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AI/Machine Learning: Predicting A Long Term Market Size Is Impossible

By Tim Sloane
September 20, 2017
in Analysts Coverage
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Robots Pandemic machine learning

If You Aren’t Thinking About Applying Robots During the Pandemic, Maybe You Should

AI in banking is evolving at a pace that is reshaping financial services far faster than traditional technology adoption cycles. Advances in machine learning are enabling financial institutions to automate complex processes, improve decision-making, enhance fraud detection, and deliver more personalized customer experiences. As new capabilities emerge, banks must continually evaluate how AI can create operational efficiencies while adapting to rapidly changing technologies.

Forecasting the long-term impact of AI has become increasingly difficult because the technology itself is advancing so quickly. Breakthroughs in machine learning, synthetic data generation, and model development continue to expand the range of potential applications, making it challenging to predict exactly how banking operations and customer behavior will evolve over the coming years.

Incremental thinking can’t possibly capture how broadly machine learning will impact every aspect of business, government, and consumer behavior. This in turn throws into question any effort “to provide reliable and authentic projections of the market” as promised by MarketResearchReports:

“Artificial intelligence (AI) is a technology that allows machines to take decisions based on data collected by them and pre-programmed tasks assigned to them. This technology is expected to transform all sectors of the industry as it reduces laborious work of employees and eliminates human errors. Applications of AI in banking is expected to facilitate various processes of organizations, which will result into enhanced user satisfaction. MarketResearchReports.biz has added a report “The Future of Artificial Intelligence in Banking,” which gives an in-depth analysis about this trend and effects of AI in banking in the future. This study is presented by highly qualified market research professional to provide reliable and authentic projections of the market. Data from both primary and secondary sources have been considered for formulation of this research report. Several aspects of the trend are included to offer 360 degree assessment of effects of this technology on banking sector. Numerous figures are added in the report to allow market players understand various applications of AI in banking.”

New machine learning capabilities will be brought to market faster and faster as the existing tools are applied to enhance existing platforms and create synthetic training data using adversarial networks (which pits one platform against another. This has created artificial languages and can be used to create a large set of training data that can’t be discerned from the original smaller training data set).

Even as the Mercator report “Bringing AI into the Enterprise—A Machine Learning-Primer” was being written, incredible breakthroughs were happening on a weekly basis! Mercator suggests that these machine learning platforms will become Observational Program Development platforms. By observing the events and actions associated with a process, these platforms will eventually be able to deliver greater insights regarding outcomes and will be able to replace the people and software systems that are executing the process today. It is difficult enough to determine what business processes and consumer behaviors machine learning will impact in the next 3 years, much less predict a market size.

The future of AI in banking will likely be defined less by individual technologies and more by the speed at which machine learning continues to evolve. As AI platforms become more capable of observing, learning, and optimizing business processes, financial institutions will gain new opportunities to improve efficiency, reduce costs, and enhance customer experiences.

Rather than relying solely on long-term forecasts, organizations should focus on building flexible AI strategies that can adapt as new capabilities emerge. Banks that continuously evaluate and integrate machine learning innovations will be better positioned to compete in an increasingly data-driven financial services landscape.

Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group

Read the full story here

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