How Big Data Will Transform the Equipment Finance Industry

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4. a bout Genpact Genpact Limited (NYSE: G) is a global leader in transforming and running business processes and operations. We help clients become more competitive by making their enterprises more intelligent: more adaptive, innovative, globally effective and connected. Genpact stands for Generating Impact for hundreds of clients including over 100 of the Fortune Global 500. We offer an unbiased combination of smarter processes, analytics and technology through our 62,000+ employees in 24 countries, with key management based in New York City. Behind Genpact’s passion for process and operational excellence is the Lean and Six Sigma heritage of a former General Electric division that has served GE businesses for 15+ years. For more information visit www.genpact.com/home/industries/banking-financial-services or contact banking.solutions@genpact.com Follow Genpact on Twitter, Facebook and LinkedIn. © 2014 Copyright Genpact. All Rights Reserved.

1. PROCESS • ANALYTICS • TECHNOLOGY How big data will transform the equipment finance industry Generatin G Commer C ial Bankin G i mpa C t Point of View The equipment leasing and financing industry has yet to realize the benefits of big data developments on a substantial scale. However, this is beginning to change as the potential for advanced analytics becomes increasingly clear to industry leaders, along with lessons from similar industries. The Equipment Leasing and Financing Foundation (ELFF) commissioned Genpact to carry out a forward-looking study of the potential benefits for the industry. “Big Data: A Study for the Equipment Finance Industry” draws on Genpact’s experience as a global leader in business process and technology management services and big data analytics implementation. This article provides additional insight into these research findings and explores the value of analytics as a business process that supports equipment finance operations.

3. GENPACT | Point of View • Consolidate what is known about existing data elements and then integrate new information sources, with the understanding that the implementation must adapt to new realities as they become apparent • Identify every data type and source that can materially inform business decisions while simultaneously exploring opportunities for cross- links and aggregation strategies • To yield maximum returns, infrastructure investments must be carefully planned, starting with an assessment of alternative deployment models including deciding between off-the-shelf big data platforms or an in-house infrastructure (such as a commodity cluster) A key success factor is leadership from the C-Suite. Top-level engagement is needed to cut across organizational silos and to bring together the right combination of stakeholders, data analysts, and information specialists to ensure that the project stays on track to meet its goals. t aking a strategic approach Big data analytics are rapidly entering the mainstream of management tools as intelligent enterprises search for new ways of generating business impact. Big data technologies have evolved rapidly over the past few years, reaching critical mass only around 2011, and gaining increasing traction since then. The ELFF study found that, globally, 64% of companies have already invested in some aspect of big data or say that they plan to invest by 2015. This includes equipment leasing and financing firms that want to separate themselves from the pack. These firms stand to reap substantial rewards, provided they strategically plan implementation, focusing investment on tools that align with existing business objectives and long-term goals. This means prioritizing business challenges and systematically applying the proven techniques of big data—starting, of course, with the applications that offer the biggest payoffs, but also clearly focusing on an operating model that can effectively embed analytics into the fabric of business operations. Analytics is not a task: It is a business process supporting others . more robust risk assessment, and an improved customer experience. All of this adds up to a healthier bottom line. Big data analytics enables improved decision making through several modes. It creates transparency by analyzing and delivering all relevant information over networks that can provide superior visibility into the business. It enables real- time assessment of data from multiple sources, revealing previously unseen patterns. Advanced analytics can even build models to test hypotheses and enable simulation of proposed business models and strategies. The result can not only be better products and services but also the creation of en tirely new offerings. Best practices for implementation The ELFF study of applications in parallel markets reveals several best practices to guide big data implementations. The most significant takeaway from this research is the importance of assessing potential scenarios for big data implementation using an end-to- end, enterprise-wide approach. The goal is to create a flexible framework that can accommodate evolving objectives across the business over time, while ensuring the scalability and cost effectiveness of the data-to-insight- to-action process. In short, there is increasing evidence that analytics must be treated as a business process, and can be “industrialized” to enable embedding the analysis at scale within the enterprise. Other best practices include the following: • Engage stakeholders for each business objective, cutting across specialized functions to achieve a holistic perspective. Consider organizing stakeholders into brand teams who want to understand customers better Most significant takeaway from this research is the importance of assessing potential scenarios for big data implementation using an end-to- end, enterprise-wide approach

2. The following are some of the key focus areas: • Fraud detection - Predictive analytics detect unusual spending behavior, enabling rapid customer contact when warning signs appear • Risk management - Integration of customer data from new sources broadens the scope of traditional credit ratings • Personalization - Big data analytics compensate for the loss of personal interaction over online channels by creating 360-degree customer views and targeting customized product offerings • Predicting customer behavior - Advanced analytics reveal customer needs and enable up- selling and cross-selling opportunities • Emotional connections - Sentiment analysis can capture customer feedback through social media and other channels to react quickly to resolve complaints and to design loyalty programs t he payoff for equipment leasing firms Big data computing has considerable potential for the equipment leasing business today, and in the longer run is positioned to transform the industry. As the ELFF study puts it: “The use of big data by equipment leasing and finance firms may result in a more comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, and employees.” Applying predictive analytics—the engine of big data—is limited only by a firm’s capacity for innovation. When decision making is supported with actionable insights drawn from real-time data, equipment leasing and finance companies can achieve more responsive deal structuring, GENPACT | Point of View Big data and the equipment leasing and finance industry The equipment leasing and finance industry has emerged from the economic downturn with renewed vigor. Now the industry faces another challenge. Today’s uncertain market has intensified competition, pressuring firms to cut costs while at the same time customers have grown increasingly cautious. Leading firms are meeting these challenges with more sophisticated systems for understanding market trends, achieving customer insights, and building operational agility. The need to operate “smarter and faster” is frequently acknowledged but less often realized because efficient decision making increasingly requires consolidating and analyzing massive volumes of business information. This is where big data is most often discussed. The fact is, the task of integrating large amounts of disparate information from multiple sources—in real time—has overwhelmed traditional database technologies. Big data tools solve this problem with advanced software running on massively parallel computing systems, typically “commodity clusters” of existing low-cost systems. The challenge for every industry is developing a practical plan to take advantage of the wealth of data that is now available. t aking cues from the banking and financial services industry Industry advances in data management have enabled the nuanced analysis of millions of terabytes of data, highlighting clear and actionable patterns from what was, until recently, information overload. The banking and financial services industry in particular has been a leader in the use of big data analytics, especially in customer engagement and risk management. Use of big data by equipment leasing and finance firms may result in a more comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, and employees The task of integrating large amounts of disparate information from multiple sources— in real time—has overwhelmed traditional database technologies

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