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Payroll stub and cash With‌ ‌the‌‌improvement‌ ‌in‌ ‌technology‌ ‌and‌ ‌AI,‌ ‌there‌ ‌are‌ ‌now‌‌ways‌ ‌to‌ ‌identify‌ ‌payroll‌ ‌anomalies‌ ‌using‌ ‌algorithms‌‌that‌ ‌rely‌ ‌upon‌ ‌historical‌ ‌data‌ ‌and‌ ‌not‌ ‌just‌ ‌the‌‌expertise‌ ‌and‌ ‌judgment ‌of‌ ‌payroll‌ ‌analysts.‌

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Data‌ ‌is‌ ‌the‌ ‌new‌ ‌information‌ ‌currency‌ ‌for‌‌organizations,‌ ‌where‌ ‌improved‌ ‌and‌ ‌faster‌ ‌access‌ ‌to‌‌analytics‌ ‌can‌ ‌drive‌ ‌decision-making‌ ‌by‌ ‌HR,‌ ‌which‌‌sits‌ ‌at‌ ‌the‌ ‌intersection‌ ‌of‌ ‌some‌ ‌of‌ ‌the‌ ‌most‌‌critical‌ ‌data‌ ‌concerning‌ ‌talent‌ ‌acquisition,‌ ‌benefits‌‌administration,‌ ‌employee‌ ‌communications‌ ‌and‌ ‌performance‌‌tracking.‌ ‌Leveraging‌ ‌artificial‌ ‌intelligence‌ ‌(AI)‌ ‌can‌‌bring‌ ‌greater‌ ‌power‌ ‌to‌ ‌these‌ ‌areas‌ ‌and‌ ‌help‌‌derive‌ ‌real‌ ‌insights,‌ ‌predict‌ ‌trends‌ ‌and‌ ‌identify‌‌anomalies‌ ‌that‌ ‌will‌ ‌impact‌ ‌the‌ ‌bottom‌ ‌line‌ ‌over‌‌time.‌ ‌But‌ ‌the‌ ‌opportunity‌ ‌to‌ ‌improve‌ ‌quality‌ ‌and‌‌efficiency‌ ‌is‌ ‌significant.‌ ‌ ‌

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Much‌ ‌has‌ ‌been‌ ‌written‌ ‌already‌ ‌about‌ ‌how‌ ‌AI‌ ‌can‌‌assist‌ ‌organizations‌ ‌in‌ ‌the‌ ‌recruiting‌ ‌process.‌‌Having‌ ‌historical‌ ‌data‌ ‌about‌ ‌employees'‌ ‌performance‌‌combined‌ ‌with‌ ‌having‌ ‌detailed‌ ‌requirements‌ ‌around‌ ‌the‌‌skills,‌ ‌knowledge‌ ‌and‌ ‌competencies‌ ‌needed‌ ‌for‌ ‌a‌‌role‌ ‌can‌ ‌help‌ ‌recruiters‌ ‌and‌ ‌hiring‌ ‌managers‌ ‌pick‌‌the‌ ‌candidate‌ ‌that‌ ‌is‌ ‌likely‌ ‌to‌ ‌be‌ ‌the‌ ‌most‌‌successful‌ ‌in‌ ‌the‌ ‌role.‌ ‌

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Related: Separating millennial myth from fact: Lessons usingpredictive modeling

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In‌ ‌payroll,‌ ‌teams‌ ‌have‌ ‌long‌ ‌provided‌ ‌quality‌‌assurance‌ ‌by‌ ‌running‌ ‌specific‌ ‌reports‌ ‌looking‌ ‌for‌‌potential‌ ‌errors‌ ‌in‌ ‌the‌ ‌payroll‌ ‌run.‌ ‌Those‌ ‌reports‌‌were‌ ‌based‌ ‌on‌ ‌cumulative‌ ‌payroll‌ ‌knowledge‌ ‌over‌‌time‌ ‌that‌ ‌indicated‌ ‌where‌ ‌errors‌ ‌are‌ ‌likely‌ ‌to‌‌occur.‌ ‌With‌ ‌that‌ ‌approach,‌ ‌payrolls‌ ‌were‌ ‌still‌ ‌at‌‌risk‌ ‌of‌ ‌an‌ ‌issue‌ ‌occurring‌ ‌that‌ ‌would‌ ‌not‌ ‌be‌‌detected‌ ‌by‌ ‌the‌ ‌specific‌ ‌quality‌ ‌assurance‌ ‌reports.‌‌Additionally,‌ ‌the‌ ‌reports‌ ‌in‌ ‌many‌ ‌instances‌ ‌would‌‌provide‌ ‌results‌ ‌that‌ ‌were‌ ‌false‌ ‌positives‌ ‌and‌‌require‌ ‌a‌ ‌lot‌ ‌of‌ ‌research‌ ‌to‌ determine‌ ‌that‌‌the‌ ‌potential‌ ‌error‌ ‌was‌ ‌not‌ ‌in‌ ‌fact‌ ‌an‌ ‌error.‌‌

