Three common traits of AI frontrunners in financial services

pcbinary June 27, 2021 0 Comments



Most respondents are seeing returns from AI


In this year’s survey, we asked respondents about 33 AI use cases across eightbusiness functions, including how adoption of AI for each of these activitieshas affected revenue and cost in the business units where AI is used. Theresults suggest that AI is delivering meaningful value to companies.The executive’s AI playbookAggregating across all of the use cases, 63 percent of respondents reportrevenue increases from AI adoption in the business units where their companiesuse AI, with respondents from high performers nearly three times likelier thanthose from other companies to report revenue gains of more than 10 percent.Respondents are most likely to report revenue growth from AI use cases inmarketing and sales, product and service development, and supply-chainmanagement (Exhibit 1). In marketing and sales, respondents most often reportrevenue increases from AI use in pricing, prediction of likelihood to buy, andcustomer-service analytics. In product and service development, revenue-producing use cases include the creation of new AI-based products and new AI-based enhancements. And in supply-chain management, respondents often citesales and demand forecasting and spend analytics as use cases that generaterevenue.Exhibit 1We strive to provide individuals with disabilities equal access to ourwebsite. If you would like information about this content we will be happy towork with you. Please email us at:McKinsey_Website_Accessibility@mckinsey.comOverall, 44 percent of respondents report cost savings from AI adoption in thebusiness units where it’s deployed, with respondents from high performers morethan four times likelier than others to say AI adoption has decreased businessunits’ costs by at least 10 percent, on average. The two functions in whichthe largest shares of respondents report cost decreases in individual AI usecases are manufacturing and supply-chain management. In manufacturing,responses suggest some of the most significant savings come from optimizingyield, energy, and throughput. In supply-chain management, respondents aremost likely to report savings from spend analytics and logistics-networkoptimization.

AI adoption is increasing in nearly all industries, but capabilities vary


As in last year’s survey, we asked respondents about their companies’ use ofnine AI capabilities. Fifty-eight percent of respondents report that theirorganizations have embedded at least one AI capability into a process orproduct in at least one function or business unit, up from 47 percent in2018—a sign that AI adoption in general is becoming more mainstream. What’smore, responses show an increase in the share of companies using AI inproducts or processes across multiple business units and functions: 30 percentof this year’s respondents report doing so, compared with 21 percent in theprevious survey. While this seems to indicate that more companies arebeginning to scale AI, high performers are much further along in theseefforts, averaging 11 reported AI use cases across the organization versusabout three among other companies.By sector, the results indicate increases in AI adoption in nearly everyindustry in the past year. Retail has seen the largest increase, with 60percent of respondents saying their companies have embedded at least one AIcapability in one or more functions or business units, a 35-percentage-pointincrease from 2018.The results show companies applying AI capabilities that help them perform thefunctions that create value in their industries. For example, respondents fromconsumer-packaged-goods companies are more likely to report using physicalrobotics—which can aid in assembly tasks—than most other types ofcapabilities. And telecom respondents report their companies using virtualagents—which can be used in customer-service applications—more than othercapabilities (Exhibit 2). High-performing companies, however, are far morelikely to adopt AI in business functions that this survey and past researchlink to greater value creation more broadly. For example, more than 80 percentof respondents from high performers say they have adopted AI in marketing andsales, compared with only one-quarter from those of other companies that useAI.Exhibit 2We strive to provide individuals with disabilities equal access to ourwebsite. If you would like information about this content we will be happy towork with you. Please email us at:McKinsey_Website_Accessibility@mckinsey.comOn a regional level, the survey shows significant increases in adoption levelsin developed Asia–Pacific, Europe, Latin America, and North America. InAsia–Pacific and Latin America, the shares of respondents who say theircompanies have embedded AI across multiple functions or business units havenearly doubled since the previous survey. However, the increases put all ofthese regions, as well as China, at similar aggregate reported levels ofadoption, suggesting that while there is considerable variation at the levelof individual companies, the adoption of AI is a global phenomenon.The results indicate that the pace of adoption will likely continue in thenear term, with 74 percent of respondents whose companies have adopted or planto adopt AI saying their organizations will increase their AI investment inthe next three years. More than half of these respondents expect an increaseof 10 percent or more. But the survey results indicate that AI high performersplan to invest more, with nearly 30 percent of respondents from thesecompanies saying their organizations will increase investment in AI by 50percent or more in the next three years, compared with just 9 percent ofothers who say the same.

