Retail banks are data businesses. Their value chains have always been supported by data, and a large part of their competitive advantage is based on better use of the information that data provides and the insights it originates. Banks, along with retailers and telecommunications companies, have long had more consumer data available to them than other businesses.
Consumers embraced digital channels for all manner of commerce well before many businesses, and banks were among the first companies to take advantage of new streams of data. A few were early movers, employing advanced data analytics, establishing dedicated teams, appointing chief data officers, and investing substantial time, effort, and resources in building out infrastructure and enabling data analysis.
All that said, The Boston Consulting Group’s work with leading retail banks around the world shows that despite the early start and formidable resources, most banks are far from realizing big data’s full potential.
Data and analytics today bring the ability to combine three elements:
- Vastly bigger volumes of data, including highly detailed data combined from different systems
- Much more insightful models, powered by so-called machine-learning software, which can make data-driven predictions and decisions
- More efficient technology, such as Hadoop Clusters de software-hardware, que estão entre as maneiras mais econômicas de lidar com grandes quantidades de dados não estruturados estruturados e muito mais complexos
Além dos obstáculos básicos que sustentam as empresas em todos os setores, como resistência à mudança e falta de recursos qualificados, os bancos têm suas próprias razões para não ter feito mais progresso com dados. Isso inclui prioridades concorrentes, como abordar mudanças regulatórias após a crise financeira; Complexidade de TI (devido a sistemas multicamadas e dados em silêncio, os bancos raramente usam toda a amplitude e profundidade dos dados à sua disposição); e uma combinação de falta de visão geral e esforços amplamente dispersos e coordenados, que resultam em alocação abaixo do ideal de recursos humanos e técnicos e interação limitada e troca de idéias. Além disso, como os bancos geralmente trabalham com dados agregados servidos por seus sistemas, eles nem sempre apreciam o potencial incorporado na rica precisão e detalhes dos dados que possuem. (Consulte o Anexo 1.)
There are at least four areas in which focused and coordinated big-data programs can lead to substantial value for banks in the form of increased revenues and bigger profits. (See Exhibit 1.)
Melhorando as práticas atuais com análise de pontos
One of the simplest—and most powerful—applications of data analytics is the development of point solutions for individual needs and issues while steering clear of other areas. Big data can be used to improve the assessment of customer risk in a particular context, for instance. Data analytics can also be employed to more effectively measure marketing potential.
One large European bank, for example, used a combination of point solutions to upgrade its credit underwriting and pricing and to enhance the effectiveness of cross-selling and up-selling campaigns. The bank had been running campaigns to increase the share of high-end (gold and platinum) credit cards in its portfolio. It had been using both risk assessment and marketing analytics based on aggregated data to preapprove current standard-card customers and target potential new clients. Its transformation rate was an unimpressive 3% to 5%.
We helped develop a series of advanced-analytics models that can process far more detailed customer information—including data collected at the transaction level and compiled from multiple sources—related to credit risk, behavior, card use, and purchase patterns for other products and services. Using this data and the new models, the bank generated an entirely new series of risk and targeting scores. After a few adjustments were made on the basis of test campaign results, the new scores were applied to the bank’s full portfolio of card-marketing programs. Uptake surged fivefold to an average of more than 20%, and the bank generated tens of millions of euros in new revenues—without incurring the excessive costs often associated with new-client acquisition in saturated European banking markets.
Transforming Core Processes with Platform Analytics
Retail banks use data-driven reasoning in many of their core processes, such as new-product development, customer relationship management (CRM), and product pricing. But most banks use data that they are already capturing, such as structured data from accounting and reporting systems or other conventional internal sources, and they apply analytics to only a limited number of points in their core processes. More advanced banks have built a platform analytics capability that collects and analyzes not only internal multistructured data but also data from external sources. The internal data takes various forms and is often sourced from new digital channels and media. It can include, for example, customer interaction logs from the bank’s website, voice logs from call centers, and smartphone interaction logs. Additional data is collected from other sources, such as external databases, geolocation analyses, public websites, and social media. These banks develop insights and apply them at every information-exchange point of their core processes.
