Credit Union Data Analytics: Risk Awareness

As we continue the Credit Union 2.0 “Almost 99 Small Data Hacks for Credit Unions – guide” series, today we are covering risk items for internal consumption only. While many of our hacks are targeted at proactive marketing, these hacks are key insights you can gather internally from your member data.

This is the fifth post in a 9 part series. If you can’t wait for next week and want the full “Almost 99 Credit Union Small Data Hacks Guide” click here!

Credit Union 2.0 – A Guide for Helping Credit Unions Compete in the Digital Age covers in depth both big and small data for credit unions. There are six types of data that your Credit Union should be aware of:

  1. Digital Analytics – Desire
  2. Profitability – Fit
  3. Wallet Share – Depth
  4. Transaction – Triggers
  5. Design Data – Predictive
  6. Execution – IFTT (if this than that)

This list is by no means comprehensive and Credit Union 2.0 does not offer any compliance advice for any state or federal laws on the impact of using this information.

Risk Analytic Where to find it? Potential Conclusions
Minimum Payments Credit Card Data If you see members reducing their payments over time on their credit card payments, this may indicate an increased risk of default or challenging cash flow situation for your member.
Member locks themselves out Online Banking Proactively reach out if the member locks themselves out. Call their phone on record, don’t just wait for the member to call in. A frustrated member will appreciate this.
Duplicate Addresses Monthly Report of more than one member with the same address Could indicate a stolen identity
Duplicate SSNs Monthly Report of more than one member with the same SSN Could indicate a stolen identity
Duplicate Driver’s License Monthly Report of more than one member with same Driver’s License Could indicate a stolen identity
Delinquent Loans by Indirect Lender Loan System Could indicate an indirect auto dealer encouraging members to lie about income
Loans without payments Monthly report of loans with new payments Possible indication of fraudulent loans
Loans with payments made at the teller line Loan payments by teller/channel Possible indication of fraudulent activity
Loan types and dollar consistencies by MSR Loan applications by channel and person Possible indication of fraudulent activity

 

Have an idea or risk related data algorithm? Submit it to the Credit Union 2.0 team today by emailing us at [email protected] and help us improve this post!

Want to learn more about how your fellow Credit Union leaders are using data? We invite you to join our Credit Union 2.0 Strategist Group where over one thousand industry leaders comment on new news and trends while sharing and learning from one another.

This is the fifth post in a 9 part series. If you can’t wait for next week and want the full “Almost 99 Credit Union Small Data Hacks Guide” click here!

In case you missed it:

Click here for part one of the data analytics series.

Click here for part two of the data analytics series.

Click here for part three of the data analytics series.

Click here for part four of the data analytics series.