A minority of companies acknowledge most AI risks—fewer mitigate them


Despite extensive dialogue across industries about the potential risks of AIand highly publicized incidents of privacy violations, unintended bias, andother negative outcomes, the survey findings suggest that a minority ofcompanies recognize many of the risks of AI use. Even fewer are taking actionto protect against the risks.Breaking away: The secrets to scaling analyticsFewer than half of respondents (41 percent) say their organizationscomprehensively identify and prioritize their AI risks. The survey also askedspecifically about ten of the most widely recognized risks. Of them,respondents most often cite cybersecurity and regulatory compliance as the AI-related risks their companies consider relevant (Exhibit 4). These two risksare the only ones that at least half of respondents cite as relevant.Furthermore, the share of respondents saying their companies are mitigatingeach risk is smaller than the share citing it as relevant. For example, while39 percent of respondents say their companies recognize risk associated with“explainability” (the ability to explain how AI models come to theirdecisions), only 21 percent say they are actively addressing this risk. At thecompanies that reportedly do mitigate AI risks, the most frequently reportedtactic is conducting internal reviews of AI models.Exhibit 4We strive to provide individuals with disabilities equal access to ourwebsite. If you would like information about this content we will be happy towork with you. Please email us at:McKinsey_Website_Accessibility@mckinsey.comRespondents at AI high performers are likelier than those from other companiesto say their organizations both recognize and work to reduce risks. Takepersonal-privacy risk, which is squarely in regulators’ line of sight. Eightypercent of respondents at high-performing companies say their companiesconsider personal-privacy risk to be relevant, compared with less than half ofrespondents from other companies. When asked about internal controls aimed atreducing privacy risks, 89 percent of respondents at high-performing companiessay their organizations adopt and enforce enterprise-wide privacy policies,compared with 68 percent of other respondents. Similarly, 80 percent ofrespondents at AI high performers report that their organizations implementtech-enabled access restrictions to sensitive data, versus 59 percent of thoseat other companies.

More expect AI to cause workforce decreases than increases, with variances


across functionsGenerally, there has been increasing concern that AI will lead to workforcereduction. The survey findings suggest that, thus far, this concern haslargely not been realized. More than one-third of respondents report less thana 3 percent change in their companies’ workforce size because of AIdeployment, and only 5 percent of respondents report a change, whetherdecrease or increase, of greater than 10 percent. While respondents from ahandful of industries, including automotive and assembly, are more likely toreport a workforce reduction than an increase in the past year because of AI(Exhibit 5), more respondents overall report job increases of 3 percent ormore at their companies in the past year than report decreases of the samemagnitude (17 percent and 13 percent, respectively).Exhibit 5We strive to provide individuals with disabilities equal access to ourwebsite. If you would like information about this content we will be happy towork with you. Please email us at:McKinsey_Website_Accessibility@mckinsey.comBut the outlook for the next three years could be shifting. Thirty-fourpercent of respondents from organizations that have adopted or plan to adoptAI expect it to drive a decrease in the number of employees, versus 21 percentwho expect an increase—although most predict the change to be less than 10percent in either direction. Another 28 percent foresee AI adoption havinglittle impact on workforce size, with any expected change being less than 3percent.Respondents also expect AI adoption to cause shifts in their workforce acrossfunctions. Respondents are more likely to predict a decrease than an increasein employment levels in HR, manufacturing, supply-chain management, andservice operations. They more often predict an increase than a decrease in thenumber of employees in product development and marketing and sales.

Running the AI leg of the digital marathon


The financial services industry has entered the artificial intelligence (AI)phase of the digital marathon.The journey for most companies, which started with the internet, has takenthem through key stages of digitalization, such as core systems modernizationand mobile tech integration, and has brought them to the intelligentautomation stage.Many companies have already started implementing intelligent solutions such asadvanced analytics, process automation, robo advisors, and self-learningprograms. But a lot more is yet to come as technologies evolve, democratize,and are put to innovative uses.To effectively capitalize on the advantages offered by AI, companies may needto fundamentally reconsider how humans and machines interact within theirorganizations as well as externally with their value chain partners andcustomers. Rather than taking a siloed approach and having to reinvent thewheel with each new initiative, financial services executives should considerdeploying AI tools systematically across their organizations, encompassingevery business process and function.

