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data-journey-building-strong-analytical-practices
4-step-iterative-process-building-a-relevant-analytics-practice
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top-chatbot-technologies
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lowest-and-highest-net-income-per-branch
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questions-you-should-ask-before-investing-in-data-warehouse
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all-about-membership-analytics
top-fraud-management-technologies
getting-started-with-your-data-analytics-journey
explore-vizualization-for-credit-unions
investment-in-website-personalization-technologies
data-analytics-supporting-cu-s-first-member-philosophy
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new-frontier-in-customer-experience-management
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top-kpis-for-online-banking
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tips-and-tricks-to-a-successful-bi-program
kpis-for-credit-card-business
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is-your-culture-ready-for-data-analytics
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turning-transaction-data-into-a-goldmine-a-becu-case-study
call-for-presentation-for-2019-credit-union-analytics-summit-is-n
top-10-keys-to-successful-data-analytics-practice
credit-union-chooses-accountscore-for-open-banking-transaction-da
how-much-do-you-spend-to-serve-a-customer
marketing-automation-technologies-for-credit-union
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dataweb-content-management-technologies-for-credit-unions
efficiency-ratio
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data-warehousing-software-for-banks
customer-experience-software
the-best-kept-secret-for-credit-union-data-analytics
mark-sievewright-on-technology-trends
naveen-jain-on-credit-union-analytics-summit-2018
why-analytics-doesn-t-make-a-difference-by-gary-angel
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update-from-naveen
error-resolution
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who-are-your-most-valuable-members-part-1
how-alexa-can-help-your-credit-union
top-10-kpis-for-measuring-retail-channel-performance
how-much-is-too-much-personalization
top-10-kpis-for-measuring-contact-center-efficiency
pressure-on-margins-for-auto-loans-indirect-auto-loans-declining
best-business-intelligence-technologies-for-credit-unions
establishing-a-thriving-data-analytics-practice-is-a-journey
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modelling-alternatives-for-cecl-a-deep-future-analytics-study
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data-analytics-opportunities-in-credit-union-business
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machine-learning-delivers-great-consumer-experiences
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strategic-agility
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cus-with-highest-and-lowest-loan-grants-per-member-june-2020
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highest-and-lowest-marketing-expense-per-member-june-2020
the-amazon-lending-experience
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4-step-iterative-process-building-a-relevant-analytics-practice
data-journey-building-strong-analytical-practices
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a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
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are-you-accurately-interpreting-your-kpi
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uncover-the-power-of-proxy-kpis
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bi-department-structure
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getting-executive-attention-for-your-data-analytics-program
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how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai
how-better-data-and-behavioral-biometrics-can-help-credit-unions-
harnessing-the-power-of-data-in-credit-unions
leveraging-third-party-data-a-strategic-guide-for-credit-unions
unlocking-member-insights-how-cus-can-leverage-third-party-data
enhancing-customer-experience-through-third-party-data
third-party-data-integration-techniques-and-technologies
the-future-of-lending-third-party-data-role-in-credit-decisioning
how-third-party-information-shapes-cu-strategies
using-data-to-improve-access-to-credit-for-low-income-members
designing-financial-products-for-low-income-members-using-data
measuring-and-enhancing-the-impact-of-support-programs
data-governance-why-selling-internally-is-important
selling-data-governance-in-your-credit-union
building-a-business-case-and-engaging-stakeholders
creating-a-data-governance-roadmap-and-executing-it
measuring-and-demonstrating-the-impact-of-data-governance
sustaining-momentum-keeping-data-governance-a-priority
overcoming-challenges-in-transaction-data-analysis-credit-unions
empowering-members-through-transaction-data
how-credit-unions-leverage-transaction-data-best-practices
unlocking-financial-independence-the-power-of-transaction-data
the-power-of-transaction-data-enrichment
avoid-financial-reputation-and-member-trust-issues
introduction-to-model-risk-management
week-1-mrm-a-practitioner-s-approach
week-2-guide-to-identifying-and-maintaining-models
survey-insights-navigating-mrm-in-credit-unions
week-3-application-of-mrm-insights-to-sound-model-development-eff
unlocking-the-secrets-to-attracting-gen-y-and-z
creating-a-seamless-member-experience-for-gen-y-and-gen-z
data-analytics-maturity-assessment-report
marketing-to-gen-y-and-z-strategies-that-work-for-credit-unions
the-imperative-of-engaging-millennials-and-gen-z
cu-build-lasting-relationships-with-gen-z-financial-literacy
how-social-responsibility-drives-gen-z-membership
loyalty-programs-that-work-keeping-gen-y-and-z-members-engaged
insights-on-engaging-millennials-and-gen-z-at-credit-union
ai-driven-member-experience
streamlining-operations-with-ai
innovation-and-member-inclusion-in-ai-credit-risk-models
ai-risk-management-enhancing-fraud-detection-and-cybersecurity
how-ai-is-transforming-data-analytics-for-credit-union
overcoming-ai-adoption-challenges-in-credit-unions
the-state-of-ai-in-credit-unions-survey-insights
creating-a-culture-of-innovation
building-the-foundation
closing-the-talent-gap
Comments
Hi Naveen,
Just to make sure that we haven't gone too deep with the CU'that we are in talks with but below are our observation:
External Sources:
Salesforce Call Details (this involves some NLP techniques to understand the sentiments)
Surveys (both on the web and in the branch)
MCIF (I assume many CU's have procured it but not used to its potential)
Census/ProximityOne (a placeholder for MCIF but not that extensive)
Census Shapefiles (to understand the drive time for members). This involves some geo-spatial activities
Credit Bureau (FICO...to understand what other institutions this member has loans)
Social Media
There is a rich trove of information in member transactions and member 360 view (MDM) can provide lot of insights including their lifestyle, interest, and others.
Thanks,
Raghu
Can you share specific low hanging use-cases where you have used specific third party data (in CU/Banking context) with quantifiable success?
Naveen,
We are still in the preliminary stages with one of the CU's we have been working with to whom we have provided our strategy and roadmap. So, I can't comment much with respect to direct CU and Bank related experience. However, We are the engineering team for Discern, a capital market investment tech firm and responsible for designing, architecting, and implementing their Banks Market Analytics that used both public and private data sources as below:
Click here for Discern Bank Analytics
1. FDIC Balance sheet & SEC Filings for peer comparison
2. FDIC: Bank Institution and branch locations are processed that contains deposit data and aggregated with Demographics data using branch zip code with census proximity to find out household income, total population, etc. and perform weighted and un-weighted rolling averages. Developed proprietary GeoMap data model to create hierarchy that consists from country, state down to county, grid block, and place
3. Census shapefiles: For geo-location and identifying Bank's market share
4. S&P CapIQ and SNL Banks (private sources): These are very proprietary and can't comment much
With respect to CU, I believe data from core systems and member transaction data can provide very valuable insights that many CU's haven't explored (pardon me If I'm wrong). I have list of use cases and few PoC's that I can share but not sure how to upload it here (its a PPT).
We did identify Member call details as one of the important external data followed by survey data to perform some PoC to understand Member Satisfaction
Thanks,
Raghavan
Thanks Raghavan. I will be very interested in the use-cases. You should be able to upload the ppt to the original blog post.
Naveen, I just posted an article on LinkedIn about the use cases using machine learning algorithms.
https://www.linkedin.com/pulse/deep-insights-credit-union-members-d...
The below are other use cases based on Loan Application and Wallet Share analytics. I'm sure most of CU's have these analytics but the idea is the normalization process that goes with using CUFX to create Member 360 from different sources.
https://public.tableau.com/profile/trtest#!/vizhome/ApplicationAnal...
https://public.tableau.com/profile/trtest#!/vizhome/WalletShare-MLb...
Note: Since you are well known SME in this industry, it would be great if you provide your feedback on our thought process. We are a data company and are not experts in any industry but get acquainted fast about specific domain data and their features. So, your feedback is very valuable.
https://public.tableau.com/profile/trtest#!/vizhome/ApplicationAnal...
Thanks for sharing. Let me review and get back to you.
Hi Raghavan, what external data sources are you getting the data from? Which one do you find useful?