According to The Financial Brand, banks leveraging AI technologies are expected to save a staggering $1 trillion by 2030. Yet, tapping into the true potential of this technology hinges on one critical factor: data preparation.

AI models, no matter how advanced, are only as good as the data you feed them, and their predictions will be faulty if the training datasets are not structured, formatted, labeled, and cleaned well enough.

This article explores how financial institutions can effectively prepare their data for impactful AI implementation.

Data structure

This is the foundation of any successful AI project. Financial institutions often have information coming from a multitude of sources, each with its own format and organization. This inconsistency can be a major roadblock for the algorithms. Here's how to ensure your data is ready for AI:

• Standardization. Unify data formats across all sources. This includes things like currency codes, date formats, and other data types. Consistency ensures the AI model understands the information it's receiving.
• Data hierarchy. Establish a clear hierarchy for your data. This means organizing it in a way that reflects the relationships between different data points.

Data accuracy

Inaccurate data leads to unreliable models and potentially misleading results. To ensure their data is trustworthy, companies can implement several measures.

• Data cleaning. Financial data is susceptible to errors like typos, outliers (extreme values), and missing entries. Using data cleaning techniques helps identify and eliminate any mistakes or inconsistencies. This might involve setting rules to detect suspicious values, correcting typos, and finding ways to impute missing data points responsibly.
• Data validation. Besides cleaning, companies can put data validation rules in place to catch errors at the source. These rules can be as simple as checking if transaction amounts exceed account balances, or verifying customer information against external databases.

Data uniqueness

Duplicate data records can significantly hinder an AI system’s ability to accurately analyze customer behavior. If your datasets contain multiple identical records for the same individuals, the model's outputs can become skewed, leading to inaccurate insights. To combat this, companies need to perform deduplication by identifying and removing these unnecessary records. This process can involve eliminating redundant customer information, repeated transactions, or any other recurring data points. Deduplication techniques often use matching algorithms to compare data across various fields and delete redundancies.

Feature engineering

While data quality and structure are crucial, it's equally important to extract maximum value from the information at hand. This is where feature engineering comes into play. It involves creating new features from existing data points to provide a richer context for AI models.

For example, when an AI system analyzes customer loan applications, raw data like income and credit score can be useful. However, combining these with features derived from transaction history (e.g., spending patterns) and demographics (e.g., age, location) can offer a more comprehensive view. This enriched data greatly enhances the AI algorithm's predictive capabilities.

Addressing data scarcity

Data scarcity is when the available data volume is insufficient to train an algorithm. Fortunately, there are several techniques that can help financial institutions overcome this obstacle.

Data augmentation. This approach involves manipulating existing data to create new variations. For example, in image data (like financial documents), geometric transformations like rotations or flips can be applied. For financial time series data (like historical stock prices), adding controlled noise can create variations that represent real-world fluctuations. By expanding the training dataset with these variations, data augmentation helps AI models generalize better and perform more effectively with limited real data.

Synthetic data generation. This process involves creating artificial data that replicates the statistical properties of real data, but protects privacy by not containing actual customer information. Examples include generating synthetic transaction data with realistic patterns for fraud detection models, or anonymized customer profiles for credit risk assessment.

Data annotation and labeling

Supervised learning remains a cornerstone for achieving high accuracy in many financial tasks, such as credit risk assessment and fraud detection. This necessitates proper data labeling, as inaccurate labels can lead to poorly performing or biased models. Financial institutions can leverage various approaches to ensure high-quality labeled data, including building in-house labeling teams, collaborating with AI-specializing software development partners, or utilizing third-party data annotation services.

Data anonymization and privacy

Financial data is often subject to strict privacy regulations. To ensure compliance and ethical use, anonymize sensitive data before feeding it to AI models. This can involve techniques like tokenization (replacing sensitive data with random tokens) or differential privacy (adding noise to data while preserving statistical properties).

Summing up

By addressing the aforementioned data preparation considerations, financial organizations can lay a strong foundation for successful AI implementation. This, in turn, can lead to significant benefits like improved fraud detection, risk management, and the development of personalized financial products.

If you’re looking to maximize the value of your AI algorithms, contact us today for a free consultation.