AI: Built To Scale In Wealth Management - Accenture

10m ago
1.95 MB
19 Pages
Last View : 1m ago
Last Download : 6m ago
Upload by : Lilly Andre

From experimentalto exponentialAI: Built to Scale in Wealth Management

Adopting new technologies is nevereasy. In our experience, organizationsoften focus on a “nirvana” destination,but without defining a clear view ofthe journey, they can get stuck at thestarting line.Artificial intelligence (AI) is perhaps the mostdiscussed “new technology” and a full 84% ofC-suite executives believe they must leverageAI to achieve their growth objectives, accordingto Accenture research. Yet 76% acknowledgethey struggle to scale AI across the business,further exacerbated by today’s uncertainties onwhat client interactions will look like as well asshrinking discretionary investment budgets.1For the wealth management industry, asmuch of Wall Street pivots their respectivebusinesses towards the higher returns ofadvice, the competitive pressure to keep andwin clients has never been greater. For far toolong advisors have relied on the same playbook,with client engagement largely a one-waystreet. Wealth customers are demanding highlydigitized experiences and, at the same time,driving ongoing fee pressure. Additionally,it’s becoming increasingly difficult for wealthmanagers to stand out in a crowded space,as the required investment to compete isimpacting profitability.2Given this, one might ask why a wealthmanager should leverage yet anothertechnology? And what makes AI investmentdifferent? First, while they won’t be immediate,notable cost savings could be achieved bythose who lean into using AI, according toAccenture’s recent AI in Wealth Managementsurvey. Although a majority of respondentsbelieve AI-driven savings will be less than 20%in year one, they expect that benefit coulddouble in two to three years.84%of C-suite executivesbelieve they must leverageAI to achieve their growthobjectives.

Second, we know those who have scaledAI are reaping the benefits—with 2x thesuccess rates and 3x the returns. Yet morethan 80% of companies are stuck on the“Proof of Concept Factory” path, with effortssiloed within a department or team.2 This ringstrue for wealth managers as well, as 88% of oursurvey participants are at the turning point ofmoving beyond experimentation and are lookingto scale and deploy AI solutions. Lack of budgetrarely tops the list of challenges, but ratherthe inability to set a supportive organizationalstructure, the absence of foundational datacapabilities and the lack of employee adoptionstunting scale. Overcoming all of theseimpediments requires time.Finally, COVID-19 has created a uniqueenvironment—immediately pivoting ourindustry toward becoming digitally native,with clients and employees alike demandingmore from their firms. Advisors are realizinghow powerful the technology they alreadyhave is, and, as they settle into new waysof working, they’re more willing to try newsolutions. Our research found 79% believetheir organizations are “digitally ready,”are excited to adopt new AI tools and havean appreciation of delivering efficiencies andenhancements—especially ones that couldhelp with working in a virtual-first world.The time is now—six in ten (60%) surveyrespondents are already focused on deployingthe technology across targeted businessgroups. Wealth managers can and should fullyembrace AI to harness its value across theecosystem, taking advantage of today’s changeagility to become more technology-savvy,AI-powered organizations. Whether your firmis just shaping its AI vision or you have alreadyjumped in, we believe the narrative should shiftfrom theory and baby steps to application andscale, moving from the experimental stage oftoday to the exponential impact of the future.379%of respondents believetheir organizations are“digitally ready.”The Impact of COVID-19on Wealth ManagementThe COVID-19 pandemic has servedas a digital accelerator for the wealthmanagement industry, among others. On onehand, it has led to specific acute challenges—most firms report that prospecting for newclients has become uniquely challenging,and some advisors are overwhelmed withmanaging client interactions in the “alwayson” virtual working environment. It is alsomassively accelerating transformational,pre-existing industry trends—especiallyoverall digitization of the operating modeland the need across capital markets tobend the cost curve. But the move to cloudand modernization of IT by the broaderfinancial services industry has created theperfect springboard for leveraging AI tosimultaneously transform client engagementas well as firm revenue and productivity.

