Rate the availability of true machine interfaces, rather than screens built for humans, across the third-party systems the agents must use

The mean rating was 4.6 and the median 5.0 (n = 802 respondents). Ratings of 3 or below, the distressed range, made up 29.80% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.63, 95% CI −0.71 to −0.52, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.71, 95% CI 0.60 to 0.80, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.88, 95% CI 0.84 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.72 to −0.60, p < 0.001 with standard errors clustered by engagement, n = 754 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 34.07, Holm p < 0.001, omega squared 0.378), respondent role (Welch F 8.66, Holm p = 0.002, omega squared 0.018) and vendor type (Welch F 3.24, Holm p = 0.038, omega squared 0.015).

Of the 60 written responses, nearly all from C198, the comment field on the third-party ecosystem, 49 were negative and 11 positive, and 36 carried the strongest negative score of −10. Every negative response described a third-party system with no machine interface, so that the agent clicked through user interfaces built for people or a person moved the data by hand. Engagements with a negative written response were canceled more often, a more negative engagement mean written score came with cancellation, and respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 239 of the 802 answers (29.80%). The engagement mean fell in the distressed range in 40 of 150 engagements.

The primary responses numbered 56, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” The remaining 4 responses, 6.67% of the total, were secondary and came from 3 other questions. That secondary share ranked 49th from the highest among the 71 Part 2 indicators with 30 or more written responses, against a median of 18.92%. Respondents who wrote both a primary and a secondary response numbered 0. Negative responses made up 80.36% of primary responses (45 of 56) and 4 of the 4 secondary responses.

Fourteen responses described data moved by hand between a provider's user interface and the agent: counterparty ratings copied from a bureau display every evening, correspondent bank files downloaded and entered each morning, threat indicators copied into a sheet, lab reports typed in from scans, verification reports saved by one person into a shared folder for the agents to collect, and a regulatory return typed into a submission portal every time. One respondent put the cost at two hours of every evening, uncounted, while the queue looked fine from the outside. Eight responses described agents driving user interfaces that broke whenever the provider moved a button, a column or a page, on statutory portals, market data terminals, network portals and an HR portal.

Some respondents described a person who had to remain in the loop: a certification portal built for a person holding a token, so that an engineer sat with every run, and month-end runs on a slow fund administration portal that two seniors watched and re-ran by hand for three months. Five responses raised licenses written for named human users. Such licenses did not cover an agent, and no one had resolved the question. The eleven positive responses described documented interfaces that worked as described, or interfaces opened during the engagement, with three responses noting a move from scraping pages to a real interface, while placing their concerns in dirty data, licensing, missing sandbox keys or fallback.

Of the 60 responses, 49 (81.67%, 95% CI 71.19% to 90.91%) were negative and 36 (60.00%) carried the strongest negative score of −10. Positive responses numbered 11 (18.33%), and no response was neutral. The mean written score was −6.0 (95% CI −7.4 to −4.4, n = 60 responses), and the median was −10.0.

Of the 60 responses, 58 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.59, 95% CI 0.36 to 0.76, p < 0.001, n = 58 responses). The written responses corroborated the rating. Of the 47 negative responses with a rating, 31 (65.96%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 57, with a mean rating of 3.4. The 745 respondents who rated the indicator without writing about it had a mean of 4.7. The difference, −1.3 (95% CI −2.0 to −0.6), excluded zero. Ratings in the distressed range made up 57.89% of the ratings from respondents who wrote (33 of 57) and 27.65% of the ratings from respondents who did not (206 of 745).

The respondents who wrote came disproportionately from the distressed end of the scale. The written evidence therefore gives more weight to the respondents who rated the indicator worst.

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 34.07, Holm p < 0.001, omega squared 0.378). Active engagements in Production rated highest (mean 5.6, n = 100 respondents) and canceled engagements lowest (mean 3.1, n = 299 respondents). Ratings also differed by respondent role (Welch F 8.66, Holm p = 0.002, omega squared 0.018). Portfolio directors rated highest (mean 5.9, n = 31 respondents) and individual contributors lowest (mean 4.5, n = 619 respondents). Ratings also differed by vendor type (Welch F 3.24, Holm p = 0.038, omega squared 0.015). Boutique or specialist providers rated highest (mean 5.1, n = 83 respondents) and BPO specialists lowest (mean 4.1, n = 186 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment differed by engagement status and lifecycle stage across the 51 engagements with a written response (Kruskal-Wallis H 9.00, df 3, p = 0.029). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −8.2, 30 responses) and responses from active engagements in Build-Out the least negative (mean −3.1, 13 responses). Responses from active engagements in Production (4) are reported as counts only.

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 42, engagement managers wrote 16 and portfolio directors wrote 2 responses.

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 = −0.63, 95% CI −0.71 to −0.52, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twelve 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 = 0.71, 95% CI 0.60 to 0.80, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in fourteen 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 = 0.88, 95% CI 0.84 to 0.91, 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 = 754 respondents). The association held after Holm correction in thirty-seven of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 108 other engagements, 99 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 42 engagements with a negative response, 24 (57.14%) were canceled, against 32 of the 108 other engagements (29.63%). The difference reached significance (Fisher exact p = 0.003). Customer satisfaction among active engagements did not differ between the two groups (mean 61.00% on 18 engagements against 64.83% on 76, Welch t −1.32, p = 0.199).

Two correlations test written sentiment directly. Across the 51 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.37, p = 0.007). Across 58 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.54, p < 0.001). Of the four comparisons, three 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 cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.708).

Answers clustered within engagements (ICC 0.65). Clustering reduced the 802 respondent answers to the equivalent of about 209 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 0.88 to 0.88.

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, 81.67%, ranked 33rd from the highest, against a median of 80.82%. Its mean written score, −6.0, ranked 14th 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 17 of the 60 responses (28.33%). C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.” (15 responses), C187, “Rate the stability of the third-party APIs and data structures the agents depend on, from one provider release to the next.” (11 responses), C195, “Rate how well third-party licenses on this engagement cover work done by agents rather than named human users.” (8 responses) and C188, “Rate the quality of the data arriving from third-party feeds, judged by whether agents can use it without human cleanup.” (6 responses) followed.

The most frequent theme was “Third-party work done by hand”, in 23 responses (38.33%). The second was “Detection only by failure”, in 12 responses.

Nearly three in ten of all ratings of the availability of true machine interfaces fell in the distressed range (239 of 802 answers, 29.80%). 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 = 299 respondents).

The written evidence shows that where a provider offered only a user interface built for people, the agent either inherited its fragility or needed a person to feed it. In both cases the automation the financial institution paid for depended on manual work that its own measures did not show. Written sentiment was associated with cancellation in both engagement-level tests and with intent to leave.

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 (60 responses on 51 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.