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This article was provided by Mamdouh Refaat, chief data scientist, Altair
Machine learning (ML) and artificial intelligence (AI) were once concepts relegated to only the most optimistic observers, much like self-driving electric vehicles and smartphones once were. But if it isn’t obvious, the times have changed.
Today, ML and AI—along with the immensely powerful data collection and analytics tools that power those processes—are a mainstay of modern life. Every day, people interact with products and services powered by some of the world’s most ground-breaking technology.
In the financial sector specifically, ML and AI present an enormous opportunity to institutions to revolutionise their businesses and generate both top- and bottom-line results. The technologies can be used to assess client creditworthiness, detect and prevent financial crimes, and improve customer experiences.
According to a recent ML adoption in Australia report, there is a strong appetite for ML in the local market, with 82% of organisations being interested in ML. In addition, 86% of respondents see ML as being critical or one of the several important technologies going forward, and 49% of those who have not yet started plan to do so in the next 12-24 months.
But ML, AI, and data science tools have historically been undemocratic, inaccessible technologies, meaning that only the most advanced users in select organizations and industries could utilize them. According to the same report, only 69% of organizations with models in production reported sufficient ML capability.
This is partly because these technologies are complex, as is getting the high-quality, abundant, and secure data that’s crucial to their success. In addition, it can be challenging for IT departments within organizations to open the data sources, operating systems, and deployment technologies that facilitate the implementation of enterprise-wide ML and AI.
That said, today’s ML, AI, and data analytics tools are easier to use than ever, and are only becoming more accessible. Additionally, organizations—even small ones—have access to more data than anyone would have imagined a few decades ago, giving them the information they need to build ML and AI strategies that can make their operations, products, and services more efficient, more cost-effective, and better for customers and employees alike.
Moreover, more students and professionals are using ML, AI, and data analytics software, which gives organizations more talent to choose from when building teams that can turn concepts into action. In other words, there’s never been a better time to invest in ML and AI.
Indeed, ML and AI is a game-changer, and provides mind-boggling ROI when supported by a solid team of data scientists, analysts, and technology that ensures it can evolve and grow. And today’s ML and AI software is also more transparent than ever, often incorporating explainable AI features that show users exactly how the algorithms and technology is interpreting, organizing, and acting upon the data it’s drawing from.
But most importantly, the proliferation of low-code and no-code ML and AI technology has opened doors to users who otherwise might not have the technical expertise needed to craft strategic models. By giving non-experts—who are often closer to an organization’s tactical operations—access to technology that can help them apply intelligent, data-driven insights, organizations can rethink the way they operate. From finance departments to HR, marketing to sales, engineering to risk analysis, there are more ways to use ML and more people that can use it.
But while it’s easy to sing the praises of new, exciting technology, every organization (and layperson) should be thoughtful and ask themselves: Why democratization? After all, giving more users access to vital data can create potential security risks, and giving non-data scientists the freedom to create ML models can lead to potentially life-altering mistakes—especially in industries that greatly impact people’s well-being like healthcare, insurance, and finance.
The answer is that democratization can also enable a flood of ground-breaking innovations that do immense good, that make people’s lives healthier, safer, more sustainable, and more secure. The world only has so many data scientists – if more people (non-data scientists) in more industries have ML and AI know-how in their toolkit, it gives them the ability to combine their domain knowledge with powerful tools that can help them achieve their goals and create better services, products, processes, and experiences for everyone.
Bear in mind that the democratization of ML, AI, and data analytics won’t happen overnight – but the gears are turning, and the world’s largest players and most innovative small start-ups alike are laying tomorrow’s AI-powered foundation. As the technology continues to grow and develop – along with people’s ability to conceptualize and implement it – it’ll only become a more integral aspect of modern life. In the near future, it’s likely that ML and AI will be embedded into our technology so seamlessly we forget it’s there.
Much like a self-driving car tracks movement, visualizes road conditions, and detects signs and signals all thanks to data and ML, it’s possible tomorrow’s bicycles and trains may do the same. The same goes for tomorrow’s credit lending industry, healthcare operations, emergency response infrastructure, and more.
In all, organizations and users should be thoughtful and thorough when implementing the ML and AI tools of the present and future, but it’s also an opportunity to make tomorrow’s world a safer, greener, more accessible, and more efficient place.
Dr. Mamdouh Re
faat
Chief Data Scientist
Altair
Mamdouh is Altair’s chief data scientist and senior vice president, product management where he is responsible for the company’s data analytics products. Refaat is an expert and published author with more than 20 years of experience in predictive analytics and data mining, having led numerous projects in the areas of marketing, CRM and credit risk for Fortune 500 companies in North America and Europe.
Refaat joined Altair (then Angoss) in 1999 to establish the company’s consulting practice before assuming leadership for data science. Prior to joining Altair through the acquisition of Datawatch, he held positions at Predict AG (acquired by TIBCO Software) and UBS in Basel, Switzerland.
Refaat earned a PhD in Engineering from the University of Toronto and an master of business administration degree from the University of Leeds.
Mamdouh’s notable publications include the books, “Data Preparation for data mining Using SAS, 2006,” and “Credit Risk Scorecards: development and implementation using SAS, 2011.”
