Accelerating Returns of Mortgage Operations Utilizing Multi-Faceted Indicators and Analytics
By Mark P. Dangelo
For decades, managers and their teams have sought the “holy grail” of decisive and discrete performance indicators that would assess and predict corporate profitability. As we now know, their inability to cohesively link strategy, operations, risks, and rewards have resulted in permanent industry realignments – M&A’s, failures, oversight, fiduciary breeches, and consumer alienation. However, the industry’s past predicaments were not just contained within individual products or exotic solutions, but with the downstream implications of their adoption. Straightforwardly stated, analytical causality across the enterprise was grotesquely misinterpreted.
Additionally, complacency and arcane systems of beliefs led many to rely on irrelevant indicators, practices, processes, and technologies. Even after billions were spent on SOX, Basel and other regulatory compliance efforts, the recent economic crisis clearly indicates that the global public, not just domestic ones are no better protected than they were in the “Age of Enron.”
So, with all the theories, vendors, and prior pundits being replaced, we have to ask, “What is next?” What are the informational governance and regulatory approaches that have efficacy today and tomorrow? How can agile and adaptable analytics be achieved across the breath of our partners and data sources, including our servicers linked to the programs targeting consumer “workouts?”
Whereas, proactive business analytics and governance were once the domains of larger lenders and originators, innovative business and technology advances have leveled the playing field regardless of organizational size and budgets. The questions are many – the answers are evolving. However, let’s take a “walk on the wild side,” and see what our future holds beyond the anti-climatic stress tests.
Conducting a Diagnostic Assessment
For many organizations, a holistic and critical examination of analytical usage (i.e., business intelligence, dashboards and scorecards, analytical applications, MDM, warehouses, et al) is a time consuming process tainted with internal biases and prejudices. Often times, analytical evaluation and projections are done across the enterprise using fragmented ROI point-solutions — not the least of which are ignoring the hundreds of siloed “management” spreadsheets lacking little referential integrity or understanding of how they interconnect or influence each other.
The result of the ensuing analytical chaos are diverse “versions” of operational performance, ROI, risks, regulatory compliance, and worse yet, a false sense of security – until as illustrated recently, the bottom falls out of markets. Moreover and with great frequency, organizations latch onto “analytical answers” and quickly proceed with allocating resources all in a desperate effort to secure success.
Yet, is the most convenient analytical answer correct? As many enterprises discovered during the recent global financial and economic meltdown, it wasn’t the answers that were difficult to achieve — it was the fact that the wrong questions were being asked. Without the relevant questions and proper alignment with required strategy, managers were ill-prepared to deal with pervasive calamities. It was as one industry observer said, “Driving with your eyes closed.”
It is this hidden cost of disjointed analytical architectures, spread among the business units and IT, which led AMR in 2008 to estimate that global enterprise expenditures exceeded $57 billion USD. What is more, according to December 2008 Accenture report[i], only 60% of organizational decisions were supported by analytical insights. The rest of those tens of billions of dollars worth of corporate investments, well, were not.
With an average organizational analytical investment consuming between $250,000 and $1 million, depending upon market sub-segment, decision makers have to be wondering, what all this technology was really worth — as budgets are cut, consumer scrimp, and the two year recession lingers into 2010. This begs the question for many decision makers, “What are the 3-year returns and operating costs for analytical investments in 2010-2012? Can we afford to sustain what we have and invest in the future? For every dollar of capital spent, are we looking at another$ 5 to $7 spread over the next 3 years?”
To gain a handle on the use of enterprise analytics (EA) and the “questioning of everything” previously deployed within the entity, organizations have begun conducting independent diagnostic assessments to establish an objective baseline and an iterative roadmap for the future. Organizations are no longer just examining impacts within lines of businesses, but the forward and backward value chains spanning multiple operational segments. Representative diagnostic categories include:
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· Financial Impact / Financial Integrity |
· Monitoring Methods, Rigor, Techniques |
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· Operational and Business Intelligence |
· Visualization, Views, and Meta Data |
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· Technology and Infrastructure |
· Performance Management, Reporting |
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· Data Warehouses / Marts |
· Security, Privacy, Information Governance |
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· Dashboards and Scorecards |
· Regulatory Compliance and Delivery |
Underpinning a base of solid financial and performance data, organizations have embarked on their own “analytical stress tests” in an effort to define what and how to frame their indicators – and the methods and sources needed for their accurate delivery. Even though we hate to admit it, the regulators just may have been on to something. When examining the data, process, and indicators contained within the stress tests themselves, before the results were subject to change, there are substantial self correcting and regulating diagnostic guidance buried in their approaches.
In a Financial Times article by Russell Walker on January 30, 2009, he stated, “JPMorgan’s success came from identifying novel data and realizing that it challenged conventional thinking.” Isn’t that really what analytics and the investments they represent are all about?
Integrating Strategy, Demands, and Success
Analytics taken out of context can yield “false positives” – aka erroneous decisions. Without proactive linkages to strategy, operational demands, and performance results, analytics are merely bits and bytes spinning on a metallic coated platter. By making the most of the entire spectrum of corporate analytics and their implications, what led to an industry’s dishonor can be used as its foundation for future growth.
For executives seeking to move forward and identify profitable new markets, what strategies for growth should be defined, deployed, and sustained in the prospective face of onerous government oversight? What has worked in the past and where should organizations concentrate their resources in the future facing new consumer behaviors? Finally, how can technology and policy be exploited to create a robust business case for reducing costs, growing profits, and capitalizing on market trends, especially within the rebirthed secondary markets?
Many quantitative organizational analytical approaches are starting over. After huge CAPEX investments coupled with significant budget increases, the value of insight and governance produced by “intelligence and analytical” tools have yielded a false sense of purpose and security.
The long held ideas, practices, and techniques of assessing and projecting have proven inadequate for current operating demands. With historical 20/20 hindsight, what is now apparent is that the conceptual and piecemeal methods deployed were too remedial and the business solutions too abstract. A new way forward must be developed.
Using the aforementioned diagnostic assessment, progressive organizations are integrating strategy, demands and success into an iterative go-forward roadmap (illustrative list below):
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· Consumer profiles, market usage, and competitor capabilities |
· Orchestrated solution sets built on componentization of best-in-class |
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· Advanced multi-dimensional data segments (e.g., OLAP) |
· Predefined and configured software components |
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· Forward and reverse “supply chains” across micro and macro sources |
· Auditability, repeatability, adaptability to promote consistency and accuracy |
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· Interoperability of decisioning networks and toolsets |
· Vendor capability and product leadership within centers of excellence |
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· Reusable libraries of statistical data sources and routines (e.g., ETL, marts, warehouses) |
· Visual and standardized query capabilities and reporting across functional segments (e.g., financial, operations, risks) |
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While there is much more that needs to be written on internalization of agile and adaptable analytics (AAA) into the corporate culture of tomorrow’s finance and mortgage groups (FMG’s), the journey begins with an objective assessment and a new path forward. For as we now realize, all too painfully, there are “ticking time bombs” still remaining within our existing operations. They must be rooted out.
The uses of analytics were once about “personal” manipulation and insights – individual, department, or special operational interest. The survival criteria of organizations are now focused on their end-to-end usage across the enterprise, while proactively integrating isolated components among the channels to achieve relevant macro-micro efficacy.
A new age of Enterprise Analytics has been launched as it is now questioning everything surrounding past and future indicators. However, are we ready to embrace new questions and non-conventional insights? Or will we relegate the new findings to aberrations that are just too painful to accept?
[i] “Business Intelligence Software Time is Now,” BusinessWeek, Rachael King, March 2, 2009.