Information Asset Management in Financial Institutions: How Much Is It Really Costing You?
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It is hard to imagine that anyone would invest $1.00 but then lose $100 in the process; that would be considered an awful investment decision. It’s widely accepted that investments should translate to positive returns. The value of an investment is determined by the quality of the information provided, demonstratable consistency, and context in how it should be leveraged. This is how intrinsic trust and positive decision making is formed. Data is a type of currency that banks invest in significantly, to transform into information assets. These information assets support decision-making, ensure compliance with regulations, control costs, optimize customer engagement, provide a seamless multi-channel, and positively impact a positive customer experience. The cost of investing in bad data quality is both escalating and profound. For example, if the cost to capture a single data element is $1.00. But, if that data element is incorrect, it will cost 10x the original costs to fix, test and deploy corrected data. After the data is repaired, it will cost well over 100x the initial value to identify, correct, and shift decisions based on inaccurate information assets. Join this on-demand session to learn more about how bad data, inconsistent data, and a lack of context is costing financial institutions more than they realize.
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Information Asset Management in Financial Institutions: How Much Is It Really Costing You?
- 1. Information Asset Management in Financial Institutions How much is it really costing you? Chuck Kane Jeff Nelson
- 2. Strategic Data Investments -100000 -80000 -60000 -40000 -20000 0 20000 40000 60000 80000 100000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2 -100000 -80000 -60000 -40000 -20000 0 20000 40000 60000 80000 100000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Proactive Data Management As adoption scales up and decisions made on data scale up, so does the value of the insights over time Reactive Data Management As adoption scales up and decisions made on data scale up, poor data quality erodes value and trust there for value
- 3. Initiatives Dependent on Data Channel Optimization Marketing Insights Geographic Leadership Customer Transformation Digital Focus Paperless Interactions Reduce Risk / Enhance Compliance Cost Reduction Compliance with Regulations AML KYC/KYCC Credit Risk Charge-Offs Financial Crimes & Compliance Artificial Intelligence Machine Learning Business Intelligence Cloud Warehousing / Enrichment Analytical Insights 3
- 4. Poor data investments – negative returns -100000 -80000 -60000 -40000 -20000 0 20000 40000 60000 80000 100000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 4 -100000 -80000 -60000 -40000 -20000 0 20000 40000 60000 80000 100000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Proactive Data Management Reactive Data Management
- 5. $3.5B Annual Innovation Spend $10B Typical Top 5 Bank – Annual Technology Spend $2.8B Annual Compliance Spend Big Banks – Big Numbers 5
- 6. $520m Annual Innovation Spend $2.0B Typical Regional Bank – Annual Technology Spend $460m Annual Compliance Spend Regional Banks – Big Numbers 6
- 7. $156m Annual Innovation Spend $600m Typical Regional Bank – Annual Technology Spend $138m Annual Compliance Spend Small Region / Local Banks – Big Numbers 7
- 8. Small Regional / Local Banks 8 -1200 -700 -200 300 800 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Original Innovation Budget – $156m Data Remediation Insights Remediation Replacement Costs / Remediation - $1.1B Continued Remediation Lack of Trust in Data
- 9. Poor quality data has real world effects News Release 2018-112 October 23, 2018 OCC Assesses $100 Million Civil Money Penalty Against Capital One “The methodology should ensure that the relationships are viewed holistically across lines of business” 9 HSBC to pay $1.9 billion U.S. fine in money-laundering case It also said that as part of the overhaul of its controls, it has launched a global review of its “Know Your Customer” files, which will cost an estimated $700 million over five years. Deutsche fined $630 million for failures over Russian money-laundering At least 12 entities were involved in these suspicious trading activities, and the entities were closely related, linked, for example, by common beneficial owners, management or agents.
