BigML Customer Success Highlights – Part 2

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In this post, we continue revealing BigML customer success stories that we kicked off with our last post detailing how a number of startups are basing their smart applications and services on the BigML platform. Those companies have profited from adopting BigML rather than taking the costly and risky approach of trying to build their own Machine Learning infrastructure that could divert their attention away from their core predictive use cases.

Today, we get into a potpourri of business problems tackled with the help of the BigML platform by large multi-national businesses. We see multiple scenarios play out as businesses with global footprints go about consuming Machine Learning. This also holds true for the sample of predictive use cases outlined in this post as we give you a glimpse of the motivation behind solving each reference application.

BigML Customers

Industry-specific use cases

Every industry contains a portfolio of data-rich workflows as part of the associated core operations and standard practices. Hard-coded business rules or knowledge-based approaches tend to govern many of those processes leaving room for further improvement with the introduction of Machine Learning approaches that frequently yield dramatic increases in productivity.

  • Rabobank, one of the largest banks in The Netherlands, is a great example of such a use case. Rabobank was faced with the challenge of having to manually analyze a very large volume of payment transactions to guard against potential financial transaction fraud. A set of heuristics and business rules existed but were difficult and time-consuming to manage. The team tasked with the monitoring was overwhelmed with the number of payments flagged by existing systems. There had to be a smarter way to deal with this situation without having to lose the gains made so far or multiplying headcount continually. As a result, they chose to focus efforts on a new Machine Learning-driven approach letting the algorithms do the hard work of sifting through hundreds of thousands of transactions to reveal the highly anomalous ones. The resulting models were able to pinpoint problematic transactions in a highly accurate manner, which is why they were eventually embedded in Rabobank’s commercial fraud detection point solution. Fraud detection is not a “one and done” type problem so the models are continually monitored against covariate shift and are automatically refreshed as new data arrives in order to stay ahead of the fraudsters.
  • In a somewhat similar vein, Seagate, the world-renown manufacturer of computer hardware headquartered in Silicon Valley routinely manufactures and services millions of parts such as hard drives, which are covered under the company’s product warranty programs that can at times be abused by fraudsters that are always looking to game those programs by inventing schemes like returning counterfeit parts in the hope to receive back the genuine article. BigML-based fraud detection models have been able to successfully identify suspicious return patterns that have helped Seagate’s customer service and security teams focus their limited attention on truly anomalous instances while minimizing false positives that could negatively affect customer satisfaction metrics.

Enterprise support functions

Modern enterprises have complex ways to organize themselves into a multitude of functions, e.g., finance, marketing, sales, operations, legal, HR and more. Some of these functions are considered to be ‘core’ such as operations while others can be portrayed as ‘support’ functions. Because most companies that begin investing in Machine Learning have done so by creating central teams with advanced technical degrees, they tend to concentrate on a few use cases revolving around the core activities. This results in an imbalanced picture that starves ‘non-core’ functions of any Machine Learning capabilities save for basic ones baked into standard third-party SaaS tools.

  • Experiencing a similar challenge with their Human Resources function, ABN-AMRO chose to get on board with BigML to predict key employee metrics, e.g., likelihood to vacate positions in upcoming periods. With positive results in supporting ongoing retention efforts, this use case has proven that with a Machine Learning platform like BigML (and some training) any enterprise function or department can reskill employees and employ a self-serve analytical approach by creating custom workflows and optionally integrating the resulting predictions in relevant IT systems to better adapt to challenges they face.

B2B platform use cases

  • In addition to the above, there are certain situations that involve embedding predictive capabilities in platforms offering B2B services as the primary beneficiary.  In such instances, the need for automation is paramount besides the ability to offer analytical end-users of client businesses ways to visually interpret the underlying custom models they can build on the subject-matter B2B platform in a self-serve manner.  Dun & Bradstreet represents such a scenario as they have chosen to integrate BigML’s resources into their Analytics-as-Service B2B platform gaining time-to-market and scale while being able to control cost by fully automating workflows on behalf of their clients.

There are too many use cases to list here among those explored on the BigML platform either by our Private Deployment customers or by more than 100,000 registered users on our multi-tenant cloud platform offering a wide spectrum of subscription choices.

The main lesson learned here is that the Machine Learning consumption behavior of large organizations cannot be pigeonholed into a few perfunctory scenarios e.g., build vs. buy. The shades of grey do matter here. However, we can make some broad-based recommendations. Presented with such a foundational piece of technology that has the potential to eventually touch every operational process, businesses can benefit from a longer-term strategic approach to ML adoption rather than solely a use case-specific outlook saving the day with incremental improvements.

The latter approach may at times be satisfactorily implemented through third-party point solutions baking in some predictive capabilities generally based on standard data models such tools contain, e.g., predictive features baked into a CRM tool. Nonetheless, this piece-meal approach may fall short if further customization is desirable to better leverage custom data sources and may, in fact, result in unwanted system integration costs leaving host businesses with siloed bespoke systems.

If you have a similar business problem as the above or have an idea of a new and potentially game-changing analytical use case in your industry, be sure to get in touch with us at We can swiftly match you with a BigML expert, who can help you better formulate your approach by advising you on your data strategy, modeling (and evaluation) strategy, as well as your run-time prediction and deployment strategies.

In short, the BigML team is ready to help you have a merry Machine Learning-filled new year in 2020!


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