As investors look to leverage AI and machine learning in their analysis and decision-making, data quality is crucial. If the data going into AI models is biased, inconsistent, or incomplete, then the insights, predictions, and recommendations generated will be flawed. Two techniques that can improve data quality for investors employing AI solutions are re-weighting and targeted data augmentation.
Re-Weighting Data
Re-weighting involves adjusting the overall mix of data samples going into a machine learning model so that certain types of data are not over- or under-represented. For investment data, this could mean ensuring an appropriate balance between different companies, industries, geographies, market cap sizes, etc. For example, if 80% of the data a model trains on is from tech stocks, it may not perform as well when analyzing opportunities in other sectors. By re-weighting to have more balanced training data across domains, the model is exposed to more diversity upfront, allowing it to generalize better to new data. Common re-weighting methods include stratified sampling and effective sample size weighting.
Targeted Data Augmentation
Data augmentation artificially expands the amount of training data available to a model by generating simulated examples. This helps reduce overfitting and improves generalizability. For investing, targeted data augmentation generates artificial data similar to meaningful gaps or anomalies in the original data.
For instance, there may be very few historical examples of companies going through major lawsuits or leadership scandals. Augmenting the training data with simulated lawsuit/scandal examples can better prepare the AI to handle these cases when they arise with real companies in the future. The key is making sure augmented data maintains the integrity and statistical properties of real data. Advanced techniques like generative adversarial networks can assist with realistic data simulations.
Real-World Examples of Data Quality in Practice
To make data quality solutions more concrete, it helps to look at some real-world examples of re-weighting and targeted data augmentation applied to financial AI systems:
Reweighting Example: Algorithmic Trading Strategy: A hedge fund develops an AI stock trading strategy that combines value investing metrics with momentum indicators. But backtesting shows the model performs far better on small-cap stocks than large caps. Analysis reveals 80% of the training data was small-cap companies. By rebalancing the historical data used across market capitalization buckets, then retraining the model, it can trade effectively regardless of company size.
Targeted Augmentation Example: Credit Risk Model: A bank creates an AI application to assess consumer credit risk for its underwriting process. But the model is unsure how to handle cases of individuals going through major life events like divorce, losing a spouse, or graduating college—since these are underrepresented in the original training data. The bank generates additional synthetic training examples simulating credit reports before and after events like divorce. This improves performance when scoring real-life applicants tackling major financial changes.
In both examples, the root issue was incomplete, misrepresentative training data for the AI systems in question. Employing data re-weighting and augmentation specifically targeted problem areas. This delivered more robust models capable of handling real-world complexities on Wall Street and Main Street. The result is AI with improved integrity, flexibility, and business value. Adopting these data enhancement practices alongside algorithm development and infrastructure modernization allows financial institutions to deploy reliable machine learning products at scale. The solutions simultaneously deepen quantitative rigor while connecting AI innovations more tightly to qualitative real-world needs for both investors and consumers alike.
By proactively addressing data quality needs like balancing and completeness, techniques like re-weighting and targeted augmentation provide more reliable, unbiased, and accurate training data for investment-focused AI systems. This improves model performance and provides investors more consistent and generalizable insights over time. Considering data quality solutions alongside AI algorithm development is key for maintaining integrity and value as machine learning scales across the financial sector.
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