Big Data and Machine Learning for Equipment Financing

By: Angelina Frimpong  
June 25th, 2019

 

Providing Business Insight and Automation through Big Data and Machine Learning

What is big data?  Is it just another buzzword in business technology?  To some, big data refers to voluminous and complex data used for analysis.  To others, it describes the technology to process complex datasets to extract actionable details and trends.  Gartner (2012) defines Big Data as: “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

  • Volume refers to large amounts of data, from terabytes to zettabytes in size.
  • Velocity refers to the speed at which information is produced.
  • Variety refers to different forms of data (structured, semi-structured and unstructured).

Business Sources of Big Data

Big data is produced by a myriad of sources, such as:

  • Media: the most popular source of big data, as it provides valuable insights on customer preferences and changing trends.  Examples are Google, Facebook, Twitter, images, videos and audios.
  • Databases: information handled by the likes of SQL, Oracle, and MS Access.
  • Internet of Things (IoT): all the equipment connected to the Internet, ranging from computer peripherals to point-of-sale terminals, home appliances and automobiles.
  • Sensors: they collect data from a variety of obvious and not so obvious sources, such as geolocation, attention, engagement levels and biometrics.

Use Cases of Big Data and Machine Learning in Equipment Financing

Big Data provides organizations with huge opportunities to gain actionable insights, competitive advantage and improve human decision-making.  However, these data are usually hidden so deep that organizations need to have the skills, technology and innovation to manipulate it and create value.  Medical researchers and healthcare systems are harnessing the power of big data to discover trends and treatments with higher rates of success.  Large retailers are better forecasting demand and supply with the help of big data.

The Equipment Finance industry also has the potential of tapping into big data.  According to a forward-looking study conducted by Genpact, Inc. and published by the Equipment Leasing and Financing Foundation (ELFF), “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.”

Predicting Loan Repayment with Machine Learning

The critical task of estimating a borrower’s creditworthiness and risk profile remains an unstructured problem with multiple correct answers: no single rule exists to predict a borrower's likelihood of repayment.

Machine learning models (that use algorithms and statistical models to make predictions) can be used to forecast the performance of a loan to reduce risk.  A set of data variables can be trained on models like K-Nearest Neighbors (KNN), tree-based classifier Random Forest (RF) and Support Vector Machines (SVM) to predict the behavior of the borrower in the future.  Given a set of data consisting of key data points such as amount financed, lease term, credit grade, and monthly payments (among others), a machine learning model can help to determine which combination of variables truly impact loan performance.  Companies like ZestFinance, a financial technology company, claims to use more than just the conventional credit information to create proprietary big data credit scoring models.  The company goes by the “all data is credit data” approach to combine both the online and offline activities of customers to determine creditworthiness.

Automating Underwriting and Credit Scoring

Process automation is using machine learning capabilities to replace some manual and repetitive office tasks to improve process efficiency and accuracy.  Processes like data entry, application review, invoice generation, underwriting and credit scoring are good candidates for automation.  Providing customers with a rapid response goes a long way in improving customer satisfaction and giving the company a competitive edge.

Using big data and machine learning techniques, credit underwriting rules could be coded to reduce the amount of time underwriters would require in decisioning an application.  A set of models can be used to sift and sort through thousands of data points available for each customer to automatically detect patterns in the data.  The resulting model may be able to score more people, or more accurately score people, by finding previously unrecognized correlations.

A well-trained system can then perform similar underwriting and credit-scoring tasks in the real-life environments.  Such scoring engines help human employees work much faster and more accurately.

Generating Leads and Sales with Big Data

Leveraging big data into marketing strategy can help improve lead generation by helping to predict which leads are most likely to close.  It can provide insights into the ideal customer to approach and which content is the most effective at each stage of the sales cycle.  Timeliness in reaching out to leads is the factor that makes the difference.

 

Historical data in the form of payment history, equipment previously financed, successful marketing messages and other big data sources can be used to determine customer interests.  Analytical models can then be developed to determine next equipment to lease and timing for next recommendations.

 

The possibilities of big data and machine learning really are endless.  They offer tangible benefits to all industries, including equipment financing, by enabling enhanced insight, decision making and process automation.  A powerful data-driven strategy is a necessity to succeed in various aspects of business development.

 

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