Definition

A predictive AI model is a machine learning algorithm-based system that learns from historical data patterns to predict future outcomes or trends. It includes an adaptive prediction system that continuously improves performance through a feedback loop.

In a business context, a predictive AI model refers to the ability to analyze information from various data sources to predict future events, actions, or outcomes.

It is an AI-based analytics platform that processes and analyzes real-time data streams to provide future-predictive information for business decision-making, and has a system that models complex interactions between multiple variables.

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How it works

First, structured and unstructured data are collected from various sources and then converted into a form that can be learned through a preprocessing process. The preprocessing process refers to the process of removing noise or extracting features from large amounts of data.

Predictive AI can make more accurate predictions based on large amounts of data. The more data there is, the more reliable the analysis.

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The model then learns the time-series patterns of data and the correlations between variables through a deep neural network. It also uses a multi-layer perceptron (MLP) and a recurrent neural network (RNN) structure in this process.

Machine learning algorithms recognize patterns through data and predict the future based on them. For example, analyzing user behavior data can predict certain behaviors.

The automatic feature extraction mechanism, which is how the model recognizes patterns, uses convolutional neural networks (CNNs) and attention mechanisms to identify and assign weights to important features in the data.

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Now, based on the learned patterns, the model's prediction is generated through the following steps.

1. Vectorization of input data
2. Application of weight matrix
3. Processing of nonlinear activation function
4. Derivation of predicted value

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Once the prediction value is derived, the model is optimized using methods such as batch normalization, dropout layers, cross-validation, and hyperparameter tuning to improve the prediction accuracy.

After that, the prediction result is compared with the actual result to calculate the error and the weights are updated to maintain the prediction performance in a dynamic environment.

In addition, the model must be continuously updated to form a continuous feedback loop.

The most important aspect of predictive AI models is their explainability. Therefore, it is essential to recognize the uncertainty of the prediction and provide a confidence interval to assess the risk of decision-making.

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Advantages of predictive AI models and why they are important

Predictive AI helps companies make better strategic decisions by analyzing historical data to predict future behavior and outcomes.

For example, if you analyze and predict customer behavior to develop a customized marketing strategy, you can increase the likelihood of customer purchases and increase conversion rates, thereby promoting sales growth.

You can also optimize operational processes such as supply chain management to reduce costs and increase efficiency. In logistics operations, you can predict road congestion or a surge in demand and take appropriate action.

Or, in inventory management, you can predict consumer demand and maintain appropriate inventory levels.

As such, predictive model AI helps identify potential risk factors and manage them. In particular, it is used in the financial services sector in various ways to minimize corporate losses, such as assessing credit risk or detecting fraudulent activities.

It is also used to increase customer satisfaction by providing personalized user experiences based on customer data. If you can predict what I want and show me the answer I want, you can increase customer loyalty and build long-term relationships.

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Medical/healthcare sector - DeepMind, Stanford School of Medicine

The Alpha Fold 3 model is a protein structure prediction model developed by Google DeepMind. This model, which was selected by Nature as one of the innovative scientific technologies of 2023, has achieved an accuracy of 98% in predicting protein structures, contributing to a significant reduction in the time and cost of developing new drugs.

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The disease prediction system developed by Stanford University's School of Medicine boasts an accuracy of 95% in the early diagnosis of lung cancer based on medical imaging data. It is said that the survival rate has improved by more than 20% in the five years since the model was used. With the actual clinical application in 2024, future results are drawing attention.

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Manufacturing/Industrial Sector - Tesla, Samsung Electronics

Tesla's manufacturing facilities have a system that uses AI-based robotics solutions to detect defects and abnormalities during the manufacturing process. It boasts high precision and efficiency even for complex tasks such as assembly, welding, and painting.

In addition, it uses predictive AI models to predict demand and optimize raw material procurement to manage the risk of overproduction or stock shortages.