A análise da plataforma ajudou um grande banco dos EUA a melhorar substancialmente o desempenho de seu processo de cobrança de ponta a ponta. Após a crise financeira, o banco enfrentou dois novos desafios:
- Volumes sem precedentes de clientes que nunca haviam sido inadimplentes, mas agora enfrentaram problemas financeiros
- An increasing number of financially stretched customers who were juggling multiple credit-card accounts and credit lines—and deciding which cards or lines to let slip into default and which to keep in good standing with regular payments
The bank’s challenge was to identify at-risk accounts as early as possible, assess the borrowers’ capacity to pay, evaluate their willingness to pay (a totally new behavioral characteristic), and match borrowers with restructuring and rehabilitation programs that suited each borrower’s specific circumstances.
Data analytics were able to improve every step of the collections process, from early identification of delinquency to treatment selection to foreclosure—even to external-recovery channel management. By combining structured and unstructured data from internal and external sources—including a number of sources that were previously untapped—into new behavioral models, the bank was able to develop programs that were tailored to customers’ financial situations and predispositions. Big-data technologies also delivered accurate information about customers with outdated contact details, allowing the bank to increase effective outreach by more than 30%. The bank developed a new valuation approach for files in distress, which allowed the institution to more accurately reprice the portfolios of nonperforming loans for sale to external collectors.
Perhaps most significant, big data helped the bank better understand both the quality of the credit files coming into the collections process and the performance drivers of its collectors—as well as the interplay between the two. This yielded some surprising results. Established practices were built on the assumption that to maximize total collections, the most difficult files should be allocated to the best collectors. But an advanced-analytics analysis of the criteria used to determine which files were difficult and which were easy showed that allocating the easy files to good collectors actually maximized the number of files processed and, eventually, yielded a higher volume of collections.
As a result of the redesigned collections process and the optimization of each step, the bank increased the funds it collected by more than 40%, resulting in savings of hundreds of millions of dollars in bad debts that it would otherwise have written off.
Boosting IT Performance
Big-data IT technologies can both improve the capabilities and reduce the costs of bank IT systems. Linear scalability, in which banks buy only the hardware or software capability that they actually need; the use of inexpensive commodity-hardware components, especially for tasks that are computationally intensive; and the ease of manipulation of multistructured or unstructured data are all big steps forward for most financial institutions.
Banks can leverage these characteristics in several ways. These include efficiently processing the vast amounts of data generated by the omnichannel customer journeys common today; implementing more sophisticated, data-intensive models; and doing a better job of balancing the workloads of data warehouses that often operate close to saturation levels, thereby avoiding expensive upgrades.
A large European bank, for example, recently faced a conundrum with respect to its plans for a new data warehouse and CRM systems: the functionalities requested by the bank’s business units far exceeded the budgeted capacity of the new system, which was a traditional, though state-of-the-art, data warehouse. A review of the bank’s data storage and manipulation needs sparked the insight that led to a different—and much less costly—solution. The bank identified a series of applications using unstructured or multistructured data from various digital channels. Because traditional systems are not well suited to processing this type of data, they consume excessive calculation and storage resources. A new, hybrid data-warehouse architecture, combining traditional and big-data technologies and running on clusters of Hadoop commodity servers, accommodated all the functionalities needed by the business units and produced savings of almost 30% of the initial budget.