The strategic imperativeIt is no secret that the traditional wealth managementindustry is facing disruption.As demographics shift, so does focus. Wealthclients increasingly demand a larger breadth ofofferings, personalized advice and a compellingexperience with a range of technology services.Broader structural changes are also takingplace. The recent Accenture-Orbium WealthManagement C-Level Survey indicates there isapproximately 78 trillion of assets in motion forwealth managers to capture, underpinned bythe global expansion of the affluent middle class,women with wealth and the wealth created byentrepreneurs and business ownership.3 Withso much promise, the marketplace is converging,intensifying competition as more firms deploystrategies to capture the emerging wealth andmass affluent segments.Seizing this opportunity requires a significantshift in strategy and approach as wealthmanagers recognize how the digital agendaof the past has quickly become the digitalimperative of the future. However, despite thisrecognition, our survey found most managersglobally don’t plan to make the necessarychanges to their business and operatingmodels, including enabling remote interactionservice models and deploying AI to reduceboth costs and time for customer acquisition.By waiting, these firms risk missing the wave ofCOVID-related digitization, falling short of boththe demands from clients as well as the needsof their employees.As we look deeper into the potentialof AI for wealth management, we see fivemajor points where value could be captured:client engagement, product and pricing,the client experience, efficiency and cost.4 78 trillionof assets are in motion forwealth managers to capture.So where are wealth managers focused today?If we first look across the entire financial servicesindustry, customer service is among the topthree areas where emerging technology isapplied (53%), followed closely by finance (49%),business operations (47%) and sales & marketing(44%).4 As we isolate for wealth management,we see an even greater focus on customer versusoperations. In the next year, 77% and 51% of oursurvey respondents believe AI will have a majorimpact on client experience, and product /investment recommendations, respectively.Simply put, there is tremendous opportunityfor wealth managers to capture more valuefrom AI. With proven use cases as a startingpoint, the journey can be accelerated—rapidlymoving from theory to execution and inturn capturing the benefits seen from scale.As we further describe our latest findingsfrom working across the industry, we seean evolution from today’s world of targetedapplication to a future of scale and innovation,making it clear why AI should be part of yourexecution strategy today.

AI’s Value-Add in Wealth FrameworkClientengagementProduct &pricingThe clientexperienceAlways-on and encyCostMaximized timeand valueBending thecost curveTheoryAs technology continuesto democratize andtailor advice delivery,it keeps pace withthe preferences ofa tech-savvy nextgeneration ofinvestors: always-onand personalized.Just as recommendationengines havetransformed society'sretail experience, theycan also help optimizeadvisory models byputting the rightproduct in the rightclient’s hands.Clients enjoy improvedand more tailoredservice because AItakes personalizationto the next level andcreates a closerconnection betweenclient and advisor.By relieving advisorsof routine tasks, AI helpsthem maximize clienttime, serve a larger bookof business, and focuson relationship building.AI can be leveraged toreinvent operationalprocesses end-to-endwith a much higherdegree of automation,reducing the amount ofeffort required tocomplete them.Productrecommendationstailored to the client(at the right time andright price) will benefitboth clients and theiradvisors, with increasedsatisfaction and benefitsfor both.AI allows for thedelivery of customizedportfolios and clienttouchpoints at exactlythe right time, in waysthat human advisorswould not be capable ofachieving on their own.Wealth managersbenefit from AI bygaining a deeperunderstanding of theirclients, which allowsthem to moreproactively interveneto help make sure theirclients have what theyneed, when they need it.When applied to themiddle and back offices,AI can save costs andrelieve pain points,ultimately bendingthe firm's cost curve.Cross-sellingNotifications basedon social media andweb browsingA focus on the highestand best use actionsfor clients andfinancial advisorsIncreased focus onefficiencies acrossthe value chainApplicationNew and AI-powereddigital tools and waysof interacting withclients open the clientbase to a youngergeneration of techsavvy high networth individuals.ExamplesAutomated leadgenerationTopical contentengagementProduct & pricingrecommendationengineSource: Accenture research77%of our survey respondentsbelieve AI will have a majorimpact on client experience.5

