Blog Industry: BFSI
Building Dynamic Data Pipelines with Snowpark: Our Framework to Drive Modern Data Transformation
Learn about the challenges of traditional data transformation methods and how a dynamic approach using metadata configuration can help address these issues. By defining transformation rules and specifications, enterprises can create flexible pipelines that adapt to their evolving data processing needs, ultimately accelerating the process of extracting insights from data.
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How to Simplify Data Profiling and Management with Snowpark and Streamlit
Learn why data quality is one of the most overlooked aspects of data management. While all models need good quality data to generate useful insights and patterns, data quality is especially important. In this blog, we explore how data profiling can help you understand your data quality. Discover how Tiger Analytics leverages Snowpark and Streamlit to simplify data profiling and management.
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What is Data Observability Used For?
Learn how Data Observability can enhance your business by detecting crucial data anomalies early. Explore its applications in improving data quality and model reliability, and discover Tiger Analytics’ solution. Understand why this technology is attracting major investments and how it can enhance your operational efficiency and reduce costs.
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Tiger’s Snowpark-Based Framework for Snowflake: Illuminating the Path to Efficient Data Ingestion
In the era of AI and machine learning, efficient data ingestion is crucial for organizations to harness the full potential of their data assets. Tiger’s Snowpark-based framework addresses the limitations of Snowflake’s native data ingestion methods, offering a highly customizable and metadata-driven approach that ensures data quality, observability, and seamless transformation.
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Migrating from Legacy Systems to Snowflake: Simplifying Excel Data Migration with Snowpark Python
Discover how Snowpark Python streamlines the process of migrating complex Excel data to Snowflake, eliminating the need for external ETL tools and ensuring data accuracy.
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Why India-Targeted AI Matters: Exploring Opportunities and Challenges
The scope for AI-focused innovation is tremendous, given India’s status as one of the fastest-growing economies with the second-largest population globally. Explore the challenges and opportunities for AI in India.
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Defining Financial Ethics: Transparency and Fairness in Financial Institutions’ use of AI and ML
While time, cost, and efficiency have seen drastic improvement thanks to AI/ML, concerns over transparency, accountability, and inclusivity prevail. This article provides important insight into how financial institutions can maintain a sense of clarity and inclusiveness.
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CECL in Loss Forecasting – Practical Approaches for Credit Cards
Discover how a combination of account-level forecasting, segmentation analysis, and rigorous model validation techniques can help credit card issuers address the unique challenges posed by CECL while reducing compliance costs and improving loss prediction accuracy.
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Credit Monitoring for SMEs: ML-Driven Early Warning Solutions
Explore how machine learning elevates credit monitoring for SMEs and corporations. Delve into the use of ML models for early warning solutions, enhancing risk assessment, default prediction, and financial stability in the banking sector.
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Building Pandemic Resilience: How Banks Leverage Advanced Analytics
Find out how banks are leveraging advanced analytics to build resilience during the pandemic, as well as the strategies they use to analyze data for intelligent decision-making, smart risk management, and elevated customer experience. Know all about the tools and technologies involved in driving this critical transformation.
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