Criando novos fluxos de receita
Empresas em vários setores estão gerando fluxos de receita inteiramente novos, unidades de negócios e empresas independentes como resultado das informações fornecidas pelos dados que possuem. (Ver " Sete maneiras de lucrar com big data como empresa , ”Artigo do BCG, março de 2014.) Os bancos não são exceção; de fato, seus vastos volumes de dados oferecem oportunidades para as idéias dos clientes que outras empresas só podem imaginar. O desafio para os bancos está usando e manipulando dados de maneiras que respeitam a confiança e a privacidade do cliente. A UE promulgou especialmente regulamentos rigorosos nesse sentido. Ganhando confiança do consumidor em big data: uma perspectiva européia= , Relatório do BCG, março de 2015.)
Apesar dessas restrições, vários bancos de varejo encontraram maneiras de monetizar as idéias (em oposição aos dados) gerados por meio de suas atividades principais, tornando os dados do cliente que são usados em um bancos de varejo e de um exemplo de um exemplo e um exemplo de um exemplo e um exemplo de um exemplo e um exemplo de um exemplo e um exemplo de um exemplo de um exemplo e o que é um dos dados que são utilizados para outras empresas e o que é o que é um dos dados que são usados para outras empresas que são usadas para outras empresas que são usadas para o Banco de Bancos e Empresas de um exemplo de um exemplo e um exemplo de um exemplo e um exemplo de um exemplo e um exemplo de um exemplo e o que é o que é o que é um dos dados O painel exibe informações agregadas e de alto nível sobre cada estabelecimento, incluindo a idade e os colchetes dos clientes, os segmentos comportamentais aos quais os clientes pertencem e se são os primeiros ou recorrentes de clientes-informações que os restaurantes poderiam usar para melhor servir e vender seus patronos. Os restaurantes foram rápidos em reconhecer o valor do painel: alcançou a penetração de mais de 50% dos clientes de restaurantes do banco em apenas alguns meses. O banco projeta novas receitas de € 50 milhões, com uma margem de lucro de cerca de 40%-e depois de pagar pelo novo sistema de big-data do banco. Desde então, o banco lançou várias iniciativas semelhantes. Leva tempo para construir, funcionários, testar, ajustar e perfeitos programas de big data, para que eles funcionem em pleno potencial. Para os bancos, como para outras empresas, o Big Data é uma jornada. (Consulte o Anexo 2.)
In one example, a leading European retail bank used data from its payment-card unit to build a digital dashboard for restaurants and bars. The dashboard displays high-level, aggregated information about each establishment, including the age and revenue brackets of customers, the behavioral segments to which customers belong, and whether they are first or repeat customers—information that restaurants could use to better serve, and sell to, their patrons. Restaurants were quick to recognize the dashboard’s value: it achieved penetration of more than 50% of the bank’s restaurant clients in just a few months. The bank projects new revenues of €50 million with a profit margin of about 40%—and that’s after paying for the bank’s new big-data system. The bank has since launched several similar initiatives.
Getting the Most from Big Data
Data and analytics are powerful tools, but they are also complex, requiring technology, technical expertise, organizational and resourcing support, and, quite often, a test-and-learn approach to capitalize on their potential. It takes time to build, staff, test, adjust, and perfect big-data programs so that they function at full potential. For banks, as for other companies, big data is a journey. (See Exhibit 2.)
