Blog Industry: BFSI
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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Reducing Customer Churn: How the Banking Sector Can Thrive
Address customer churn in banking through data-driven strategies with techniques to analyze customer behavior, predict churn risks, and implement retention programs. Strengthen customer loyalty and minimize attrition with future-proofed approaches.
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Building Data Engineering Solutions: A Step-by-Step Guide with AWS
In this article, delve into the intricacies of an AWS-based Analytics pipeline. Learn to apply this design thinking to tackle similar challenges you might encounter and in order to streamline data workflows.
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