Ep 51. Need help with Data implementing AI? Read this!

Heads up, Thought Leaders!

In this episode, we will address an important topic Thought Leaders face as they are implementing AI: Data. This is a follow-up of the previous Episode (Ep.50)

This episode is inspired by feedback from a Thought Leader involved in Retail /FinTech so I will offer examples related to that Industry Segment.

Let’s go!

If you struggle to implement AI in your business due to data issues, don’t feel alone, you’re the only one. In Ep.50, we commented that CIOs decide to implement one of 4 AI business models because they need to think strategically and decide based on the level of disruption their organization can absorb.

However, there are cases where the right decision is to build homemade solutions.

Eventually, issues like building and training AI models take risks into play, mainly when collecting and labeling data. Nonetheless, there are ways to overcome these challenges and create an effective AI solution.

In this article, we’ll explore some of the key issues businesses face when implementing AI and provide insights on how to address them. We’ll handle the following questions:

1. What are the challenges of building and training an AI model?
2. How can mature solutions like analytics be used to shortcut the process?
3. Why can starting with pilot projects reduce risk and improve the maturity of the AI team?
4. What are some excellent use cases for building and training quality data models and AI algorithms in the financial industry?

Challenges of Building and Training an AI Model

Building and training an AI model can be a complex and challenging process. One of the biggest challenges is collecting and labeling data. AI models are only as good as the data they’re trained on, so it’s essential to ensure that the data is accurate, relevant, and aligned with the business’s specific needs.

Another challenge is selecting a suitable learning model. If you want to know more about this topic, let me know, and we can address this in a future episode.

I got you, Jose. Next topic, how can analytics be a starting point for building AI solutions?

Using Analytics to Shortcut the Process

Analytics can deliver two benefits:

1. Helping Mature the AI Team
2. Helping shortcut the process of building and training an AI model

Think of this: Often, Fin Techs companies have mature Analytics solutions. These solutions can provide a solid foundation of data and insights that can be used to inform and train an AI model. By identifying patterns and relationships within a dataset, analytics solutions can help create labeled datasets to train the AI model.

Starting with Pilot Projects

Andrew Ng (an AI Thought Leader) is a big sponsor of this approach: building an AI team that matures by taking care of pilot projects. In many cases, this approach is an effective way to reduce risk and improve the maturity of the AI team.

By starting with a limited scope, the AI team can focus on a specific use case and develop the necessary skills and expertise before scaling to larger projects. Pilot projects also provide faster feedback loops and help ensure that the AI solution is aligned with the specific goals and needs of the business.

OK, Jose, I got it. One more point: Often, these options are unavailable. Any suggestions on some use cases with limited risk where Fin Techs may explore building homemade AI solutions?

Good Use Cases for Building and Training Quality Data Models and AI Algorithms in the Financial Industry

There are many good use cases for building and training quality data models and AI algorithms in the financial industry. One example is fraud detection. Fraud detection requires the analysis of large amounts of data to identify patterns and anomalies that could indicate fraudulent behavior. Starting with a pilot project focused on a specific type of fraud can help develop the necessary skills and expertise to build and train an effective fraud detection model.

Any final comments?

Just one

Implementing AI may seem daunting, but -with the right approach- it can be a valuable tool for businesses. By addressing data issues, using mature solutions like analytics, starting with pilot projects, and selecting the proper use cases, companies can build and train effective AI models aligned with their specific needs and goals.

I would love to know more if you need help implementing AI due to data issues. Feel free to DM me. Happy to assist.

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