DeepSee Recognized by Gartner in latest Report: 2023 Emerging Tech Impact Radar: Artificial Intelligence in Banking

DeepSee.ai, the creator and leading provider of Knowledge Process Automation (KPA), announced that it has been recognized as a Sample Vendor for Foundation Models in a recent report, 2023 Gartner Emerging Tech Impact Radar: Artificial Intelligence in Banking.

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“By harnessing the power of foundation models and wrapping them in an AI-enabled application built for business users rather than technologists, you empower department heads to solve their most pressing business problems and prioritize advancements to AI.”

The report states, “A total of 67% of the respondents from banking in the 2022 Gartner Data and Analytics for Digital Transformation Survey reported that they are currently using AI.” One of the key findings of the report was “Computer vision and intelligent applications are delivering return on investment in banking now, and they are expected to move to majority adoption across Tier 1 and Tier 2 entities within the next 12 months.” The report evaluates 10 emerging AI technologies that leverage AI techniques with significant near- and short-term impact to the banking and investment services industry.

“From our understanding of Gartner’s report, it emphasizes the importance of AI adoption in core banking operations and highlights the industry-wide opportunities to transform front to back-office operations in highly regulated environments. According to us, this report is tremendous validation that we’re building the right solution to meet the current and future demands of the financial services industry,” said DeepSee CEO, Steve Shillingford.

In scrutinizing ten technologies, Gartner noted, “Model-centric AI highlights the most promising emerging techniques, such as generative AI, foundation models and composite AI, which will support future revolutionary advancements to AI.”

The report listed the impact range for the market to be short, 1 to 3 years, and the mass, or size of impact, to be high. The report noted, “This profile’s impact mass is high because foundation models are driving a paradigm shift in natural language technology (NLT) by displacing recurrent neural network (RNN) systems at a surprising rate. Using pretrained models can save time and compute costs for training a model from scratch.”

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As a deep learning algorithm that has been pre-trained by billions of data points, foundation models create large-scale neural networks in which knowledge can easily be transferred. This innovation allows models to be finely tuned for specific tasks, rather than needing to be built from scratch for each use case, unlocking tremendous opportunity for organizations to solve highly complex problems quickly and cost-effectively.

“Foundation models allows us to add another layer of precision to solve complex business problems,” said Armen Sargsyan, Head of Strategy at DeepSee. “By harnessing the power of foundation models and wrapping them in an AI-enabled application built for business users rather than technologists, you empower department heads to solve their most pressing business problems and prioritize the work that matters most: reducing costs, mitigating risk, and increasing revenue.”

In one of its key findings, Gartner listed, “Core artificial intelligence (AI) technologies — such as foundation models and generative adversarial networks (GANs), are improving model accuracy, functionality and potential application for marketing and risk management use cases.”

“Facing the current pressures of unparalleled market volatility and ever-tightening regulations, financial institutions are focused on driving efficiencies and decreasing operational risk more than ever before. Solutions that leverage foundation models will enable them to do both,” Shillingford said.

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