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With‌ ‌the‌ ‌improvement‌ ‌in‌ ‌technology‌ ‌and‌ ‌AI,‌ ‌there‌‌are‌ ‌now‌ ‌ways‌ ‌to‌ ‌identify‌ ‌payroll‌ ‌anomalies‌ ‌using‌‌algorithms‌ ‌that‌ ‌rely‌ ‌upon‌ ‌historical‌ ‌data‌ ‌and‌ ‌not‌‌just‌ ‌the‌ ‌expertise‌ ‌and‌ ‌judgment ‌of‌ ‌payroll‌ ‌analysts.‌‌For‌ ‌example,‌ ‌we‌ ‌have‌ ‌undertaken‌ ‌this‌ ‌at‌ ‌Alight‌‌Solutions,‌ ‌where‌ ‌our‌ ‌intelligent‌ ‌assistant,‌ ‌‌Eloise‌,‌‌assists‌ ‌payroll‌ ‌teams‌ ‌in‌ ‌performing‌ ‌quality‌ ‌assurance‌‌and‌ ‌preventing‌ ‌and‌ ‌predicting‌ ‌costly‌ ‌errors‌ ‌in‌‌payroll.‌ ‌ ‌

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Utilizing‌ ‌previous‌ ‌payroll‌ ‌payment‌ ‌history,‌‌algorithms‌ ‌calculate‌ ‌the‌ ‌average‌ ‌of‌ ‌earnings,‌‌deductions‌ ‌and‌ ‌taxes‌ ‌‌by‌ ‌employee‌.‌ ‌The‌ ‌average‌ ‌of‌‌each‌ ‌earning,‌ ‌deduction‌ ‌and‌ ‌tax‌ ‌‌by‌ ‌employee‌‌ ‌can‌‌then‌ ‌be‌ ‌compared‌ ‌to‌ ‌the‌ ‌earnings,‌ ‌deductions‌ ‌and‌‌taxes‌ ‌by‌ ‌employee‌ ‌on‌ ‌the‌ ‌payroll‌ ‌currently‌ ‌being‌‌run.‌ ‌Any‌ ‌significant‌ ‌variation‌ ‌can‌ ‌be‌ ‌identified‌ ‌as‌‌an‌ ‌anomaly‌ ‌that‌ ‌requires‌ ‌research‌ ‌and‌ ‌resolution.‌ ‌‌

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The‌ ‌resolution‌ ‌of‌ ‌these‌ ‌anomalies‌ ‌can‌ ‌be‌ ‌done‌‌through‌ ‌business‌ ‌rules‌ ‌and‌ ‌machine‌ ‌learning.‌ ‌Since‌‌much‌ ‌of‌ ‌payroll‌ ‌is‌ ‌defined‌ ‌by‌ ‌rules‌ ‌and‌ ‌limits,‌‌many‌ ‌anomalies‌ ‌can‌ ‌be‌ ‌explained‌ ‌simply‌ ‌by‌ ‌applying‌‌business‌ ‌rules‌ ‌to‌ ‌clarify‌ ‌the‌ ‌difference.‌ ‌For‌‌example,‌ ‌an‌ ‌employee‌ ‌hitting‌ ‌any‌ ‌tax‌ ‌or‌ ‌deduction‌‌that‌ ‌has‌ ‌a‌ ‌limit‌ ‌set‌ ‌by‌ ‌the‌ ‌business‌ ‌would‌‌typically‌ ‌show‌ ‌up‌ ‌as‌ ‌an‌ ‌anomaly.‌ ‌However,‌ ‌those‌‌types‌ ‌of‌ ‌differences‌ ‌are‌ ‌easy‌ ‌to‌ ‌explain‌ ‌and‌ ‌do‌‌not‌ ‌require‌ ‌the‌ ‌involvement‌ ‌of‌ ‌the‌ ‌payroll‌ ‌analyst.‌‌

