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Wall Street Likens Automated Intelligence to Transformers, Both Good and Bad

The sci-fi fantasy franchiseTransformers, loved by millions of kids and immortalized on Hollywood’s silver screen, presents the classic contest of good versus bad.

Wall Street Likens Automated Intelligence to Transformers, Both Good and Bad

As the power- and resource-hungry Decepticons fight the human-friendly Autobots for global dominance, these bots present several parallels to the benefits and dangers of the latest cutting-edge business technologies we now see coming to life off-screen.

As we find new applications for Artificial Intelligence (AI) in the workplace, both positive and negative potential impacts are top-of-mind for executives, including those on Wall Street. In fact, at the SIFMA Operations Conference this year, 46 percent of financial services executives and regulators surveyed by Broadridge equated the future of AI to the Transformers movies, in that there will be a mix of good and bad uses of the technology.

The good news is that, unlike in Transformers, AI in the workplace will not jeopardize the fate of humankind. However, financial services firms need to lay the ground-work to ensure that the development and application of AI produces the best possible outcomes for their organizations. This starts with re-imagining the way we bring AI to life.

Re-Imagining Data Fabric& Network Value

 As AI becomes a mainstream business technology, gaining an understanding of machine learning (ML), deep learning (DL)and Robotic Process Automation (RPA) has become increasingly important across financial services. Not surprisingly, key to successful AI implementations rely heavily on the availability and quality of data, as well as the data fabric, from data aggregation to data normalization, extraction and analytics to support development.

In fact, for many of the firms surveyed, having access to quality data and ensuring that it’s standardized across an organization has become a tremendous challenge(53 percent). A perfect illustration of this challenge occurs when a financial trade fails and both parties involved need to reconcile data in the middle- and back-office, as well as between firms. Currently, the process is arduous and often leads to inaccurate outcomes, in large part because firms do not store and manage their data in the same way.

Not only are AI-driven projects inconsistent across the financial services industry, but also many businesses continue to struggle with driving enterprise-wide efficiencies. This makes demonstrating a true return on AI investment, justifying ongoing deployment and resourcing expenses, remarkably difficult. In fact, business justification/ROI and cost were each cited as inhibitors to implementation by approximately 40 percent of survey respondents—leading most firms (96 percent) to consider partnering with an industry leader to benefit from best practices and mutualize investments to maximize ROI.

Solving for these emerging challenges within a company’s own walls involves creating an enterprise-wide data fabric that has a consistent data ontology, normalizing and consolidating data across systems and silos and thereby reducing the complexity associated with the availability and standardization of data.

However, there is an even greater opportunity to apply AI and a consistent data fabric to automate processes and interactions across a network of firms and market participants. An increasing number of financial services firms are realizing the benefits of industry-wide data standardization and AI-co-development, creating efficiencies and synergies with a network of partners that no one firm could achieve alone. This desire for network value is evident –96 percent of firms see value partnering with other firms to build out their AI capabilities.

Re-Imagining AI Beyond the Big Screen

AI’s application off-screen is changing the rules of how companies operate, how they service customers and how they partner with each other. Fortunately, a significant portion of firms are already conducting assessments of or due diligence of AI/Machine Learning/Robotic Process Automation projects for their operations departments (19 percent), and many already have projects in the pilot or production stage (37 percent).

From chat bots that detect our emotions, to software that anticipates our needs, or even supply chains that can process information in real time, the future is here – right now – and there’s more to AI’s implementation than meets the eye. Firms can mutualize their innovation investment and unlock the true value of AIwith a strategic partner to leverage a consistent data fabric internally and across a network of industry participants. By working together, we can redefine Wall Street as we know it, and for the better.

Hear more from Mike Alexander in this recorded panel discussion about AI and other disruptive technologies from SIFMA Ops 2018.

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