Most banks have already run pilot or proof-of-concept projects, and rightly so. This is the best way to validate the potential, identify issues, and get the first quick wins from big data. Speed and agility are crucial in creating big-data applications. Short cycles, iterative development, and frequent pilots should be the rule. Risk taking should be encouraged and mistakes accepted. Big data is often uncharted ground, and even disappointment—or, at least, carefully analyzed disappointment—can be a good teacher. Since companies can evolve and mature, even after an imperfect start, most banks will be able put themselves on the road to high-impact big-data success. We have created a basic roadmap to follow. Avalie sua situação atual. A maioria dos bancos já está usando big data, às vezes mesmo sem saber. Todo banco de varejo possui equipes que usam dados e técnicas analíticas relativamente avançadas em tarefas diárias, como avaliação e preços de risco e gerenciamento de campanhas. E a maioria dos bancos começou a experimentar as novas tecnologias de big data. Na maioria das vezes, no entanto, esses esforços são realizados de maneira fragmentada e não coordenada. Ainda mais frequentemente, a governança de dados é administrada em uma base ad hoc e com base em considerações puramente técnicas, não relacionadas aos negócios. Em muitos casos, os bancos também deixam de integrar novas oportunidades e funções analíticas para criar mais organizações centradas no cliente orientadas a dados. ATENÇÃO. Com muita frequência, os bancos têm uma visão estreita das oportunidades e capacidades necessárias para ter sucesso. As opções mais inovadoras - e potencialmente mais lucrativas - geralmente não são prontamente aparentes. Essa visão também molda o papel e o local do big data na organização e ajuda a determinar orçamentos, pessoal e estrutura da organização. O forte patrocínio nos níveis sênior envia um sinal para o restante do banco de que a alta gerência atribui alta importância aos dados e análises.
It is paramount for a bank to run a thorough diagnostic of its current data and analytics situation to identify the areas and capabilities in which it is close to achieving its desired state (or to aligning with the current state of the market) and those to which it needs to devote attention.
Develop a big-data vision. In our experience, the next step is the one that causes many banks to falter: moving beyond the diagnostic stage and building a vision of the role that data will play in the value chain, which includes identifying and prioritizing future applications and opportunities and evaluating the capabilities that the bank needs in order to successfully implement its plan. Too often, banks take a narrow view of the opportunities and capabilities necessary to succeed. The most innovative—and potentially most lucrative—opportunities usually are not readily apparent. That vision also shapes the role and place of big data in the organization and helps determine budgets, staffing, and organization structure. Strong sponsorship at senior levels sends a signal to the rest of the bank that top management attaches high importance to data and analytics.
Os bancos precisam criar um ambiente no qual novos aplicativos - ideas que realmente diferenciam uma empresa de seus concorrentes - podem ser rapidamente identificados e desenvolvidos. A exploração de novos aplicativos de dados deve ser incentivada em todos os níveis da organização, com os funcionários recebendo tempo e recursos para buscar suas idéias. É difícil exagerar a importância desta etapa. A ampla gama de conhecimentos necessários para identificar e desenvolver aplicativos exigirá as habilidades de muitas pessoas em toda a empresa. É vital, portanto, criar vínculos fortes entre profissionais que podem muito bem ter origens muito diferentes e muito pouca experiência em trabalhar um com o outro. O diálogo frequente e a colaboração contínua ajudarão essas equipes interdisciplinares a se concentrarem e priorizarão os problemas e oportunidades de negócios mais relevantes. Os processos formais podem estimular esse tipo de colaboração, assim como um impulso mais informal do topo. Estabelecer um roteiro claro para o sucesso que se concentra não apenas na criação de recursos, mas também em demonstrar continuamente o valor dos big data é essencial para alcançar a adesão e a construção de impulso. Com demasiada frequência, a incapacidade de reconhecer a amplitude dos recursos exigia que a capacitação de dados da organização e restrinja o impacto do big data a algumas áreas de impacto muito específicas e geralmente limitadas. Os bancos acabam construindo pequenos bolsos de excelência, mas não incutam em suas organizações uma apreciação do poder que o big data pode trazer. Como isso protege os dados? Ele usa a confiança do cliente como um diferenciador competitivo -chave?
Bring the organization along. Ensuring widespread success means overcoming organizational inertia and skepticism. It’s hard to overstate the importance of this step. The wide range of expertise needed to identify and develop applications will require the skills of many individuals across the company. It’s vital, therefore, to create strong links among professionals who may well have very different backgrounds and very little experience in working with one another. Frequent dialogue and ongoing collaboration will help these interdisciplinary teams zero in on, and prioritize, the most relevant business problems and opportunities. Formal processes can spur this kind of collaboration, as can a more informal push from the top. Establishing a clear roadmap for success that focuses not only on building capabilities but also on continually demonstrating the value of big data is essential to achieving buy-in and building momentum.