- 10. Global recognition of data challenges 10 https://www.waterstechnology.com/data-management/4393246/hsbc-data-chief-urges- better-industry-wide-data-governance “Data is a living breathing thing and never perfect, but when it stops someone from doing something, it becomes an issue” David Gleason, Chief Data Officers, Deutsche Bank Corporate Bank https://flow.db.com/more/technology/driving-data-qualty “Democratising data and a data driven culture are about upskilling and making data a core part of everyone’s role, not just the data scientists” Tom Jenkins, Group Head, Data Quality & Governance, Deutsche Bank https://flow.db.com/more/technology/driving-data-qualty
- 11. Poor data has significant downstream effects 11 Data Integrity & Quality Artificial Intelligence Machine Learning Advanced Analytics Screening Credit Risk Fraud Detection Marketing Insights Charge-Offs Global Payments Transaction Monitoring Big Data Cloud Relational Application Text Based
- 12. Data Integrity – Invest with Confidence
- 13. ACCURACY CONSISTENCY CONTEXT On-premises, Hybrid Cloud & Cloud Insurance Cloud Data Warehousing Retail Banking & Financial IT Operations Solutions Customer 360 Data Integration Data Quality Location Intelligence Data Enrichment Foundation INTEROPERABLE | INTELLIGENT | DYNAMIC Telco Government Healthcare Data Governance 13
- 14. MDM, Data Quality and Data Integrity Capabilities Data Movement Web Services API Source Integration Integrated Security Que Management Scalable Processing Federation & Virtualization Unstructured Data Discovery Profiling Scorecards Quality Context Analytics & Reporting Deployment Enrichment Business Glossary Modeling Lineage Parse & Normalize Standardize Match & Link Supervised Learning Deduplication Business Intelligence Self Service Insights Iterative Actionable Machine Learning Graph Analytics Data Science Graph Spatial Analysis Cloud On Premise SaaS/PaaS Company Person & Social Location
- 15. Data Integrity – Use Cases
- 16. Precisely Case Studies US Super regional bank with 18M+ customer accounts • Challenge with lack of holistic view of customer & screening alert volume • Challenge with disparate data systems and quality of information, • Current “single view” was built for marketing purposes • Consent Order & Cease and Desist • Holistic view of customer throughout the enterprise, Investigative efficiency over 35% in less than 6 months US National bank with 45M+ customers • Challenge with alert volume and false negatives • Consent Order resolved in part by Precisely technology • Implementation around 6 weeks • Reduction in alerts by 58% US Based Global bank with 100M+ customers • Regulatory requirement to identify all high risk customers throughout the enterprise in all systems • Currently man power trying to solve manually • Automation to provide efficiency worth $10’s of millions of Dollars
- 17. Precisely Case Studies US Based bank with 45M + customers • Almost $2B in charge-offs annually • Only 5% of charge-offs were previously attributed to fraud • No process created to understand loan over exposure • Identified over 28% of charge-offs related to loan over exposure (In first pass - additional PII data components being added for additional uplift) • Significant lift in identification of synthetic identities and stolen identities as well US Regional bank with $120+ B in assets • Challenge with KYC Consent Order • 60 data sources, consolidated with resolved entities, synthetic identities and relationships • Implementation - 8 weeks US Super regional bank with 15M+ customers • Challenge with lack of holistic view of customer, spent $M’s with failed MDM • Challenge with disparate data systems and data quality of information • Accessed, cleansed, matched account holders to show holistic view of customer that had never been seen before (Only exact match on Name, Address, Govt ID and D.O.B used previously) US Based Global bank with 500M+ accounts • Global challenge with having a single view of customer globally • Challenge with data quality, data privacy • Significant reduction in over (6%) and under matching (8.1%)
- 18. Thank You
Editor's Notes
- This diagram shows at a high level what I mean when I saw the suite is modular and interoperable. In the center of the diagram, you can see the core competencies of the suite – data integration, data quality, location intelligence, and data enrichment. Each of those offers unique capabilities that solve data challenges – capabilities like data profiling, spatial visualization, integration of data into a cloud data warehouse, and more. You can choose just the capabilities you need to solve your challenges. Beneath those capabilities you see a foundation that integrate the suite’s capabilities when they are implemented together. As future releases of the suite are delivered, you’ll see additional work done on the foundation to enhance interoperability, expand the way it applies machine learning to tough data integrity challenges, and dynamically response to its environment – taking the suite’s ability to deliver data integrity to the next level. The suite integrates at all levels with today’s leading data technology providers. Our certified integrations and strategic relationships with partners like Collibra, Databricks, and Snowflake ensure more than just coexistence, but technical and workflow integration with the market-leading technology your business runs on today. And the suite offers APIs that enable developers to expand on its capabilities. So, your in-house team can build solutions to your unique requirements. But let’s talk a bit more about the suite’s core capabilities.