Key messages


* Embed AI in strategic plans: Integrating artificial intelligence (AI) into an organization’s strategic objectives has helped many frontrunners develop an enterprisewide strategy for AI that various business segments can follow. The greater strategic importance accorded to AI is also leading to a higher level of investment by these leaders. * Apply AI to revenue and customer engagement opportunities: Most frontrunners have started exploring the use of AI for various revenue enhancements and client experience initiatives and have applied metrics to track their progress. * Utilize multiple options for acquiring AI: Frontrunners seem open to employing multiple approaches for acquiring and developing AI applications. This strategy is helping them accelerate the adoption of AI initiatives via access to a wider pool of talent and technology solutions.As with any race, some companies are setting the pace, while others arestruggling to hit their stride after leaving the starting gate. What can thosewho are seemingly at the back of the pack do to keep up with theirfrontrunning competitors? How can they jump-start or adapt their AI game plansto come up on top as the race heats up?To answer these questions, Deloitte surveyed 206 US financial servicesexecutives to get a better understanding of how their companies are using AItechnologies and the impact AI is having on their business (see sidebar,“Methodology: Identifying AI frontrunners among financial institutions”). Thereport identified some of the following key characteristics of respondents whohave gotten off to a good start and taken an early lead:Embed AI in strategic plans: Integrating AI into an organization’s strategicobjectives has helped many frontrunners develop an enterprisewide strategy forAI, which different business segments can follow. The greater strategicimportance accorded to AI is also leading to a higher level of investment bythese leaders.Apply AI to revenue and customer engagement opportunities: Most frontrunnershave started exploring the use of AI for various revenue enhancements andclient experience initiatives and have applied metrics to track theirprogress.Utilize multiple options for acquiring AI: Frontrunners seem open to employingmultiple approaches for acquiring and developing AI applications. Thisstrategy is helping them accelerate the adoption of AI initiatives via accessto a wider pool of talent and technology solutions.

Methodology: Identifying AI frontrunners among financial institutions


To understand how organizations are adopting and benefiting from AItechnologies, in the third quarter of 2018 Deloitte surveyed 1,100 executivesfrom US-based companies across different industries that are prototyping orimplementing AI.1 In this report, we focus on a sample of 206 respondentsworking for financial services companies. All respondents were required to beknowledgeable about their company’s use of AI technologies, with more thanhalf (51 percent) working in the IT function. Sixty-five percent ofrespondents were C-level executives—including CEOs (15 percent), owners (18percent), and CIOs and CTOs (25 percent).All financial services respondents in the survey were required to be currentlyusing AI technologies in some form or another (see “Appendix: The AItechnology portfolio”). The entire respondent base of individuals working forfinancial institutions could thus be considered as early adopters of AIinitiatives.Within this respondent base, we wanted to identify the practices adopted bythose leading the pack in terms of AI deployment experience and tangiblereturns achieved from them. Using data from Deloitte’s AI survey, weidentified two quantitative criteria for further analysis: performance(financial return from AI investments) and experience (number of fullydeployed AI implementations, which represents AI projects that are “live,”fully functional, and completely integrated into business processes, customerinteractions, products, or services).We found that companies could be divided into three clusters based on thenumber of full AI implementations and the financial return achieved from them(figure 1). Each of these clusters represents respondents at different phasesof their current AI journey. * Frontrunners: Thirty percent of respondents worked for companies that had achieved the highest financial returns from a significant number of AI implementations. * Followers: Forty-three percent of respondents worked for companies in the middle ground of AI implementations and financial returns. * Starters: Twenty-seven percent of respondents worked for companies that were at the start of their AI journey and/or lagging in the level of return achieved from AI implementations.

Three common traits of AI frontrunners in financial services


As financial institutions look to find a rhythm in their AI race, frontrunnerscould provide an early-bird view into how to effectively integrate thetechnology with an organization’s strategy, as well as which approachescompanies could adopt for implementing such initiatives throughout theirorganization.From the survey, we found three distinctive traits that appear to separatefrontrunners from the rest. Frontrunners are generally able to embed AI instrategic plans and emphasize an organizationwide implementation plan; focuson revenue and customer opportunities, rather than just cost reduction; andadopt a portfolio approach for acquiring AI, where they utilize multipledevelopment models for implementing AI solutions (figure 2).

