3 Considerations for Getting Started with Artificial Intelligence in your Operation

This article is part of an ongoing Think Differently series, launched in October 2019. Written by members of the iA Innovation Council, the series showcases thought leadership in analytics, communications, payments, and compliance technology for the accounts receivable management industry.

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One of the most common misconceptions about Artificial Intelligence (AI) is that it’s all a black box that runs itself — all you need to do is plug it in, let the machine do its learning, and allow the algorithms to take over. 

Wrong. Just like any new partnership or team member, AI requires an onboarding process to establish familiarity with operations, time to figure out how to do its job well, and regular check-ins with its manager. 

In a recent Forbes article, Joe DeCosmo lays out steps for how fintechs can implement AI into middle and back-office operations first, before they go “all in” and extend it to front office (consumer-facing) ops.

Embrace redundancy and remediation

For instance, “embrace redundancy and remediation.” Every automated process should be tested against a manual process to make sure it’s doing what it’s supposed to do (only quicker and better than the manual model.) AI is no different, and I encourage people to start with one facet of their business, worked with both a machine learning and a manual process concurrently. Not only does this enable you to see immediate results of your AI implementation, it also allows a firm to understand what decisions are being made and the kind of algorithms that are being built.

Monitor everything

“Monitor everything” is another recommendation, which is, of course, a requirement for staying compliant in more ways than one. For instance, an algorithm is developed based on a set of factors that seem to work, but if that algorithm goes unmonitored for an extended period of time, it could also develop a pattern of unwanted or suboptimal practices that may be tougher to explain to regulators than the technology itself. (Although the CFPB has championed AI for cutting down on discriminatory practices, the possibility is still an example of what could happen when a firm adopts a laissez-faire approach to an investment in AI.)

Ensure explain-ability

Ensuring explain-ability must be paramount when exploring machine learning strategies. Having the ability to access and generate reports on your algorithms is a necessity, and you must make sure those reports are transparent and explainable to different stakeholders within an organization, and therefore its regulators. 

While AI is probably the most exciting new tool for fintechs and can easily live up to its hype, it’s not a set-it-and-forget-it kitchen appliance (even a crockpot ultimately needs a human at the helm). If your firm is considering implementing AI into any part of its operations, you’ll find that half-measures rarely produce big results. Even if you’re onboarding AI incrementally, a dedicated CTO, analyst or data scientist within your organization should be charged with managing it: keeping an eye on its activity, communicating tweaks and changes, and maintaining the right controls.

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Gregory Allen is the Founder and CEO of Pairity, an AI platform that offers Machine Learning as a Service to the accounts receivable industry.

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About the iA Innovation Council

The iA Innovation Council is a collaborative working group of product, tech, strategy, and operations thought leaders at the forefront of analytics, communications, payments, and compliance technology. Group members meet in person several times each year to engage in substantive dialogue and whiteboard sessions with the creative thinkers behind the latest innovations for the industry, the regulators who audit and establish guardrails for new technology, and educators, entrepreneurs and innovators from outside the industry who inspire different thinking. 

Learn more at www.iainnovationcouncil.com

2019 members include: