High Level CU Business Goals:

  • Generate & automate reports from the data that are in different silo systems.
  • Enable business analysts and other stakeholders find the needed data and perform necessary data wrangling and reports with minimal engineering involvement.
  • Create 360° view of member with master data management.
  • Gain insights into data by correlating with external data sources and find patterns.
  • Improve key business metrics – loan growth, member satisfaction, profitability

Does this ring bell? Check out our Journey with one of the CU's during our initiation phase

Big Data Strategy & Roadmap - Our Journey with a Financial Institution

Email me when people comment –

You need to be a member of Credit Union Big Data Analytics Community and Summit - CULytics to add comments!

Join Credit Union Big Data Analytics Community and Summit - CULytics


  • Vendor

    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 ( 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.


    • CU Employee CULytics Founder

      Can you share specific low hanging use-cases where you have used specific third party data (in CU/Banking context) with quantifiable success?

    • Vendor


      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


      DISCERN, the leader in personalized platform as a service (PaaS) for financial insight, is developing state-of-the-art web applications to dramat…
    • CU Employee CULytics Founder

      Thanks Raghavan. I will be very interested in the use-cases. You should be able to upload the ppt to the original blog post.

    • Vendor

      Naveen, I just posted an article on LinkedIn about the use cases using machine learning algorithms.

      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.!/vizhome/ApplicationAnal...!/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.!/vizhome/ApplicationAnal...

    • CU Employee CULytics Founder

      Thanks for sharing. Let me review and get back to you.

  • CU Employee CULytics Founder

    Hi Raghavan, what external data sources are you getting the data from? Which one do you find useful?

This reply was deleted.