Operationalizing Machine Learning for Fraud: Human + Machine Learning is the Best of Both

Fraudsters are leveraging every tool and strategy they can to take advantage of large organizations. They are breaching big data exposures, wielding new technology, and manipulating vulnerabilities in the market complexity itself. At the same time, the industry is going increasingly real-time to meet customer demands of immediacy. Innovation teams and risk teams looking to the transformational power of AI to help them build holistic views of transactions and identities across a fragmented payments ecosystem, all without adding friction.

As enterprises try to make sense out of the generalized excitement over AI capabilities, we’re hearing many of the same questions. How will machine learning help my organization? How do I operationalize it? What capabilities do I look for?

To answer these questions, organizations should begin at the intersection of the human and the machine, and seek out the best of both. We’ve seen customers evaluating the technological impact without considering the human impact. We advise them to seek out a system that doesn’t just have the most advanced technology, but that will also make their most valuable employees even more valuable.

Augmenting the Human

In our just-launched ebook, Operationalizing Machine Learning for Fraud, we illustrate two AI advancements in particular — workforce augmentation and operational machine learning — that are streamlining workflows and driving functions like Data Science, IT, and Fraud toward increasingly advanced and strategic tasks.

For example, well-architected machine learning systems can automate the janitorial tasks of data science (e.g. fixing inconsistencies and eliminating fields in the raw data), which drives data science toward higher-level work (e.g. building and evaluating predictive models). Today’s most advanced systems can also eliminate workstream dependencies between Data Science and IT, by embedding data science techniques into the real-time transaction workflow.

Operationalizing the Machine

A machine learning system depends on the greatness of the team that builds it and implements it.  To keep pace with today’s fraud, data scientists must double as domain experts in fraud, so they can quickly attune the system to the unique fraud patterns in a given organization and industry, and deploy models that will be effective in mission-critical scenarios.

Better fraud detection happens as we break down more silos and connect more data into insights. Connecting this data happens at the technological level, with orchestration layers and capabilities that can build and propagate insights across more areas, more quickly. But connected intelligence happens at the human level too, as implementation teams gain better understandings of patterns and hotspots that spell risk, and transmit those insights to other product groups and use cases.

In seeking the best of both the machine and the human, organizations are finding that they have more control over their journeys than they realize. They’re adopting technological innovation that delivers business value, and they’re discovering that the intelligence disruption is something they can ease into.

Read our ebook for our insights about the key considerations to take in choosing a machine learning platform that augments your people and puts you in control of your implementation journey.

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