Unlocking the Value of AI

Unlocking the Value of AI in our Recent Webinar

Today, there’s a lot of buzz around artificial intelligence (AI). But you can’t really have a conversation about artificial intelligence without talking about data. And that’s why we made both the focus of our recent webinar.

Yumiko Kato, Head of Product - Connectivity at Envestnet Data & Analytics, kicked off the webinar by highlighting how training data is often referred to as a force multiplier for artificial intelligence, because it significantly impacts the performance and capabilities of AI models. Kato emphasized that AI models need vast amounts of training data to learn patterns, make predictions, and perform tasks. But data needs to be diverse, high quality, and connected in order to achieve the best results.

Here’s why:

Enhancing generalization

Generalization refers to how well an AI model handles new and unseen data. This is where the intelligent piece of artificial intelligence comes in. By exposing models to a wide range of data during training, they become more robust and adapt, and better equipped to handle variations, outliers, and complex scenarios that they haven’t encountered before. This is especially important for investment data, which incorporates many different kinds of transactions, accounts, and data patterns tied to helping consumers manage their financial well-being.

Reducing bias 

Quality, diverse training data is key to reducing and mitigating bias. By representing the real-world population and including various demographics, training data can help reduce outcome biases and promote fairness in AI applications. This is crucial when it comes to financial data that is meant to represent all parts of the population and financial universe.

Increasing knowledge and know-how 

By training AI models on different types of data, AI can acquire new capabilities and excel at a wider range of tasks. The right training data enables AI models to gain the knowledge necessary to perform specific tasks effectively.

Scaling AI models

High-quality training data is needed to scale AI models and deploy them in real-world applications. Training data helps to create robust and scalable AI models that can handle large amounts of data efficiently. It’s also necessary for fine-tuning and adapting models to specific contexts or domains and making them more effective in real world scenarios.

Boosting AI’s performance with Envestnet Data & Analytics 

As Kato pointed out, a lot of the things that people are talking about with machine learning, generalization, and data inclusivity are things that Envestnet Data & Analytics is already doing, as we’ve been helping consumers collect data about their financial accounts, transactions, investments and holdings, and enabling them to successfully manage their financial health for decades.

Some of the ways we prepare data for AI models and insights are:

Normalization: While financial data is usually somewhat standardized, there are always variations depending on the source. Normalization, which means transforming and standardizing data to a consistent format, enables analysis and integration of data from different systems and presents a more useful overall picture. It also ensures that data is clear, because discrepancies in the data are rectified.

Data enrichment: Data enrichment involves enhancing existing data by adding additional information or attributes. At Envestnet D&A, we enrich original datasets with external data sources such as financial market data, economic indicators, demographic information, social media feeds, investments, holding information and more, to support deeper analysis and broader insights. 

Data validation: The data enrichment process includes cross references with trusted external sources as well as internal validation rules. This enhances the data's value for analysis and decision making.

Data aggregation: Leveraging data from multiple sources such as financial institutions, market data providers, custodians, and other data vendors enables a deeper and broader view of an individual's financial wellness. This aggregated data can be used to generate meaningful insights, identify trends, perform comparative analysis, provide personalized advice, make strategic decisions, and enhance the customer experience.

Interested in learning more about connected data and how it powers AI innovation? Watch the full webinar on demand.

Want to talk about how you can put AI and AI-ready data to work in your organization? Contact us!