Today: Proving the valueand building a foundationIt is clear AI is garnering significant interest acrossindustries and wealth management is no different—broadly undertaking pilot projects to test its potential.The workforce perspective further reflectsthese changes, with our research showing 89%of respondents agree (47% strongly agree) theyunderstand how AI can impact their business.Further, efforts are starting to becomepermanent fixtures. According to Gartner,by the end of 2024, three-quarters ofenterprises will have shifted from piloting tooperationalizing AI.5 Among wealth managers,early AI adopters focused efforts on practicessuch as “know your client” (KYC) and riskmanagement. More recently, attention hasshifted to marketing and service in the form of“next best action” (NBA) and “next best offer”(NBO) decision making. Our survey resultsindicated up to 80% of respondents reportedthey’re either deploying or scaling both clientand advisor-facing AI-powered technology,so some players may find themselves playingcatch-up to the rest of the industry.Up to 80%of respondents reportedthey’re either deployingor scaling both clientand advisor-facingAI-powered technology.6With the level of exploration and adoptionreadiness increasing, AI across wealthmanagement is poised to capture potential tothe benefit of both the top and bottom line.We’ve helped a number of players of all types,sizes and strategies launch their AI journeywith a common set of use cases that generatesignificant, measurable value. Our experienceshows it’s not atypical for a single use caseto generate a 20% uplift, growing bothexisting clients as well as revenue from newclients. When multiple use cases are pursueda combinatorial effect occurs, and the longerterm uplift could easily be doubled or more.With our survey showing that 49% and 55% ofrespondents are focusing their AI investmentover the next one to two years on the frontand middle offices respectively, we’ve sharedsome of the most impactful use cases on thefollowing pages.

Front OfficeAI-Powered MarketingAI and “new” data present tremendouspotential for unlocking insights that generateactionable leads and opportunities for betteracquisition, cross-sell/up-sell and attritionmanagement. For example, AI can helpdevelop client micro-segmentation, movingbeyond traditional client demographics andnet worth to include dynamic and attitudinalbehaviors—such as how actively they transactand their propensity to concentrate share ofwallet. Going even further, signals generatedby individuals through their digital interactionscould more quickly lead to the “zero momentof truth” within their consideration andpurchase journey.The following examples demonstratethe value of AI-powered marketing:Prospecting/client acquisition: A U.S.retail digital bank wanted to increase funnelconversion and reduce cost per acquisitionby leveraging signals mined from external datasources to create a more personalized digitalexperience. The firm turned to Accenture forsupport, and we helped them analyze more thantwo terabytes of external data for 10 millionhigh quality prospects for the pilot campaign.Through leveraging AI on a data set of this size,the bank was able to drive micro-segmentationthat delivered a stickier client experience andhigher conversion rate via its website.Value-add: As a result of the tailoredengagement, the bank realized a 30% upliftin click-through rates, a 50% increase inapproval rates and a 40% reduction in costper acquisition.Event marketing analysis: A wealth managementfirm’s analysis revealed that the customerselection process for marketing events wasnot appropriately aligned to deliver maximumbenefit in net new money (NNM). Despite certain7customer segments that showed a propensity toimprove NNM, overall performance was belowthat of comparable peers. Accenture workedwith the client to develop and profile segmentsto identify clients with uniform NNM behaviorsand developed an attribution approach related toevent attendance that was underpinned by AI.Value-add: The firm received a multimillion-dollar impact in NNM benefitsand improved marketing ROI through theanalysis of marketing events and changingthe mix of client profiles.Next Best ActionNBA frameworks can benefit from an ensembleof AI capabilities, optimizing the ability toidentify and prioritize actions for advisorsbased on clients’ needs and behaviors.These capabilities are applied for marketingpurposes as well as client management,delivering smarter, prioritized, customer-centricinteractions that are tailored for each userand customized for marketplace dynamics.The following examples demonstrate the valueof AI-powered NBA:Pricing recommendation engine: Large scaleprice discounting by advisors was prominentin both managed advisory and transactionalportfolios, leading to significant revenueleakage. Pricing changes were, in turn,impacting client behaviors. A wealth managerwanted to examine the impact of pricechanges on revenue fluctuations. The answerwas a comprehensive AI-powered pricingsolution and simulation engine to providefinancial advisors with customized pricingrecommendations.Value-add: This comprehensive pricingsolution leveraging AI and client behaviordelivered a multi-million-dollar annualbenefit as well as greater control fromthe home office.

