'll need to answer.

3. Are you able to measure success? How does the business define success? Dashboards and scorecards are one way of measuring and showing improvements to your data (which is one definition of success). This may also resonate better with the business folks, especially if you have gotten their input in defining "success."

4. Make no mistake, you will experience roadblocks or setbacks.  How you respond and recover is what makes the difference between success and failure.  Don't be afraid to admit your mistakes and learn from them.  Use these lessons learned to help improve implementations into other data domains.

5. Communicate your successes. Set up a forum to share best practices across the team of data stewards, data owners, and other stakeholders involved in your data governance program (e.g., center of excellence). Additionally, the dashboard mentioned above will help communicate your successes.

I outline these takeaways because they have been critical components of the real, live data governance engagements I’ve seen and managed. They were presented with the hopes of helping you plan better in advance, and reach success faster. I hope these are helpful considerations for you as you move forward with your own data governance efforts.  Good luck!

 

When Risk Becomes Reality – Even Weather Impacts Data Quality

By David Binkley, Senior Technical Account Manager, Harte-Hanks Trillium Software

As many of you may know, the weather here in the United States has been rather unique this summer.  It’s been extremely hot and dry in the South and Southwest and extremely rainy here in the Northeast.   Take those conditions into effect; add a random earthquake and a hurricane or two; and you’ve got a very challenging opportunity for many of our clients.

Well, I just happened to be at one of those clients recently, whose shipping operations were badly impacted by all of the above external conditions.  This particular customer happens to be a manufacturer of extremely expensive products that are required to be refrigerated from the point of manufacturing to the final point of use.

Taking into account the disruptions of power in the East from hurricanes like Irene, the client needed to be able to effectively synchronize product manufacturing and shipments to their customers in regions where the power grid was not impacted, or to hold the manufacturing of products for those areas where the power was likely to be out.   Because this client has excellent data quality control over their shipping information, the IT and shipping departments were able to quickly identify potentially impacted orders and have customer service representatives contact the customer to either reschedule the deliveries or confirm that delivery was still requested.

Now you might ask, how much of an impact did data quality really have on this situation?  Well, take the following into account:

To identify the orders that were impacted, the customer first needed to know what products required refrigeration, and that required accurate coding of the components of the order.  Second the order had to be evaluated as to the location of the actual shipping address, and that shipping address had to be evaluated as to its accurate location within the affected delivery areas.  Third, the customer had to be contacted via phone, e-mail etc. to verify that the order could still be sent. 

Finally, the shipper had to coordinate its impacted delivery areas with our customer’s shipments to make sure that the shipper’s distribution centers were also not impacted by the weather.  ALL OF THIS INFORMATION relies on the quality of data!!

Fortunately, for our customer and thousands of their customers, all of the above worked out.  Literally thousands of shipments were halted, re-routed or otherwise modified to make sure product would not be impacted by these events.  Production runs were modified to make up for halted orders and business resumed as normal after a week or so. 

In fact, the only problem I heard about was that some reports weren’t out at their usual time the day after manufacturing and shipping had set an all-time record for orders fulfilled in a single day as catch-up processing was in full bloom.  Well, I guess you just can’t win them all…
Think about it.  Would your data and its quality permit you to respond as well?

Two Capital Frameworks Cause Challenges for US Banks

By Shameka Simmons, Banking Attorney, Financial Solutions, Harte-HanksTrillium Software

Shameka Simmons is a Banking Attorney with Harte-Hanks Trillium Software.  She offers a wealth of knowledge about the inner workings and processes of financial institutions, retail/commercial lending, and complex loan transactions.  She gained this expertise through her experience at GMAC (now Ally Bank), the Legal Division of the FDIC Enforcement division, and her active role in the Illinois Attorney General subpoena/investigation of Countrywide at the height of the foreclosure crisis.

Increased capital demands have introduced a host of challenges for U.S. banks, especially now that they are operating under two regulatory capital frameworks. U.S. regulators issued a final rule June 2011 for core banks that have adopted the “advance approaches,” which utilize general risk-based capital rules to compute the bank’s risk-based capital floor.

This final rule maintains the relevancy of Basel I, but also burdens banks with many regulations that mandate increases in regulatory capital. Increases in regulatory capital reserves will have an unintended effect by tying up money that could be used for loans, investments, and growth. 

For example, Dodd Frank legislation imposes capital requirements on all depository institutions, and their subject holding companies, while Basel III includes two new capital buffers. So, this begs the question, how and when will regulators determine one appropriate capital regime to effectively protect against risk and absorb losses in this volatile economic climate?

This question will likely remain unanswered until banks overcome the following challenges as a result of the current dual capital regime:

•    The risk based capital floor (calculated under Basel I) does not adequately address “risk sensitivity” in banks’ credit portfolios. In addition, the new floor does not reflect financial innovations and risk management practices at large financial institutions. Thus, the bare minimum that banks are required to maintain in regulatory capital will not safeguard against the very risks that contributed to the recent financial crisis in the first place.

•    Financial institutions are facing a rigorous task in measuring capital under Basel I, II, and III (when implemented) with no transitional floors for Basel II, but a parallel run and phase-in period for Basel III…all while using Basel I to compute the floor. Yes, I know…extremely confusing!

•    Economic capital models and regulatory capital models are presumed to be aligned, but with differences in purposes and scope.  However, under current capital frameworks, economic capital will eventually erode as regulatory capital burdens a bank’s platform.

•    The tier 1 risk-based capital floor for U.S. banks will conflict with Basel III minimum tier 1 capital as a result of the capital conservation buffer.

As a result of all this, U.S. banks will continue to face competitive disadvantages as foreign banking organizations aren’t subject to the same floor requirement/dual utilization of Basel I and Basel II.

The subject frameworks will affect overall capital positions for banks. It is questionable whether the challenges will (a) impede growth ( b) induce further investment in compliance solutions (c) spark creativity amongst bank risk management personnel to meet regulatory demands…or all of the above! 

To prepare for the challenges that the dual frameworks introduce, banks should identify key problem areas that lead to erroneous capital calculations under both frameworks, such as data quality. Banks also should perform an in-depth analysis of the calculation changes associated with each framework because each framework requires quality data to get the best result.