Embed AI in strategic plans with emphasis on organizationwide


implementationWhile many financial services companies agree that AI could be critical forbuilding a successful competitive advantage, the difference in the number ofrespondents in the three clusters that acknowledged the critical strategicimportance of AI is quite telling (figure 3).An early recognition of the critical importance of AI to an organization’soverall business success probably helped frontrunners in shaping a differentAI implementation plan—one that looks at a holistic adoption of AI across theenterprise. The survey indicates that a sizable number of frontrunners hadlaunched an AI center of excellence, and had put in place a comprehensive,companywide strategy for AI adoptions that departments had to follow (figure4).For example, as part of an overall strategy to become a “bank of the future,”Canada-based TD Bank set up an Innovation Centre of Excellence (CoE). Actinglike an umbrella organization, the CoE connects all the innovationinitiatives, including AI, to broader bank business units. It provides aplatform for experimentation across the organization with the purpose ofreducing operational complexity and improving customer experience. The CoEthus helps in testing and identifying best practices from AI pilots beforeintroducing them as full-scale customer solutions.2It is also no surprise, given the recognition of strategic importance, thatfrontrunners are investing in AI more heavily than other segments, while alsoaccelerating their spending at a higher rate. Close to half of thefrontrunners surveyed had invested more than US$5 million in AI projectscompared to 27 percent of followers and only 15 percent of starters (figure5). In fact, 70 percent of frontrunners plan to increase their AI investmentsby 10 percent or more in the next fiscal year, compared to 46 percent offollowers and 38 percent of starters (figure 6).A major emphasis of these investments likely was to secure the talent andtechnologies necessary for the transformational journey ahead.3

Focus on applying AI to revenue and customer engagement opportunities


Despite steady improvement in the economy following the 2008 financial crisis,the pressure to reduce costs at financial institutions has continued toincrease. At the same time, rising competition from incumbents andnontraditional entrants, as well as greater regulatory oversight andcompliance demands, are raising the cost of doing business. The return onaverage equity of commercial banks, for example, has yet to reach pre-financial-crisis levels.4It is no surprise, then, that one in two respondents were looking to achievecost savings or productivity gains from their AI investments. Indeed, inaddition to more qualitative goals, AI solutions are often meant to automatelabor-intensive tasks and help improve productivity. Thus, cost saving isdefinitely a core opportunity for companies setting expectations and measuringresults for AI initiatives.That said, what differentiated frontrunners (figure 7) is the fact that moreleading respondents are measuring and tracking metrics pertaining to revenueenhancement (60 percent) and customer experience (47 percent) for their AIprojects. This approach helped frontrunners look at innovative ways to utilizeAI for achieving diverse business opportunities, which has started to bearfruit.A good case could be how AI and predictive analytics were used by UK-basedMetro Bank to help customers manage their finances. Working in partnershipwith Personetics, the bank launched an in-app service called Insights, whichmonitored customers’ transaction data and patterns in real time. The app thenprovided personalized prompts to make subscription payments and be aware ofunusual spending. The AI tool also provides personalized financial advice,including savings recommendations and alerts.5Frontrunners have taken an early lead in realizing better business outcomes(figure 8), especially in achieving revenue enhancement goals, includingcreating new products and pursuing new markets.This mindset was reflected in the overall performance among respondents aswell, with frontrunners reporting a companywide revenue growth of 19 percentaccording to the survey, which was in stark contrast to the growth of 12percent for followers and a decline of 10 percent for starters.Meanwhile, our research indicated that companies should give special emphasisto the human-centered design skills needed to develop personalized userexperiences.6 In fact, the survey found that frontrunners are already startingto suffer from a shortage of designers for AI initiatives, which indicates thehigh degree of application of these skills by frontrunners during AIimplementations.Nordic bank Nordea is using AI to lead multiple efforts across theorganization. Nova, an internally developed chatbot, uses natural languageprocessing to interpret customers’ queries and decide the relevant response.Another project uses algorithms to study central bank documents and understandthe central bank’s economic perspective. The bank is also actively evaluatingopportunities to deploy AI for automating claims handling, detecting fraud,and providing personalized recommendations to clients.7

