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RMA/AFS Data Quality Survey2017 Highlights
April 26, 2018
©2018 Automated Financial Systems, Inc. All Rights Reserved. AFS and all AFS product trademarks Are registered trademarks of Automated Financial Systems, Inc. The content of this document represents confidential and proprietary information of AFS. This information may not be disclosed to any third party, other than the direct addressee and its employees, agents, and representatives. The infringement of this prohibition may violate AFS proprietary and trade secret rights with resulting irreparable damage to AFS. Your cooperation is requested and appreciated. Thank you for your help in this matter. AFS 123 Summit Drive, Exton, Pennsylvania 19341. Telephone (610) 524‐9300 Fax (610) 524‐7977
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.2April 26, 2018
Achieving Success through Execution
IntroductionNinth Annual RMA/AFS Data Quality Survey
AFS and the Risk Management Association (RMA) have jointly conducted the global survey “State of Enterprise Data to Support Credit Risk Management” annually since 2007, excepting only 2011 and 2013.
The survey’s unique focus has been to gather insights into how data quality is perceived, evaluated and managed by banks.
The 2017 survey was conducted from early November to mid‐December: 32 questions covering data governance, confidence and new initiativesSent globally to bank executives with responsibility for credit, risk and/or data managementResponses received from 26 institutions in North America, Europe and EMEA; 19 respondents had participated in the 2016 survey69% of respondents represented Risk Management, 15% Commercial Credit, 8% Technology, 8% Other
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.3April 26, 2018
Achieving Success through Execution
Survey Objectives“What is Good Data Quality and What Does it Depend On?”
Effective credit risk management is predicated upon certain basic capabilities:Measuring the risks inherent in credit applicants Information about current credit positions and exposuresForecasting how and why exposures can changeManaging to keep exposures within established risk appetites
These capabilities, and therefore the decisions based upon them, are all fundamentally dependent on the quality of data supporting them.
Our survey tracks how banks define and judge their data quality, what policies and procedures are in place to manage it, and what specific steps they are taking to improve it.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.4April 26, 2018
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The Data Quality JourneyFrom Benign Neglect to Potential Competitive Differentiator
Evolving business value of data: Descriptive Predictive Prescriptive
The Strategic Data “Journey”Data quality is the critical pre‐condition for exploiting future technology innovation and realizing the strategic value of data
Enterprise Data Governance
Strategic DataManagement
Fintech & AI Innovations
Data QualityBusiness Value
What are our business priorities?What is good data?Why is it important?Executive commitmentChief Data OfficerProcess & technology upgrades
ValidationConsistent terminologyAutomated controlsSystem and data integrationReal‐time informationTransparent data lineage“One version of the truth”
Open API to integrate processRPA to automate repetitive tasksPattern recognitionLearning and rememberingArtificial intelligenceVoice activationNatural language understandingAdvanced data analyticsBusiness recommendations
Embedded DQ capabilitiesSustainabilityBetter business decisionsMonetization through greater efficiency & data serviceData as competitive differentiator
Compliance or Cleanup Mandate
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.5April 26, 2018
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The Strategic Value of Data“Data is the New Oil”
Data is now recognized as a strategic asset that must be continuously nourished and cultivated.
Survey participants have shown steady and noteworthy jumps in strategic priority of data quality.
“The future is data.” “Data is the new oil.” “Success begets confidence
begets success.”
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.6April 26, 2018
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Data Quality Confidence RegainedImprovement Efforts are Bringing Success
The large jump in data confidence in last year’s survey was questionable.
This year validated it. No “excellent” ratings but higher
overall confidence supported by 10% increase in “average” ratings.
Answers broken by asset size for first time.
Higher confidence at larger banks may be due to greater regulatory scrutiny.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.7April 26, 2018
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Data Quality: Chronic ImpedimentsA New Public Enemy #1?
Inadequate system functionality, budget priorities and manual work‐arounds have become the leading problems for data quality.
Is this a corollary by‐product of recent data quality improvements, or just a “blip?”
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.8April 26, 2018
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Executive Management SupportPerceived Strong Support Doubles Over 10 Years
Mixed perceptions of executive support for data quality improvement at smaller banks.
However, a huge overall positive trend in past decade.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.9April 26, 2018
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Data ManagementMixed Judgments Despite Stronger Employee Support
While 89% say their bank manages credit risk effectively, only 31% agree that data quality policies are well managed.
Last year’s level was 38%. Nonetheless, 69% say
bank employees understand and support improving data quality as a strategic priority.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.10April 26, 2018
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Data ManagementChief Data Officers are Nearly Universal at Larger Banks
Chief Data Officer (CDO) or equivalent is becoming a “best practice.”
When separated by asset size, however, that role is much more prevalent at larger banks.
There is a close correlation with adoption of enterprise‐level data governance strategy.
