By Geordie Connell, Solutions Consultant
Why Digital Transformation Is Just the Starting Line
When institutions don’t regularly review their operational data or the configurations that influence those metrics, key KPIs often fall short of expectations. Whether the issue is higher days to resolution, lower dollars recovered, or elevated averages of manual tasks per dispute, you can guarantee that agents are working assignments that could be automated, costing valuable time better spent on higher-impact issues.
Those who treat their platform as a static tool are leaving efficiency on the table. By optimizing on a consistent cadence, you not only take an honest look at performance and areas for improvement but also take advantage of newly available features your platform may now offer. This further fine-tunes processes and strengthens the bridge of trust between institution and vendor.
Think of ROI as a barbell-shaped graph of Days to Resolution. The bulk of work we want to automate lives on the two ends: the low-dollar disputes that may have been written off anyway, and the more complex disputes headed to recovery. When we thin out that middle section so agents have the time and focus to handle these disputes appropriately, while also keeping the overall “bar” short by getting recoverable disputes out the door quickly, we’ve succeeded in optimizing, at least for now.
Case Study 1: Consulting on Efficiency Gains
For one client, we analyzed their top 10 completed assignments per month over a six-month period, along with claim and dispute outcome data for the same timeframe. The data around the top 10 tasks revealed two insights:
- Where they were spending time unnecessarily (where the claim and dispute data came into play), and
- Where we could hold ourselves accountable.
For the first insight, the findings aligned with what I mentioned above: presenting options for different thresholds and the savings recovered by resolving those disputes. For the second, it was about working with the client to identify how we could enhance the platform and improve implementation to further elevate the user experience, efficiency, and effectiveness for end users.
Case Study 2: Optimizing Autopay Settings
In another project, I recommended that a client review dispute outcomes for those under one or both of their current thresholds, as well as for those above either or both autopay thresholds. When evaluating these outcomes, they reviewed the total value of those disputes compared to the amount paid to the accountholder plus any merchant credits identified. The difference represented the amount denied by agents, and effectively, the amount saved.
My advice was to look at the savings amount across all disputes (not just those denied) and determine whether that ratio was acceptable. By repeating this process with various threshold combinations, the client could confidently land on an autopay amount that balanced efficiency with savings.
Case Study 3: Improving Member Satisfaction
I worked with a client concerned about an increase in their reopen rate. When a member challenges a denial, it signals dissatisfaction, and internally, it may indicate a need for investigator coaching. We reviewed the percentages of reopens accepted and declined, and established a baseline for what constitutes a valid reopen request. We discovered several premature denials, which made the coaching path clear, but there was also a sense of urgency in addressing the affected cases to regain member trust.
We also evaluated system settings to improve clarity. Specifically, we reviewed deny justifications (the verbiage used in denial letters). There were opportunities to add more detail to fraud-related denials, such as noting when a disputed transaction was stale-dated or including merchant information to encourage members to work directly with the merchant. These small improvements have a big impact, reducing callbacks and rework while improving overall member interactions.
Digital transformation may get institutions to the starting line, but meaningful results come from continuous optimization. When teams regularly review their data, refine configurations, and embrace new platform capabilities, they create a cycle of improvement that strengthens efficiency, accuracy, and customer trust. The institutions that treat optimization as an ongoing practice—not a one-time project—are the ones that see lasting gains across performance, cost savings, and member satisfaction.