Samsung Electronics also succeeded in commercializing an AI model that predicts semiconductor yield. By introducing an AI-based defect prediction system, the production yield was improved by nearly 15%, which saved hundreds of millions of won in annual costs.

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Logistics/Distribution - Amazon, Walmart

Amazon has built a model that predicts changes in demand by combining a vast amount of data, including sales data, social media trends, economic indicators, and weather patterns.

For example, if a typhoon is forecast to hit a certain area, the company adjusts the inventory levels at nearby warehouses to ensure that essential items can be delivered quickly.

Based on this, the average delivery forecast time for Amazon has been continuously reduced from 2019 to 2023, and customer satisfaction has been quickly improved.

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Walmart also uses predictive AI models to improve and personalize the shopping experience. First, the checkout process was learned to predict the busiest times and determine the number of employees needed at the counter.

In addition, we were able to monitor the supply chain using predictive estimation to build efficient delivery routes.

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Financial sector - KB Kookmin Bank, NH Nonghyup Bank

KB AI Signal, an AI-based asset management service launched by KB Securities, uses an AI model to predict the risk of US stocks and predict market volatility. The model works through three stages: data processing, AI modeling, and signal calculation, and provides customers with customized asset management services.

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NH Nonghyup Bank has introduced an “AI financial product recommendation service” that applies real-time deep learning AI technology. Through this service, customers can receive information that takes into account their interests in tax savings, investment, and other areas, as well as predictive information on their actual interest rates and real estate holdings.

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Limitations and challenges

Predictive AI relies on large amounts of data. As explained earlier, having a large amount of data can improve accuracy. However, having a lot of data is not always good. It is more important to have high-quality training data.

However, in most industries, it is becoming increasingly difficult to secure data, along with the issue of personal information protection. It is important to note that if the data is of low quality or biased, the reliability of the prediction results may be compromised.

The most important aspect of predictive AI models is explainability. The ability to explain the AI's prediction results in a way that users can understand and trust is essential. In particular, the opaque decision-making process of AI is emerging as a critical issue in the medical and financial sectors.

In addition, ethical questions about whether AI decisions are fair and unbiased are emerging as major limitations. For example, bias contained in training data can affect the results of AI. This has the risk of deepening social inequality, so caution is required.

There are also indications that AI is still unable to solve complex situations involving judgment and creative tasks. Predictive AI has certainly made remarkable technological advances, but it still has the limitation of a limited range of applications.

Training and operating deep learning models requires a lot of computational resources. Building and operating large-scale AI systems is still difficult for the average company's budget and resources. That's why various AI SaaS solutions like Deepflow are emerging, but finding and using a good solution is not easy.

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Attempts to solve data dependency

Data augmentation is a method of generating new data by transforming existing data.

For example, in the case of image data, the data set can be expanded by transforming it, such as rotating, enlarging, or reducing it. This is helpful for companies with only small data sets to increase efficiency and reduce the need for large data sets.

In addition, transfer learning is a method of applying a model that has already been learned to a new task. This allows for high performance with a small amount of data and reduces data dependency by applying the knowledge gained from an existing model to a new problem.

Complex models require a lot of data. Therefore, reducing the complexity of the model can reduce the amount of data required. Using simpler algorithms or structures to perform predictions can achieve effective results with a small amount of data.

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Explainable AI

XAI (eXplainable AI) is a technology that explains and interprets the decision-making process and results of an artificial intelligence system in a way that humans can understand. It often refers to a methodological framework for overcoming the characteristics of AI that look like a 'black box'.

The purpose is to ensure reliability and enable the implementation of responsible AI by transparently disclosing the decision-making process of the AI system, and through this, users will be able to understand how the AI reached a certain conclusion.

For example, techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) can help users understand the impact of each input feature on the prediction result by visually representing it. This helps users better understand the data and trust the AI's decisions.

Continuously improving the model by receiving feedback from users is also a way to increase explainability. When users provide feedback on AI decisions, the model can be adjusted and improved based on this feedback.

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