Salesforce integration into the NBA engine:A wealth management firm integratedSalesforce into its NBA engine to feed analyticsrecommendations to the distributed marketingsolution. This solution is designed to provideinsights that enable proactive actions relating toclient life events, investment activity and servicealerts, such as Required Minimum Distributionor bond maturity. The firm turned to Accentureto help maximize its investment to generateimproved ROI by combining machine learningwith CRM.Value-add: The solution allowed advisorsto both proactively and reactively engageclients and also drove change enablementvia rollout for thousands of field users.Cross-sell engine: At one wealth managementfirm, the share of clients’ total liabilities waslow, indicating significant potential to growthe portfolio. Accenture developed a liabilitiesproduct recommendation engine powered byAI and propensity models that combined theclient balance sheet with micro-segmentationto increase penetration of lending products.Value-add: Prioritized client targetingestablished an opportunity for 30% lendinggrowth and for adding billions of dollars tothe current liabilities held at the firm.Advisor cross-sell engine: A structuredproducts and insurance business wantedto increase the penetration of its respectiveproducts in customers’ portfolios. The solutionwas a propensity model to identify advisorswith a higher affinity to structured productsand insurance.Value-add: Millions of dollars in revenuebenefit was delivered by identifyinga large group of advisors with thepropensity to sell the targeted products.8Client attrition engine: A wealth managementfirm was experiencing high attrition of itsaffluent and high net worth clients. It wantedto take preemptive measures to reduce thenumber but lacked a clear approach foridentifying these clients. The solution was anAI-powered predictive model to identify clientswith a high risk of leaving the firm.Value-add: The model helped capture a 40%client attrition risk by focusing on only 20%of the base and gave the business months oflead time to take preemptive measures.Advisor retention: A wealth management firmwas experiencing high financial advisor attritionand wanted to take preemptive measures toreduce it. Accenture used data from internaland external sources to create a “single advisorview” that gave a detailed analysis and profileof key attributes for advisor attrition. We thenused these trends and insights to develop apredictive model to identify advisors with thepropensity to leave.Value-add: Similar to the previous example,this solution helped capture a largepercentage of attrition risk by giving thebusiness months of lead time and theability to target the riskiest advisors withpreemptive measures.Middle/Back OfficeComponents andUse CasesAs firms look to the middle and back office,most apply AI to perennial causes of poorclient experience, such as onboarding, orto issues that intuitively should be solvablethrough machine logic, such as reconciliation.We also see firms continuing to focus on thefoundations of data cleanup and CRM capabilityenablement—an ongoing and evolving journeyfor the entire industry.

Below are two middle and back office areaswhere AI could provide immediate value:Intelligent automation of operationalprocesses: AI capabilities such asinformation extraction combined withintelligent workflow, visualization and contentmanagement are a powerful combination.Integrating these into a single solution movesfar beyond point automation, transformingend-to-end processes such as clientonboarding. This approach not only drivesefficiencies for both the middle and backoffice, it enhances productivity and datacompleteness for the front office. Accenturehas worked with a number of financialservices firms on next generation platforms.Value-add: Elimination of manualprocesses and centralized rules-basedapprovals and controls based onmachine learning capabilities.Portals and “one-stop apps” for the frontoffice: Firms have long been moving towarda “single pane of glass” concept, connectingmultiple applications and data sources tofacilitate an advisor’s day-to-day activities.Applying AI further amplifies value, such ascognitive agents to suggest courses of actionand provide intelligent search, along withcontinuous learning to better understandthe personas and related terminologies.Accenture has helped develop a numberof such platforms in financial services thatare more efficient in responding to clientrequests and providing clients with moreappropriate content the first time around.Value-add: The benefits of fasteronboarding, accelerated productivityand greater employee satisfaction.9

