Common Data Quality Challenges that can Impact AI and ML Models (Business Opportunities - Other Business Ads)

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Common Data Quality Challenges that can Impact AI and ML Models


Increasingly Financial Institutions (FIs)are focusing on data analytics as tool to develop a competitive edge in traditional market segments, as well as, in the under banked sectors of an economy. The challenge is to enhance customer experience, improve risk identification and assessment, and meet increasingly regulatory requirements.

To meet these challenges, FIs have been deploying state-of-the-art technologies to streamline current processes with the aim of improving efficiencies (thus reducing costs) and improve profitability, in an increasingly competitive marketplace. Concurrent to the effort, FIs are also developing and deploying initiatives to better serve small-to-medium size businesses as well as the under banked sector in pursuit of increasing market share and profitability. A key component is the deployment of big data that enables FIs to leverage the power of data analytics to improve customer experiences, manage risks, optimize operations, and make informed business decisions. It empowers FIs to unlock valuable insights from the vast amounts of data they generate and store, driving innovation and competitive advantage in the financial industry.

While much effort is being place in the development and deployment of AI and ML models, there is a need to balance this effort with common data quality challenges. Managing and ensuring the quality of diverse and large datasets can be complex and more effort (in addition to BCBS 239) is needed to mitigate risks associated with poor data. Needless to say, data quality is crucial for AI models as it directly impacts the accuracy, reliability, and generalizability of the models’ predictions and decisions.
#multi-currency online banking | Aerapass

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Last Update : 24 February 2024 9:26 PM
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Item  Owner  : Aerapass
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2024-06-02 (0.433 sec)