Fraud-related Projects for 2019-2020

Have comments about these projects?

We’d love to hear your feedback, use the Comments Section below

Introduction

Beginning in 2018 CU*Answers started on a multi-year project to drive a business plan goal to move from compliance monitoring to fraud prevention as an active feature of our network.  The focus is to make the most of our data-rich environment, on the way to building new tools and businesses that can help reduce and mitigate the effects of fraud for credit unions in our network.

Why should CU*Answers get in the fraud business? Because with the comprehensive data that’s concentrated within CU*BASE, we can gain a global perspective, across multiple channels, that other vendors just can’t get.  We can also look at people, not just transactions, for a more well-rounded view of who is causing (or being victimized by) fraud.  And since CU*BASE authorizes member transactions, we’re at the point of the request, which means we are in a unique position to stop fraud before it happens.

Projects for 2019-2020

We made significant investments in learning and foundation work in 2018 and will be releasing our first software tool in the 19.05 CU*BASE release.  Here’s a quick recap of projects in the works:

Project A: Gather the Data

Find out what fraud is actually occurring. This new database will give every network credit union a centralized place to log fraud incidents. We will then gather all of these stats to form a network-wide picture of fraud affecting our network. This picture will allow credit unions to begin building block lists (see projects B and C) to stop the bleeding on specific troubled members and accounts.  Then we’ll use our new knowledge of fraud patterns we see to build tools that stop those things from occurring in the first place (see projects D and E).

Status: Project #47270 is targeted for release with the 19.05 release.

Project B: Create Block Lists

Create lists of problem members and accounts. The idea here is to give credit unions a place to log member accounts where you’ve seen fraud in the past.  Block lists will be blunt instruments, in the sense that we’ll be able to look for a SSN/TIN in the list and stop a member from accessing a particular product or type of activity altogether.

This project will build the infrastructure to allow credit unions to enter the accounts and designate which products/services (things like P2P vs. bill pay vs. wire transfers, etc.) should block activity for that account. This will include both single-channel denial-of-service blocks that simply stop a member from enrolling or using a particular feature or service altogether, and multi-channel activity attribute blocks based on a particular data element, such as country of origin, merchant name, pay to name, etc., to stop specific transactions from occurring, across various delivery channels.

Block lists give us maximum flexibility to adjust and evolve these settings over time as we learn more about what fraud is actually happening in our network.

Status: Specs in process; development targeted for 2019.

Project C: Stop Transactions for Accounts on the Block Lists

Test against block lists and stop actions from being taken.  Build new routines into various posting programs and interactive features to honor the settings configured on CU block lists. We already check for available funds or account freezes, now we’ll check against CU block lists at the same time.

For batch processes where there is already an exception mechanism in place, items can be stopped at the point of posting and funneled through the normal exceptions handling process.  In cases where there’s no batch exception process, we’ll need to adjust the specific program that either a member or a CU employee uses, either to present an error message, or prevent the account suffix from being selected or the particular feature from being launched in the first place.

Status: Specs in process; development targeted for 2019.

Project D: Create Tools to Configure Fraud Patterns

Look for specific patterns that signal fraud.  If block lists are blunt instruments, this project starts to build surgical scalpels to fine-tune which patterns of activity signify a possible fraud attempt. Flexible configurations will allow us to cover patterns related to velocity, product, out-of-the-ordinary activity, cross-channel activity, and idle activity fraud indicators.

Status: Still in the early design phase. 

Project E: Test Transactions and Stop Ones That Match Configured Fraud Patterns

Test against fraud patterns and stop incoming transactions before they post.  Build new routines to test activity against fraud pattern tests you’ve activated and automatically put accounts onto your block lists.

Status: Still in the early design phase. 

Other Future Project Ideas

Also on the drawing board are tools for flagging accounts as exceptions to the rule – an “unblock” for the block list, as it were.  And although we’re starting with transaction posting programs, we expect to eventually reach out to new account opening, applications, and other non-transactional processes.

Building New Businesses for Shared Execution

AuditLink Fraud Monitoring and Consulting Services

  • Starting in 2019 AuditLink experts will configure your CU*BASE Abnormal Activity Monitoring tools for FREE, or provide free consulting to credit unions who want to go it alone but need help getting started.  Visit the Store or contact AuditLink today!
  • Coming soon, AuditLink will begin offering new daily monitoring services to actually work the daily alerts this tool generates.  Beta CUs for the new service are being selected in February, and the new service will be announced at this year’s Leadership Conference in June.

Asterisk Intelligence Fraud Analysis Services

  • AI will also get into the act with consulting services around analyzing the fraud data you’ll be entering into your new database (see project A).  Our data analysts are experts at finding patterns and translating them into actions your credit union can take now.  Visit the Store or contact AI today!

Ultimately the goal is to build a community of users and use what we learn from those credit unions as well as vendors in the fraud space to help us build the new fraud pattern configurations (see project D above).  We hope you will join us.

Feedback

Chefs for these recipes:  Jim Vilker and Keegan Daniel

Have thoughts or ideas about these changes? We’d love to hear your feedback. Use the Comment Section below.

Updated
March 14, 2019

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