Words Manish Kumar
NEW DELHI (India CSR): Deep learning models are transforming traditional risk assessment frameworks, which have long relied on statistical methods and expert systems. Innovations in Deep Learning, like Transformer architectures and Large Language Models (LLMs), provide unmatched accuracy and insights, redefining how financial institutions assess credit risk.
This article highlights insights from Abhishek Shivanna, a founding engineer at Hyperplane AI, a company leading the way in financial risk modeling with advanced techniques. Abhishek was instrumental in developing Hyperplane AI’s cutting-edge SaaS platform, showcasing deep learning models’ transformative potential in risk assessment. “The Hyperplane AI platform redefines how financial institutions approach risk,” says Abhishek. “By integrating
state-of-the-art deep learning techniques, we’ve empowered our clients to analyze datasets they had never considered before, uncovering insights that traditional models couldn’t provide.”
The platform’s impact extends beyond risk management. “Financial institutions can personalize customer experiences by leveraging our insights, offering tailored products and services that boost satisfaction and loyalty,” he adds.
Financial risk assessment has relied on structured data like credit scores and transaction histories, analyzed using traditional statistical models for decades. While effective, these methods often fail to account for the complexities of user behavior and overlook the wealth of unstructured data generated by users.
Abhishek explains, “Our models generate embeddings by processing large, unstructured datasets, such as user event logs, transaction descriptions, and documents. These embeddings transform complex, high-dimensional user data into simplified, low-dimensional representations. This process enables our models to uncover deeper patterns and relationships that traditional methods can’t capture.”
He provided an example: “Unstructured data like a financial transactions stream for a user are captured into numerical embeddings using our pre-trained language models. These embeddings capture the semantic meaning of all the user’s transactions, allowing our risk assessment models to deliver significantly more accurate and nuanced predictions.”
Through this innovative approach, Hyperplane AI is setting a new standard in financial risk assessment by unlocking the potential of unstructured data to make better-informed decisions.
Migrating to embeddings in financial risk modeling introduces challenges such as handling data quality, ensuring relevance, and managing computational complexity. Abhishek adds, “Unstructured or fragmented financial datasets often require robust data processing and training/inference pipelines to produce meaningful embeddings, while irrelevant or biased data can distort model outputs. Pre-trained embeddings, though powerful, may lack company-specific nuances, necessitating fine-tuning. Additionally, embedding-based models can be computationally intensive, requiring significant infrastructure upgrades to handlelarge-scale data efficiently. This is where the systems we built at Hyperplane AI come into play,” says Abhishek. “Hyperplane’s state-of-the-art platform simplifies this transition for customers by providing end-to-end solutions for modeling.” The platform integrates data processing pipelines, fine-tunes embeddings for financial contexts, and optimizes infrastructure for scalable performance. Doing so empowers financial institutions to unlock the full potential of embeddings without building these capabilities in-house, allowing them to focus on insights and decision-making.
As financial systems grow more complex, deep learning’s role in mitigating and predicting risks will become increasingly critical. Through his work at Hyperplane AI, Abhishek showcases the transformative potential of deep learning models in financial risk assessment. He and his team have set new industry benchmarks by combining technological innovation with real-world applications, demonstrating that AI can create more intelligent and efficient financial systems. The future of financial risk lies in blending rich data, technological advancements, and organizational agility. Abhishek’s insights, based on his work at Hyperplane AI, highlight AI’s transformative potential and underline its critical role in managing risks.
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Manish Kumar is a news editor at India CSR.
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