Rate how completely the engagement knows where humans used to quietly fix third-party data and interface problems before agents took over

The mean rating was 4.6 and the median 5.0 (n = 792 respondents). Ratings of 3 or below, the distressed range, made up 33.08% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.66, 95% CI −0.74 to −0.56, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.61, 95% CI 0.47 to 0.73, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.82 to 0.90, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.71 to −0.60, p < 0.001 with standard errors clustered by engagement, n = 746 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 42.51, Holm p < 0.001, omega squared 0.421) and respondent role (Welch F 11.70, Holm p < 0.001, omega squared 0.024).

All 21 written responses came from C198, the comment field on the third-party ecosystem. Twenty were negative and one positive, and fifteen carried the strongest negative score of −10. Respondents said the quiet manual fixes that once absorbed third-party problems had never been written down, that the people who made them had left, and that each fix was being rediscovered when something broke. No written outcome test reached significance.

Ratings of 3 or below, the distressed range, accounted for 262 of the 792 answers (33.08%). The engagement mean fell in the distressed range in 39 of 150 engagements.

The primary responses numbered 21, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” No response from any other question addressed the indicator, so every response was primary.

Seventeen responses said the quiet fixes made before the agents arrived had never been written down or captured. The examples were specific: payroll deduction corrections held in one supervisor's head for eleven years, custodian quirks such as a fee accrual sent a day late and a restatement a month after the fact, name matching on a bureau file, date formats corrected by hand every morning on a custody feed, stale accounts checked by hand before an agent took the step over. Five said the people who held the fixes had left, or that the team would find out only once they did.

Three responses said the fixes were being rediscovered one at a time when something broke, one respondent noting that each became known only at the moment it broke a valuation. Four said the question could not be answered on a scale: if the fixes had been known they would not have been quiet, and a middling rating recorded ignorance rather than a capability. The single positive response said the old team's manual checks, at least, had been written down.

Of the 21 responses, 20 (95.24%, 95% CI 85.71% to 100.00%) were negative and 15 (71.43%) carried the strongest negative score of −10. Positive responses numbered 1 (4.76%), and no response was neutral. The mean written score was −8.1 (95% CI −9.5 to −6.4, n = 21 responses), and the median was −10.0.

Of the 21 responses, 20 came from respondents who also rated the indicator. Among them, the written score did not track the rating (Spearman rho 0.25, 95% CI −0.14 to 0.59, p = 0.293, n = 20 responses). Of the 19 negative responses with a rating, 8 (42.11%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 20, with a mean rating of 4.4. The 772 respondents who rated the indicator without writing about it had a mean of 4.6. The difference, −0.2 (95% CI −1.3 to 0.8), included zero. Ratings in the distressed range made up 40.00% of the ratings from respondents who wrote (8 of 20) and 32.90% of the ratings from respondents who did not (254 of 772).

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. Where an expected count fell below 5, the test of the distressed share used Fisher's exact test for a two-by-two table, marked exact, or a Monte Carlo test, marked simulated. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 42.51, Holm p < 0.001, omega squared 0.421). Active engagements in Transition/Cut-Over rated highest (mean 5.7, n = 233 respondents) and canceled engagements lowest (mean 3.1, n = 305 respondents). Ratings also differed by respondent role (Welch F 11.70, Holm p < 0.001, omega squared 0.024). Portfolio directors rated highest (mean 6.0, n = 30 respondents) and individual contributors lowest (mean 4.4, n = 610 respondents). Outsourcing model, institution type, process family, class of work and vendor type showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 21 engagements with a written response (Kruskal-Wallis H 0.96, df 3, p = 0.812). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (7), active engagements in Transition/Cut-Over (6), active engagements in Production (2) and canceled engagements (6).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 10, engagement managers wrote 10 and portfolio directors wrote 1 responses, reported as counts only.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with less cancellation (r = −0.66, 95% CI −0.74 to −0.56, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in fourteen of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with higher customer satisfaction (r = 0.61, 95% CI 0.47 to 0.73, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in thirteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator is one of the rated indicators. Each comparison therefore uses the mean recomputed without it.

Higher ratings came with a higher engagement indicator mean (r = 0.87, 95% CI 0.82 to 0.90, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported lower intent to leave (r = −0.66, 95% CI −0.70 to −0.62, Holm p < 0.001, n = 746 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 130 other engagements, 129 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 20 engagements with a negative response, 6 (30.00%) were canceled, against 50 of the 130 other engagements (38.46%). The difference did not reach significance (Fisher exact p = 0.621). Customer satisfaction among active engagements did not differ between the two groups (mean 62.50% on 14 engagements against 64.38% on 80, Welch t −0.58, p = 0.569).

Two correlations test written sentiment directly. Across the 21 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.19, p = 0.415). Across 21 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho 0.07, p = 0.761). Of the four comparisons, none reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, five of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with customer satisfaction the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.937).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 792 respondent answers to the equivalent of about 212 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence. The indicator is one of the rated indicators, and the engagement indicator mean was recomputed without it. The correction changed r by 0.00, from 0.87 to 0.87.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator followed the study's typical pattern on all four measures, and no deviation held after Holm correction.

Among the 71 Part 2 indicators with 30 or more written responses, the indicator's share of negative responses, 95.24%, ranked 2nd from the highest, against a median of 80.82%. Its mean written score, −8.1, ranked 3rd from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.”, in 6 of the 21 responses (28.57%). C188, “Rate the quality of the data arriving from third-party feeds, judged by whether agents can use it without human cleanup.” (5 responses), C021, “How likely are members of your current team to leave their jobs in the next 12 months?” (4 responses), C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.” (4 responses) and C187, “Rate the stability of the third-party APIs and data structures the agents depend on, from one provider release to the next.” (4 responses) followed.

The most frequent theme was “The scale or question does not fit”, in 5 responses (23.81%). The second was “Third-party work done by hand”, in 4 responses.

About a third of all ratings of how completely the engagement knew where people had quietly fixed third-party problems fell in the distressed range (262 of 792 answers, 33.08%). The rating was associated with all four study measures, and every association held under each of the 22 robustness checks. Canceled engagements rated far below the three active stages (mean 3.1, n = 305 respondents).

The written evidence is near unanimous, with all but one response negative, and it describes a control that was never formal: people who corrected feeds by habit, whose corrections no process recorded. Automation removed the habit without recording the correction, and the financial institution learns of each one when a result is wrong.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Customer management executives gave 2 answers, a base of anecdote strength.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (21 responses on 21 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.

The written responses number fewer than 30, so their shares and the written-sentiment tests rest on a small base.