Common Data Quality Challenges that can Impact AI and ML Models
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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.financial services institute | Aerapass 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. The following are selected data issues and their impact of AI models: If the training data set used to build the AI model is biased or incomplete, the predictions will likely reflect those biases or gaps. The model might make inaccurate predictions, leading to biased outcomes. Poor data quality may contain outliers, inconsistencies, or noise that can mislead the AI model during training. These anomalies can skew the model’s understanding of patterns and relationships, resulting in inaccurate predictions. If the data used to train the model is missing important information or contains errors, the model may not learn the complete picture. This can lead to incomplete or incorrect predictions when encountering similar patterns in real-world data. Data that does not adequately represent the target population can lead to biased predictions. AI models trained on such data may fail to capture the nuances and characteristics of different groups, resulting in inaccurate predictions for specific subgroups. Poor data quality may inadvertently introduce data leakage or overfitting issues during model training. Data leakage occurs when information from the future or the target variable leaks into the training data, leading to overly optimistic predictions. Overfitting happens when the model becomes too specific to the training data and fails to generalize well to new, unseen data. The following are some examples of how technology can assist with data quality: Automated tools and algorithms can assist in data cleaning and preprocessing tasks. These tools can identify and handle missing values, outliers, duplicates, and inconsistencies in the data. They can also standardize data formats, correct errors, and reconcile conflicting data entries. By automating these processes, technology helps ensure cleaner and more reliable data. Technology can be used to implement data validation rules and quality checks. These checks can be built into data entry systems or data pipelines to flag potential data quality issues in real-time. For example, validation rules can be set to verify data formats, ranges, or logical relationships. Automated data quality checks help detect errors or anomalies early on, allowing for timely remediation. Technology solutions, such as data integration platforms and master data management systems, help consolidate structured and unstructured data from multiple sources and ensure consistency across datasets. These tools provide mechanisms to map, transform, and merge data from disparate systems, reducing data silos and improving data quality through standardized and unified data sets. Technology can also facilitate data governance practices by providing tools for managing metadata, data dictionaries, and data lineage. These tools help establish data quality standards, define data ownership, and enforce data governance policies. They enable organizations to track and manage data quality metrics, audit data usage, and establish data stewardship roles and responsibilities. Machine learning and AI algorithms can be used to identify patterns and anomalies in data, which can assist in detecting and addressing data quality issues. For example, anomaly detection algorithms can flag unusual data points, data profiling techniques can identify data inconsistencies, and data imputation methods can fill in missing values based on patterns in the data. While technology can significantly contribute to improving data quality, it is important to note that it is not a complete solution. Data quality also requires human involvement, domain expertise, and a thorough understanding of the specific context in which the data is being used. Therefore, a combination of technology, proper data management processes, and human oversight is crucial to effectively address data quality issues.