Adopt a portfolio approach for acquiring AI


As market pressures to adopt AI increase, CIOs of financial institutions arebeing expected to deliver initiatives sooner rather than later. There aremultiple options for companies to adopt and utilize AI in transformationprojects, which generally need to be customized based on the scale, talent,and technology capability of each organization.From our survey, it was no surprise to see that most respondents, across allsegments, acquired AI through enterprise software that embedded intelligentcapabilities (figure 9). With existing vendor relationships and technologyplatforms already in use, this is likely the easiest option for most companiesto choose.For example, Guidewire, maker of enterprise software solutions for insurancecompanies, offers its users access to AI capabilities through its PredictiveAnalytics for Claims app. The app utilizes machine learning algorithms tocategorize claims based on their severity and the potential for litigation,automatically routing any high-priority claims to the correct departments.8Similarly, Salesforce helps users access AI through its Einstein program,which applies machine learning to historical sales data and predicts whichprospects are most likely to close.9However, the survey found that frontrunners (and even followers, to someextent) were acquiring or developing AI in multiple ways (figure 9)—what werefer to as the portfolio approach.This portfolio approach likely enabled frontrunners to accelerate thedevelopment of AI solutions through options such as AI-as-a-service andautomated machine learning. At the same time, through crowdsourced developmentcommunities, they were able to tap into a wider pool of talent from around theworld.Adopting the portfolio approach could help companies preserve the legacybusiness process while utilizing AI for incremental gains. American FidelityAssurance, a US-based health and life insurance company, was evaluatingoptions to improve the handling of a growing volume of customer emails andmapping the flow to different departments. The company’s R&D team wasexploring both robotic process automation (RPA) and machine learningapplications, albeit separately. While RPA was a good match for automaticallysending mails to the correct department, it was providing too many rules foridentifying the right department based on the email’s subject and keywords. Toresolve this, the team decided to explore automated machine learning with thehelp of a third-party vendor. Using the database of customer emails andeventual department response (outcome), the company found a well-fitting modelwithin a few hours. This model was converted to an application programminginterface (API), which was combined with RPA to automate the entire emailclassification, department identification, and mail-forwarding process.10

Significant challenges could lie ahead


As financial services companies advance in their AI journey, they will likelyface a number of risks and challenges in adopting and integrating thesetechnologies across the organization. But not all are facing the same set ofchallenges. Our survey found that frontrunners were more concerned about therisks of AI (figure 10) than other groups.With the experience of several more AI implementations, frontrunners may havea more realistic grasp on the degree of risks and challenges posed by suchtechnology adoptions. Starters and followers should probably brace themselvesand start preparing for encountering such risks and challenges as they scaletheir AI implementations. Indeed, starters would likely be better served ifthey are cognizant of the risks identified by frontrunners and followers alike(figure 11) and begin anticipating them at the onset, giving them more time toplan how to mitigate them.We observed a similar pattern in terms of the skills gap identified bydifferent segments in meeting the needs of AI projects (figure 12). Morefrontrunners rated the skills gap as major or extreme compared to the othergroups. While a higher number of implementations undertaken could partlyexplain this divergence, the learning curve of frontrunners could give them amore pragmatic understanding of the skills required for implementing AIprojects.Delving deeper into the capabilities needed to fill their skills gap, morestarters and followers believe they lack subject matter experts who can infusetheir expertise into emerging AI systems, as well as AI researchers toidentify new kinds of AI algorithms and systems.While these skills are often necessary in the initial stages of the AIjourney, starters and followers should take note of the skill shortagesidentified by frontrunners, which could help them prepare for expanding theirown initiatives. Frontrunners surveyed highlighted a shortage of specializedskill sets required for building and rolling out AI implementations—namely,software developers and user experience designers (figure 13).User experience could help alleviate the “last mile” challenge of gettingexecutives to take action based on the insights generated from AI.Frontrunners seem to have realized that it does not matter how good theinsights generated from AI are if they do not lead to any executive action. Agood user experience can get executives to take action by integrating theoften irrational aspect of human behavior into the design element.That said, financial institutions across the board should start training theirtechnical staff to create and deploy AI solutions, as well as educate theirentire workforce on the benefits and basics of AI. The good news here is thatmore than half of each financial services respondent segment are alreadyundertaking training for employees to use AI in their jobs.

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