Tomorrow: Acceleratingalong the journeyIt’s a journey, not a destination—with a true business need at its very core.Focus on thebusiness strategy,not technologyBuild/buyaccording tobusiness strategyAs companies learn from their initial usecases, AI quickly becomes a C-suite priority,progressing them from proof of concept toscale. AI transformation journeys are not “onesize fits all.” Part of the equation is defining howto use AI for what you want the technology todo—a clear business strategy. We’ve also seena correlation between a firm’s size and whenand how the journey evolves: Smaller firms tend to rely more on theecosystem evolution, choosing out-of-thebox solutions while in parallel aggregatingsupporting data to build out customer andadvisor analytical records. Mid-size firms are typically fast followersof successful market leaders, building depthin their solutions, yet prioritizing capabilitiesthat are core to their strategy.10View throughan iterative,on-going andliving process Market leaders go both broad and deep,actively deploying AI in the front, middleand back offices. These firms continue tofocus on research and testing of the latestcapabilities, while creating a culture of AI thatdemocratizes data and analytics and drivesusage across the enterprise.Regardless of size, we see common challengesas wealth management firms progress alongtheir AI journey, hampering their ability toscale. Our research has also revealed threecritical success factors that separate firms whoovercome these roadblocks, and could helpwealth managers realize AI’s full benefit.

Roadblocks to ScalingFoundational data capabilities: Data privacyis paramount in wealth management, andthere are substantial regulatory requirementsto consider, especially in light of CCPA andGDPR. A leading question for firms is how toassign the proper controls and tools to protectclient data from internal and external threats,especially in a remote work environmentthat’s vulnerable to cyber-attacks.Governance and risk management: AI decisionsin wealth management have a real bearing onpeople’s lives, and placing decision-makingcapability in the hands of a machine raisesbig questions around ethics, trust, legalityand responsibility. This has never been truerthan today, in a climate where fairness andjustice are front and center of most of ourpersonal lives. Further, businesses are exposedto additional risk, including reputational,employment/HR compliance, data privacyand health and safety issues.Employee adoption: New ways of workingare required to achieve the potential of AI.In wealth management, advisors will needto adjust. Our survey indicated the mostlong-term value for AI is perceived in thefront office, where 71% see it transformingthe client advisor relationship in the next year,and 100% within three years. In an industrywhere a traditional playbook is still the norm,companies cannot underestimate the criticalityof engaging advisors in the value of AI.Critical Success FactorsDrive “intentional” AI: Leaders view AI asa critical source of value, with an approachdriven by the business strategy, not thetechnology. To build momentum, werecommend undertaking projects in a fewareas to undeniably demonstrate the value,concentrated on stakeholders who are likelyto be converted to evangelists. Successfully11scaling requires not only a clearly definedstrategy, but also an operating model withdefined processes and owners for measuringvalue, appropriate levels of funding andestablished executive support. With clearaccountability for scaling, leaders more oftenleverage reusable assets, and could completesuccessful AI programs 3-5X faster.6Tune out data noise: It has been predictedthat every person will generate 1.7 megabytesof data in just one second in 2020—whichshould help convey why most organizations arestruggling with the sheer volume of informationand how to consume it.7 Leaders recognizethe importance of business-critical data, withthe greater ability to integrate internal andexternal sources—including not just traditionaldemographic information but “new data” such asonline presence and behavior, life stages/eventsand geo-location. Moreover, using the right AItools—such as cloud-based data lakes and datascience workbenches with model management—could enable not just data maintenance andconsumption but also enhanced “trusted AI”governance and model explainability.Treat AI as a team sport: AI is not a one-timeevent, but rather an ongoing and iterativeprocess as the data landscape and underlyingtechnologies evolve. Given this dynamic,leaders have recognized that having executivesponsorship is not enough—effectively scalingcalls for embedding multi-disciplinary teamsthroughout the organization. This not onlysends a powerful signal about the strategicintent, it also highlights the “learning” inmachine learning, enabling more rapid cultureand behavior changes. The better the blendof skills, the more sustainable the result—re-enforcing a constant commitment todemonstrate value to the business.