Cultivate the critical capabilities. Similarly, banks need to recognize that the requisite big-data capabilities are not limited to high-price, state-of-the-art hardware and software plus a team of data scientists. All too often, the inability to recognize the breadth of the capabilities required hinders the organization’s data enablement and restricts the impact of big data to a few very specific, and often limited-impact, areas. Banks end up building small pockets of excellence but fail to instill in their organizations an appreciation of the power that big data can bring.
Big data capabilities fall into three domains:
- Data Usage. How does the bank generate and manage new ideas? How does it secure data? Does it use customer trust as a key competitive differentiator?
- Data Engine. Qual é o melhor modelo operacional para cada banco em particular? What are the key combinations of technology and people necessary to build an efficient data engine? What is the best operating model for each particular bank?
- ecossistema de dados. Quais funções são internas e quais são externas? Qual é a estratégia ideal para a construção do ecossistema? Qual é o papel do próprio banco nele? (Veja o Anexo 3. Veja também Who are the partners, and what are the relationships that a bank needs? Which roles are internal, and which are external? What is the optimum strategy for building the ecosystem? What is the bank’s own role in it?
Banks need to address all three domains as they move from vision to execution. (See Exhibit 3. See also Habilitando Big Data: Construindo os recursos que realmente importam , BCG Focus, maio de 2014.) Esses recursos precisam ser construídos concluindo projetos específicos e discretos com casos de negócios mensuráveis e marcos claros. Grandes programas fundamentais que levam anos para oferecer valor comercial - se eles o fizerem - devem ser evitados. empresários, cientistas de dados e especialistas em TI. As equipes devem ser unidades fortemente ligadas que são essenciais para os negócios. Por último, mas não menos importante, os bancos precisam entender que o ritmo operacional é fundamental: não é tanto o que você faz, mas com que rapidez você faz. O foco dos bancos e de suas equipes de big data precisam estar na velocidade do mercado, desde a geração de idéias até a implementação final. Construir a estrutura da organização ideal é menos importante do que trabalhar multifuncionalmente e integrar dados e análises nos processos de negócios do dia-a-dia, com o objetivo de gerar rápido valor tangível.
Working on data and analytics requires compiling the right mix of skills early on, with dedicated resources working in multidisciplinary teams that combine businesspeople, data scientists, and IT experts. The teams should be tightly linked units that are core to the business. Last but not least, banks need to understand that operating pace is key: it is not so much what you do, but how fast you do it. The focus of banks and their big-data teams needs to be on the speed to market from idea generation to final implementation. Building the ideal organization structure is less important than working cross-functionally and integrating data and analytics into day-to-day business processes, with the goal of rapidly generating tangible value.
para
bancos de varejo, o big data já é grande negócio
. Mas, para muitos, pode ser muito maior ainda, à medida que o volume e a profundidade dos dados disponíveis crescem, os modelos analíticos melhoram e a sofisticação de executivos bancários e cientistas de dados aumenta com a experiência e o sucesso. Não há maior campo de jogo para big data do que bancário. Os bancos que elevam o jogo primeiro não apenas colherão recompensas financeiras imediatas, mas também estabelecerão recursos de dados e análises que serão difíceis para os concorrentes superarem.
Agradecimentos
Os autores gostariam de agradecer a Astrid Blumstengel, Julia Booth, Ravi Chabaldas, Nicolas Harlé e Claire Tracey por suas contribuições para este relatório. Eles também gostariam de agradecer a David Duffy por sua ajuda na redação do relatório e Katherine Andrews, Gary Callahan, Lilith Fondulas, Elyse Friedman, Kim Friedman, Abby Garland e Sara StraSsenreiter para suas contribuições para a edição, design e produção do relatório.
Elias Baltassis
