AI Driven Customer Engagement – Morphiq AI

Quality is all you need
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Transformers in Financial Recommenders. Quality is all you need

In 2017, a landmark paper titled “Attention is All You Need” was published by a team of Google researchers. This innovative work introduced the Transformer architecture which replaced recurrent neural networks (RNN) and convolutional neural networks (CNN) with attention mechanisms as the primary operation for processing sequences. This groundbreaking approach resulted in models that delivered superior results, achieved quicker processing times and required less data for training.

Why is attention transformative? The answer lies in its ability to focus on the most crucial aspects of the data, acknowledging the dependencies between all elements in the input and output sequences, irrespective of their positional distances which enhances the model’s predictive accuracy significantly.

For example, let’s consider a model for language translations. In order to translate a sentence from one language to another, the model must be able to understand the meaning of the sentence and identify the important words and phrases. The attention mechanism allows the model to focus on the most important parts of the sentence which helps it to make more accurate translations.

It is a remarkable innovation.

However, while the attention mechanism helps the model make well-informed decisions by accounting for all input elements’ relevance, the fundamental requirement for quality data remains unchanged across all models and algorithms.

High quality data facilitates the attention mechanism, enabling the model to focus more effectively on the data’s critical aspects. This is because consistent, accurate and high quality data streamline the learning of patterns for the model. In other words, the data’s quality directly influences the model’s ability to pay attention to it. Low quality data can obscure the important parts, thus leading to inaccurate predictions despite the power of the attention mechanism.

It’s like you’d try to to read a book that is full of typos and grammatical errors. It would be very difficult for you to pay attention to the text and understand what the book is saying. The same principle applies to Transformers. If the data is of poor quality, the model will not be able to pay attention to the data and make accurate predictions. You know, garbage in – garbage out.

Therefore, it is critical to ensure high data quality before training a machine learning model.

This can be achieved by thorough data cleaning (as in removing errors and inconsistencies), data validation, feature engineering and enriching it with relevant additional information.


#AlphaML #Transformers #spendingDNA #recommenders

– Alpha.ML is a model that prepares financial data to be effectively used in a Transformer-based Recommender System for banking and financial services allowing Financial Institutions to provide personalized recommendations and improve the customer experience, increase customer loyalty and reduce churn with a positive direct impact in CLV.