The future: A digitalplatform mindsetIn 2019, it was predicted that AI augmentation wouldcreate 2.9 trillion of business value and 6.2 billionhours of worker productivity globally.8 For wealthmanagers, we firmly believe AI will continue toprovide long-term value across the front, middle andback office, and anticipate five years from now therecould be new ways of innovating not yet imagined.There is still a long way to go on that journey, but therewards could be substantial.The Art ofthe Probable84%of respondents agreeAI will transform theindustry in five years.12The Art ofthe PossibleThe NextHorizon

The Art of the ProbableWhere will value likely be realizedin the short term (one-two years)?Over the next one to two years, the wealthmanagement industry should focus on extractingvalue from use cases currently being exploredwhile concurrently pursuing natural adjacencies.Many of the respondents to our survey sharedtheir intent to focus AI investment on the client,yet indicated they see significant long-termvalue in the middle office. What’s driving thediscrepancy? Maybe it’s that 85% of respondentsbelieve AI in wealth management is more hypethan reality, but 84% agree AI will transformthe industry in five years. As a result, near-termpriorities target “incremental” benefits, whilemany firms wait to see how innovation will unfoldacross a broader front.In our perspective, NBA is the area where themost value is left on the table. We recommendthe following use cases get specific focus,given their high business value: Real-time alerts of life events that signal whento reach out to clients:AI value-add: A child’s collegeacceptance announced via socialmedia can trigger the advisor to offercongratulations and planning. Portfolio and investment activity relatedto active portfolio triggers or everydayrecommendations:AI value-add: Analytics thatautomate recommendations tailoredto the client’s preferences andgoals based on market activity.13 Hyper-personalization that leads to tailoredproduct recommendations:AI value-add: Analytics that enablean Expedia-like dashboard of tailoredproducts curated for the individual client. Pricing and client attrition prediction andrecommended actions:AI value-add: A data-driven model thataggregates and analyzes information fromclient satisfaction surveys, tenure, advisorperformance and service history to rankclients accordingly.Natural adjacencies to explore includespecific advice simulation modules and tools.Most financial advisors report their focus is onhelping clients with retirement. Building outmore self-service and scenario modeling toolsis a natural focus point for advisors in the lowerand middle wealth brackets. This could includepeer comparisons of goals and net worth aswell as product recommendation enginesbased on cross-population similarities.Moving into the middle and back office,firms are much less further along, hamperedby legacy complexity around required datacleanup and outsourced functions. However,one clear next step is to focus on derivingvalue in areas where other financial servicesfirms have seen traction, including onboarding,KYC, compliance, reconciliation and otherrepetitive or manual processes wherepeople input or extract information (such asdata entry). We highlight the middle office(including investment management) and backoffice functions specifically as nearly two-thirds(65%) of respondents believe this is where AIcould create the most long-term value withinwealth management.