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The‌ ‌remaining‌ ‌differences‌ ‌require‌ ‌machine‌ ‌learning‌‌where‌ ‌payroll‌ ‌analysts‌ ‌research‌ ‌differences‌ ‌and‌‌provide‌ ‌responses‌ ‌to‌ ‌the‌ ‌anomalies‌ ‌identified.‌ ‌If‌‌the‌ ‌research‌ ‌reveals‌ ‌an‌ ‌error,‌ ‌adjustments‌ ‌are‌‌required‌ ‌to‌ ‌correct‌ ‌the‌ ‌anomaly‌ ‌prior‌ ‌to‌ ‌completing‌‌the‌ ‌payroll‌ ‌run.‌ ‌For‌ ‌those‌ ‌that‌ ‌are‌ ‌resolved‌ ‌as‌‌an‌ ‌acceptable‌ ‌difference,‌ ‌an‌ ‌explanation‌ ‌must‌ ‌be‌‌provided‌ ‌that‌ ‌can‌ ‌be‌ ‌utilized‌ ‌by‌ ‌the‌ ‌algorithm‌ ‌to‌‌explain‌ ‌similar‌ ‌anomalies‌ ‌in‌ ‌the‌ ‌future.‌ ‌Leveraging‌‌machine‌ ‌learning,‌ ‌these‌ ‌explanations‌ ‌and‌‌classifications‌ ‌of‌ ‌anomalies‌ ‌are‌ ‌returned‌ ‌to‌ ‌the‌‌database‌ ‌and‌ ‌then‌ ‌used‌ ‌to‌ ‌automatically‌ ‌explain‌‌subsequent‌ ‌anomalies.‌ ‌ ‌

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As‌ ‌payrolls‌ ‌are‌ ‌run,‌ ‌the‌ ‌learning‌ ‌performed‌ ‌above‌‌minimizes‌ ‌the‌ ‌number‌ ‌of‌ ‌anomalies‌ ‌that‌ ‌require‌‌payroll‌ ‌analyst‌ ‌intervention.‌ ‌Dashboards‌ ‌are‌ ‌created‌‌that‌ ‌provide‌ ‌the‌ ‌analyst‌ ‌with‌ ‌an‌ ‌overview‌ ‌of‌ ‌the‌‌payroll‌ ‌run‌ ‌to‌ ‌show‌ ‌how‌ ‌many‌ ‌earning,‌ ‌deduction‌‌and‌ ‌tax‌ ‌opportunities‌ ‌were‌ ‌normal,‌ ‌how‌ ‌many‌‌resulted in an anomaly, how manywere resolved via business rules, and how many were concluded to beokay due to machine learning.  

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Machine learning dramaticallychanges how the payroll analyst will process payroll in the future.The skillset required will demand more analyticalability as opposed to transactional. More importantly, through theright algorithm and supervised learning, the expectation is thatfewer and fewer anomalies are presented to the payroll analyst forreview, allowing efficiencies and improved quality in runningpayroll.

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The example of anomaly detectiondescribed above applies to the quality of running the payrollgross-to-net calculation. However, the same approach can be appliedto other aspects of payroll or other HR-related business processes.For payroll, moving the anomaly detection up in the processingcycle such that errors in inputs can be identified quickly andresolved prior to running payroll. Applying an algorithm toidentify anomalies to inputs received via integration is anotheropportunity. Utilizing historical time data submission, similarapplications can be applied to identify significant anomalies withemployees' time submission. Such anomalies can alert the timekeeperor the employee directly that time has not been entered orsubmitted. This could result in the reduction of off-cycle payrollruns and improve the employee experience by ensuring employees aregetting paid timely and accurately.

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Lastly, opportunities exist inprocesses related to outbound interfaces. For example, a file thatgoes from payroll to a tax provider typically is balanced to thepayroll results to ensure that it is accurate. However, AI can alsobe applied to determine if what is on the file is consistent ornormal given the previous transmissions of that same file. Thisgives the payroll team additional assurance that the quality ofdata going to the tax system is correct, which is something thatcould lead to large penalties and interest ifincorrect.  

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These are some ideas of wherethe payroll business can take advantage of new technology andspecifically cloud software and AI. The disruption in HR businessprocesses is obvious, but it is also accelerating, so shifting fromdata entry to understanding deeper end-to-end processes will becritical and give rise to new value creators withinHR. 

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WilsonSilva ([email protected]) isa senior vice president of outsourcing delivery at AlightSolutions

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