www.fico.com Make every decision count TM INSIGHTS WHITE PAPER Number 82 Reducing Regulatory Drag on Analytics Teams Automated workflows standardize and speed model management processes Banking regulators are increasing scrutiny of analytic models, peeling back layers of the onion with probing questions. They want to know not only how models affect credit policies and customer decisions, but about the processes used for developing, validating, deploying and updating them. Banking executives, increasingly aware of the full dimensions of model risk, are also asking pointed questions. Finding answers can add drag to the performance of analytics teams—even pulling them away from high-value work that leads to competitive advantage. To improve compliance and response time to detailed questions, leading banks are implementing formal model management processes throughout the analytic lifecycle. But while best practices may be understood, they can be challenging to deploy consistently across analytic teams. It’s also difficult to know if they’re being followed at the right level of granularity, such that no matter where regulators probe—and even with analytic staff turnover— all questions can be readily answered. This white paper examines how automated, configurable model workflow tools promote process consistency and accountability. We show how banks are using workflow at enterprise and departmental levels to improve model governance without creating extra work for analytic teams. In fact, by orchestrating model processes while automatically capturing key artifacts, decisions and sign-offs, workflow can lead to better model performance. Analytic teams are freed to spend more of their time creating and updating models. We’ll cover: • Turning work friction into work flow • Creating standard processes that fit the needs of diverse teams • Tracking model and characteristic lineages Improve compliance while spending 75% less time on it.
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www.fico.com Make every decision countTM
INSIGHTS WHITE PAPER
Number 82
Reducing Regulatory Drag on Analytics Teams Automated workflows standardize and speed model management processes
Banking regulators are increasing scrutiny of analytic models, peeling back layers of the onion with
probing questions. They want to know not only how models affect credit policies and customer
decisions, but about the processes used for developing, validating, deploying and updating them.
Banking executives, increasingly aware of the full dimensions of model risk, are also asking pointed
questions.
Finding answers can add drag to the performance of analytics teams—even pulling them away from
high-value work that leads to competitive advantage.
To improve compliance and response time to detailed questions, leading banks are implementing
formal model management processes throughout the analytic lifecycle. But while best practices may
be understood, they can be challenging to deploy consistently
across analytic teams. It’s also difficult to know if they’re being
followed at the right level of granularity, such that no matter
where regulators probe—and even with analytic staff turnover—
all questions can be readily answered.
This white paper examines how automated, configurable model workflow tools promote process
consistency and accountability. We show how banks are using workflow at enterprise and
departmental levels to improve model governance without creating extra work for analytic teams.
In fact, by orchestrating model processes while automatically capturing key artifacts, decisions and
sign-offs, workflow can lead to better model performance. Analytic teams are freed to spend more
of their time creating and updating models.
We’ll cover:
• Turning work friction into work flow
• Creating standard processes that fit the needs of diverse teams
• Tracking model and characteristic lineages
Improve compliance while spending 75% less time on it.
Reducing Regulatory Drag on Analytics Teams
INSIGHTS WHITE PAPER
November 2014 www.fico.com page 2
Banks, long at the vanguard of data analytics for business, have continued to expand their use
of models. Predictive scorecards and other models for anticipating and responding to customer
behavior now play a central role in every area of operational decision making. The sheer quantity
of these models is increasing rapidly.
What hasn’t advanced as quickly is model management. An August 2014 article by
Butler Analytics pointed out that “some banks simply do not know how many models are
actually deployed.” In other cases, model information is in so many places that preparing for
audits or answering regulator inquiries becomes extremely labor-intensive. The Butler article
reports that “the modeling staff in one major US bank now spend 80% of their time meeting
regulatory requirements, detracting from much needed new model development.”
While this is an extreme case, every bank is seeing an impact on the productivity and
performance of its analytics teams. Both those building models and those compiling reports
and answering inquiries have more work to do. And where one group is charged with both
duties, compliance tasks can siphon away analytic resources from work that could be producing
significant value for the bank.
An executive in the mortgage division of one bank told FICO that, for a period of time, 25% of its
analytics workforce had to be diverted to collecting, preparing and reporting on data required by
regulators—costing the bank tens of millions of dollars.
Yet some banks are moving ahead of the curve. They’re improving their ability to answer
questions about analytics while lightening the burden on their analytics teams. In fact, a bank
devoting 80% of modeler time to regulatory requirements could reduce that expenditure to 20%
or less.
Model Management—Getting Ahead of the Curve
Over the past five years, analytic excellence has become a core requirement in today’s financial market… Modeling touches virtually every decision of the bank… IDC Financial Insights A Framework for Model Governance, June 2013
“Organizations today face heavy regulatory pressures…To meet these challenges and mitigate risk, they need model management solutions that can reduce resources required to complete compliance
audits, and encompass the full model lifecycle and risk-management continuum...” ” Peyman Mestchian Managing Partner at Chartis
The Insights white paper series provides briefings on research findings and product development directions from FICO. To subscribe, go to www.fico.com/insights.
Tracking model and characteristic lineages
The concept of model lifecycle management is broader than the life of any particular model. As
analytics proliferate across organizations and the pace of change in financial services markets
accelerates, banks need to start thinking in terms of lineages.
Regulators may, for instance, ask pointed questions about why a retired model was replaced
with the current one, and which customer characteristics were given greater predictive weight
in the process. When a customer characteristic is changed by its author/owner, banks need to
know which models incorporate that characteristic so they can manage all downstream effects.
State-of-the-art model lifecycle management takes this broader view. Automated workflows
help banks capture a complete lifecycle history of all models and their components. For each
model, users can quickly track the lineage of any predictive customer characteristic—generated
during development, harvested from a previous model, taken from a shared library, etc. For each
characteristic, they can see everywhere it is currently used or was previously used—predictive
models, segmentation strategies, decision strategies, etc.
Another advantage of this approach is that banks have the opportunity to evaluate the value of
individual customer characteristics over time.
Increasingly far-reaching and detailed regulatory scrutiny is making it more important than ever
for banks to put standard, approved model management processes in place. At the same time,
banking executives want more visibility into and control over the full dimensions of model risk,
including both compliance exposure and performance issues.
Automated workflows that feed model lifecycle management solutions help banks lower model
risk by improving process consistency and accountability. And they do it without clipping the
wings of analytic teams—in fact, they offer efficiencies that can help them soar.
To learn more about best practices for model management, visit the FICO Blog and read these
Insights papers:
• Customer Centricity: Four Bank Success Stories (No. 78)
• Satisfying Customers and Regulators: Five Imperatives (No. 75)
• Comply and Compete—Model Management Best Practices (No. 55)