Artificial Intelligence (AI) is not new. These days, it would be difficult to find a business that isn’t at least attempting to use AI. However, too often the development of AI is described in a similar way to traditional coding: “compiled,” “built,” and “run”. This implies that AI is built once and then adjusted occasionally as needed.
AI is, however, incredibly dynamic, and requires more than a “set and forget” approach.
Developing and implementing AI-based machine learning systems requires planting algorithms in rich data soil and cultivating them. As these systems search real-time data for patterns, their insights expand with their inputs.
As a result, using traditional coding language to describe AI can mislead businesses about the realities of developing AI and severely limit the benefits.
To avoid this, businesses need to consider this technology less in terms of ‘building code’, and more as ‘cultivating a system.’
The core utility of AI is to enable technology systems to transform data into meaningful insights that lead to smarter decision-making.
As society generates and stores more data, AI’s ability to make use of this information is crucial to unlocking its power. It will enable a world of smart cities, homes, cards, and goods, and will serve as the refinery for new data generated by those connections.
To design fit-for-purpose AI systems in this environment, businesses need to connect the critical business opportunities AI can address with the optimal technology solution. A company needs to understand the data that is on-hand and design an AI and machine to generate the desired end decisions.
Having organised data sets is key, and companies should be vigilant about protecting against bias within their AI systems.
As AI systems grow in size, they become increasingly vulnerable to concept drift.
Concept drift happens when the relationship between the inputs a model receives and its target insights changes and becomes outdated. At Mastercard, the growth of e-commerce has greatly expanded the sources of data we have to monitor fraud, but also expanded the opportunities for fraud to happen. The comprehensiveness of our systems means that finding and eliminating one kind of fraud can move the market to develop new kinds of illicit activities driving concept drift in our own environment.
To combat this, we have developed a second monitoring system, ‘AI for AI’, to oversee the machine-based system used for fraud prevention. This second system tracks the inputs and outputs of the first, flagging any anomalies that could signal drift.
This structure sharpens our fraud monitoring and analysis, and reduces the consumer inconvenience of false declines. Mastercard’s SafetyNet product prevents billions of dollars in potential fraud losses every year. It has also greatly improved the rate of false declines, thereby increasing consumer convenience and sales for merchants.
We are also ever-improving the data we have to analyse. For example, our recent acquisition of NuData allows us to identify and feed unique biometrics, such as the way one handles and uses a phone– into our fraud analytics. This helps more accurately authenticate the user and adds to the precision of our SafetyNet monitoring.
Planting the AI Seed
While AI plays a powerful role in improving and enhancing our fraud mitigation capabilities, so do people in deciding how to act on new information and adjusting the system to maintain quality AI inputs and outputs.
Finding the right talent to manage AI in this way is a challenge organisations face. Like many companies, Mastercard invests in preparing the next generation of technologists. Through programs like Girls4Tech, designed to encourage school-age girls around the globe to pursue STEM careers, we’ve reached over 400,000 young women. We recently announced a new AI curriculum to this program.
While much is being done to get more students involved with STEM curriculum, and tech companies are providing easier routes into new professions, companies need to invest more in upskilling workers.
At Mastercard, learning is the new currency. Tech employees receive regular opportunities to learn and upskill in hot fields like AI and cybersecurity. We drive a continuous learning culture, including helping employees take advantage of educational opportunities and non-traditional training programs. These empower stronger human-machine collaborations through environments where employees experiment and learn to work with AI as it’s being applied within their industry. They are also designed with the expectation that data will constantly evolve, new patterns and more roles will emerge to manage and monitor AI and machine learning systems.
All of this skills development requires upfront investment – and patience – but the rewards can be great.
AI and machine learning systems promise to deliver ground-breaking insights and precision decision-making for those focused on “cultivating” them within an organisation versus building them.