Artificial intelligence (AI) is here to stay. As it gains popularity, more financial institutions are discovering how they can use AI to stay ahead, especially machine learning. Machine learning is indeed about teaching machines to make predictions or decisions based on past behaviors or data. It’s like training a dog — you show it pictures of different animals, label them as “cat” or “dog,” and over time, it learns to recognize them on its own.

However, when it comes to machines, the quality of data plays a pivotal role in the learning process. In this article, we’ll explore how your data is preventing you from using AI, what that means for your bank and how you can fix it.

The first step in the process is to develop a data strategy. Unfortunately, this is not a simple or quick process. It involves understanding the business case for the data and determining what data needs to be stored in which structures. To truly gain the benefit of AI or machine learning, a standalone data warehouse is often required. Again, no small undertaking but invaluable once achieved.

Once the strategy and structure are determined, the next steps of getting and keeping the data healthy can begin. This means getting the data healthy at the data source. If you have to “clean” the data after it is integrated into a data warehouse, then it will be a constant battle to keep it healthy.

Garbage In, Garbage Out: The Data Dilemma

The saying “garbage in, garbage out” holds true in the world of machine learning. In essence, if you feed a machine poor-quality or inaccurate data, it will produce unreliable results. Here’s why data quality is critical:

• Incomplete Information: Missing or incomplete data can lead to inaccurate predictions. Imagine a machine learning model trying to forecast stock prices with gaps in historical price data. It’s likely to make flawed predictions.
• Bias Amplification: If your data is biased, the machine will learn those biases. For instance, if historical hiring data is skewed towards a particular gender or ethnicity, the machine may unintentionally perpetuate these biases when making future hiring decisions.
• Noise and Outliers: Data that contains excessive noise (random variations) or outliers (extreme data points) can confuse the learning process. Machines might focus too much on these anomalies and struggle to find meaningful patterns.
• Data Imbalance: When one class of data significantly outweighs the other, as in fraud detection where legitimate transactions far outnumber fraudulent ones, the model may become biased towards the majority class, missing out on detecting important minority cases.

The Cost of Bad Data

The consequences of poor-quality data can be staggering:

• Lost Opportunities: Banks may miss valuable insights, opportunities or potential cost savings due to inaccurate predictions.
• Customer Dissatisfaction: In sectors like e-commerce or personalized recommendations, bad data can result in poor user experiences, potentially driving customers away.
• Reputation Damage: In some cases, relying on bad data can lead to public relations nightmares. For instance, if a banking AI system denies loan approvals due to historically biased training data, it could harm both customers and the bank’s reputation.
• Financial Loss: Banks investing in machine learning without ensuring data quality may end up wasting resources on models that fail to deliver.

Getting It Right

So, how can banks avoid falling into the trap of “garbage in, garbage out” when it comes to machine learning? Below are six ideas to consider before using your data in machine learning.

1. Data Collection and Preprocessing: Start by establishing standards for collecting high-quality, diverse and representative data. Then, preprocess it to remove noise, handle missing values and balance classes with an equal number of samples if needed.
2. Continuous Monitoring: Data quality is not a one-time task. It should be an ongoing process. Implement data monitoring systems to identify issues as they arise.
3. Diverse Data Sources: Consider using data from various sources to reduce biases and improve generalization.
4. Feature Engineering: In other words, create new features or upgrade existing ones from your data. Sometimes, the right feature engineering can compensate for a lack of data.
5. Model Evaluation: Continuously evaluate your models’ performances. If they’re not meeting the desired accuracy or reliability, it may be time to revisit the data quality.
6. Ethical Considerations: Be aware of the ethical implications of your data. Ensure that you’re not inadvertently perpetuating discrimination or bias.

Conclusion

In a world where data is the new gold, the old adage “garbage in, garbage out” is more relevant than ever. Machine learning can revolutionize banks, but only when fueled by high-quality data. Financial institutions that recognize this and invest in data quality are more likely to succeed in the new frontier of machine learning. Remember, it’s not just about the algorithms; it’s about the data that feeds them. So, ensure your data is top-notch, and you’ll be on the path to harnessing the true power of machine learning.

When was the last time you checked your data? If you’re ready to upgrade your data and unlock the potential of machine learning in your business, visit www.jmark.com or call 844-44-JMARK to schedule a network evaluation today.