[Q:9] Percentage of Banks with CDO or Equivalent Role
[Q:8,9] Banks with CDO Role and DQ Strategy
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.11April 26, 2018
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Chief Data OfficersPrimary Job Responsibilities
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Data Quality in the Credit ProcessResponsibility Remains Disjointed
Banks recognize “credit product management,” but it does not carry over to responsibility for the credit process workflow.
Data quality remains generally not a factor in executive compensation.
[Q:12] Does your bank recognize credit productmanagement as a distinct responsibility?
[Q:13] Does any one role or business unit “own” or oversee the entire credit delivery process?
[Q:14] Does any one role or business unit have responsibility for ensuring the completeness and accuracy of data in the credit delivery process?
[Q:15] Does your institution link data quality to executive compensation?
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.13April 26, 2018
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Technology & OperationsTop Data Quality Challenges
Challenges categorized as “technological and operational” often bleed into “organizational and budgetary.”
Consistency, cross‐business accessibility, and informational silos are perennial challenges affecting both technology and operations.
They have remained for several iterations at or near the top of chronic problems.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.14April 26, 2018
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Data Process Life Cycle Problem “Hot Spots” Vary by Asset Size
The “point of first data entry” is by far the most problematic spot for data.
Booking into the servicing system is often done manually.
Larger banks cite system transfers where data loss, corruption and errors are rife.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.15April 26, 2018
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Cause and Effect in the Credit ProcessManual Data Entry and Lack of Validation
Excessive manual intervention and lack of validation at point of entry lead all other factors.
This is indicative of insufficient system integration.
Data must be re‐entered manually to move through the credit process, jeopardizing its integrity.
Data not validated at point of entry must often be performed by units without access to source documents.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.16April 26, 2018
Achieving Success through Execution
Data QualityAggregation from Multiple Source Systems
Challenges with system integration and data aggregation remain prevalent.
35% have established a single‐system repository for credit risk data and 19% say they are “under way.”
72% have more than ten or an “unsure” number of systems feeding their credit risk data warehouse.
There has been steady but slow progress in reducing complexity since 2007.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.17April 26, 2018
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Credit Risk Data Meets Present NeedsNotwithstanding Technology Challenges
Although still short of full compliance with BCBS 239 Principles, most banks say their credit risk reports are readily accessible and adequate for purpose.
Despite numerous issues with technology, banks are highly confident they are “getting what they need.”
Credit risk data is updated in real‐time for only 8%.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.18April 26, 2018
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Data Quality ImprovementRecent Initiatives are Delivering Sustainable Benefits
The second year asking about effectiveness of improvement projects.
40% of banks say expectations have been surpassed or mostly met.
Risk management was the largest driver at 36%.
Cost reduction was cited by only 4%.
64% say improvements are lasting and sustainable.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.19April 26, 2018
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“One Version of the Truth”Getting Closer to the Holy Grail of Data?
58% agree their bank has “one version of the truth.”
There was virtually no difference between larger and smaller banks.
We saw significant increase over last year, when 47% agreed.
Overall, responses to “agree or disagree” questions reflect stronger alignment and support for higher confidence in data quality.
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.20April 26, 2018
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The Final Word?Data Quality is “Good Enough”
In‐house resources are insufficient for initiatives.
85% say data quality investment was increased over prior year.
Nevertheless, 56% say investment still is not enough.
But data quality is “good enough.”
[Q:32] Our bank has sufficient resources available in‐house for our data quality initiatives
[Q:32] Our bank’s commercial lending lines of business accept and fulfill responsibility as the “first line of defense” for data quality
[Q:32] Our bank now devotes sufficient investment to data quality improvement
[Q:32] On the whole our bank’s data quality is good enough to satisfy both regulatory requirements and customer expectations
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.21April 26, 2018
Achieving Success through Execution
Data Quality: The Journey Continues ForwardImprovement Success Begets Confidence
Key Findings Showed sustained higher confidence
in data quality. There was near‐universal recognition
as a strategic priority. Chief Data Officers are becoming more
prevalent and with broader responsibilities.
Data quality in the credit process is more localized but accountability and validation remain scattered.
Improvement initiatives are showing noteworthy and sustainable results.
Despite chronic technology and operations challenges, banks report data reports are fully adequate and data quality is “good enough.”
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.22April 26, 2018
Achieving Success through Execution
Some Questions for Next Year and the FutureWhat Does “Good Enough” Data Quality Really Mean?
How do banks actually use data? Is data quality a “value” or a
“capability?” How will banks transition to data
analytics and artificial intelligence?
Will robotics be used to address the chronic technology challenges?
Can data quality become a competitive differentiator? What will that really mean?
Will “good enough” remain good enough?
[Q:7] Our bank has been able to monetize data quality via new products or services
[Q:7] Data quality is a differentiator at our bank
©2018 The Risk Management Association and Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.23April 26, 2018
Achieving Success through Execution
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April 26, 2017©2018 Automated Financial Systems, Inc. All Rights Reserved. Confidential & Proprietary.
RMA/AFS Data Quality Survey For Further Information Contact
Lee Kidder, Director, AFS – lkidder@afsvision.com
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