The Foundation has issued two new RFPs for studies on big data for the asset finance businesses and an assessment of the software leasing industry.
The study tentatively titled, “Big Data for Asset Finance Businesses, Large and Small,” will address this topic that is making headlines around the world. As companies today collect unimaginable amounts of data from a multitude of sources, from customers to vendors, from prospects to partners, significant issues and challenges around how to manage and glean insights from the data in an efficient and effective way have arisen. For the equipment finance industry, collecting, processing, and analyzing millions of customer and transaction records in order to better understand issues around customers, markets, products, services and profitability is a daunting task.
This new study will define the big data issue, put that definition into context for the industry, and showcase the variety of ways asset finance companies could benefit from using big data. The study will address the technology, process, and personnel aspects of incorporating big data into a business.
Other issues to be addressed include:
- If equipment leasing companies were to record all proposals during negotiations, what big data concepts could be applied?
- What data is or could be considered?
- How does data need to be captured, and how will it be used?
- Is a big data program appropriate for large companies and small companies alike?
- What roles are business intelligence and predicative analytics taking in their interaction with big data?
- Do mathematical models need to be built?
- Is a big data program essential, or a nice to have? Will a company be “left out” of the competitive race if it does not have a big data program?
- What will result from actively maintaining a program to analyze big data?
- What resources are needed to implement and maintain an effective big data program?
The second study, tentatively titled, “Introducing the SELFI - The Software (and Equipment) Leasing and Finance Industry,” will address this growing section of the equipment leasing and finance industry. Software – with more than $170B financed annually -- has become the industry’s single largest asset class representing more than 25 percent of all leasing and financing volume. Not long ago financing penetration of this asset class was negligible. Today the financing of these assets is ubiquitous.
The study will examine the all the dimensions of the software financing marketplace including the market and competitive landscape and the risk, legal, regulatory and accounting environment, delving into the depth and breadth of the software financing market, its key drivers and challenges and the future implications for financing companies.
- What is the history of the evolution of software as financeable asset class? Initial issues, integration into hardware, converged platforms (appliances, etc.), marriage of software and technology? What has been the growth path? Are product life cycles accelerating? What are the implications?
- What are the unique characteristics of those segments, what are their relative sizes, what are the key drivers and growth prospects?
- Who are the participants in these markets and how are their needs changing? (ISVs, vendors, channel partners, system integrators)?
- How are financing sources addressing these needs?
- How are software deals typically structured? How has the structuring of these transactions changed over time? What are the implications for revenue recognition?
- What regulatory and capital considerations are relevant? What determines borrowers’ decision to acquire, expense, amortize? What is the expected impact of accounting changes on finance companies and borrowers?
- How are code controls employed? What remedies are available in default to repossess, enforce ownership rights? How does this vary by captive/bank/independent perspective? What is the value of asset vs version control?
- What has been the performance from a credit and pricing perspective vs. other asset classes over time? (PayNet data)
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