The Art of the PossibleWhere will value likely be realized inthe medium term (three-five years)?Moving beyond to the next three to five years,the industry’s focus will likely shift to intelligentproduct creation based on client needs.This effort is underpinned by pursuing moreholistic, end-to-end use cases.While the near-term focus for the front office ison tactical uplift to address specific problems, inthe medium term the emphasis is on optimizingtechnology to enable client interaction on a scalewhich we cannot yet foresee. This transformationwould allow advisors to expand the scope of theadvisory services they offer (such as trust andestate, Medicare planning and/or more in-depthgoal-based planning), growing the size of theiroverall business.Client Service. The doomsday prediction thatrobo-advice would cause the demise of theindustry never came to fruition, as wealth clientsvalue having a trusted coach for holistic, goalbased financial planning. However, these clientsdo want greater control of the interaction—bothwhen and how it takes place. In three to five years,every client could have a seamless experienceacross both their traditional advisor as well asa virtual advisory assistant (“intelligent advisor”),helping with day-to-day decisions and providingpersonalized interactions at the right time.Value-add: This helps form a virtual cyclefor the advisor. As interactions betweenclients and virtual advisors/assistantsprovide crucial data that leads to insights,human advisors can act upon them whendeveloping an overall product strategy andcreating customized products based onclients’ needs. This would allow advisorsto better understand their clients in a waythat meeting up for a few hours a year nevercould. We also see an opportunity for AI tocreate a lot of value with respect to NBAsthat translate into sales coaching, productrecommendations and optimized pricing.14Adding Value witha Vir

wealth managers to capture, underpinned by the global expansion of the affluent middle class, women with wealth and the wealth created by entrepreneurs and business ownership.3 With so much promise, the marketplace is converging, intensifying competition as more firms deploy strategies to capture the emerging wealth and mass affluent segments.

Related Documents:

CCC-466/SCALE 3 in 1985 CCC-725/SCALE 5 in 2004 CCC-545/SCALE 4.0 in 1990 CCC-732/SCALE 5.1 in 2006 SCALE 4.1 in 1992 CCC-750/SCALE 6.0 in 2009 SCALE 4.2 in 1994 CCC-785/SCALE 6.1 in 2011 SCALE 4.3 in 1995 CCC-834/SCALE 6.2 in 2016 The SCALE team is thankful for 40 years of sustaining support from NRC

Svstem Amounts of AaCl Treated Location Scale ratio Lab Scale B en&-Scale 28.64 grams 860 grams B-241 B-161 1 30 Pilot-Plant 12500 grams MWMF 435 Table 2 indicates that scale up ratios 30 from lab-scale to bench scale and 14.5 from bench scale to MWMW pilot scale. A successful operation of the bench scale unit would provide important design .

work/products (Beading, Candles, Carving, Food Products, Soap, Weaving, etc.) ⃝I understand that if my work contains Indigenous visual representation that it is a reflection of the Indigenous culture of my native region. ⃝To the best of my knowledge, my work/products fall within Craft Council standards and expectations with respect to

Scale Review - Review the E - flat scale. Friday 5/29/2020. Scale Review - Review the c minor scale. Sight Reading. Monday 6/1/2020. History - Read 20th Century Packet - Complete listenings and quiz. Scale Review - Practice the B - flat Major scale. Tuesday 6/2/2020. Scale Review - Practice the g melodic minor scale. Wednes

Remember, this is just an abridged form of the major scale. It's not a 'separate', distinct scale. It's just the major scale, in a simpler form. Can you see that this has just a few notes less? Minor Scale Minor Pentatonic Scale Remember, this is just an abridged form of the minor scale. It's not a 'separate', distinct scale.

Technical White Paper, Built Up Sections Page 4 Page 2 CREATION OF A BUILT-UP SECTION To create a built-up section, you need to use the Built-Up Sections command ( ) from the Table - Sections menu. Then click on the Add a new tab button and select the desired shape. Built-up sections are sections built from other existing sections.

So if you start playing the major scale pattern on the 7th fret you will be playing the B Major Scale. If you start playing the minor scale pattern on the 10th fret you will be playing the D Minor Scale. If you start playing the minor pentatonic scale on the 3rd fret you will be playing the G Minor Pentatonic Scale. Have fun! !! !

The scale has an Auto Shut Off feature which automatically turns off the scale after three minutes of inactivity. To turn the scale on Press on the front of the scale. When the scale is first turned on, wait a few seconds for the scale to stabilize before weighing items. The di