Experimenting Azure Automated Machine Learning
SmartLake | The future of business with smart technologies
by smartlake
3y ago
Customer churn 2 Years ago I published a series of articles on LinkedIn related to customer churn ( this is part I https://www.linkedin.com/pulse/machine-learning-predict-customer-churn-accuracy-patrick-rotzetter/). I tested a few approaches and showed how to explain the model using Lime, how to measure feature importance, fight against class imbalance, and few other related topics. I recently performed a certification on Azure and wanted to test Azure Auto ML features on a simple example. So, I thought to use the previous examples and see how Azure AutoML simplifies the whole process. Azure M ..read more
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The AI book
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Thank you #AIBook authors for sharing your expertise: AI to Transform Push-Pull Wealth Management Offers @thepsironi The Rise of Conversational AI Platforms @anna_maria_maj Machine Learning in Digital Wealth Management @RotzetteP Find out more: https://t.co/u67sKywSI6 pic.twitter.com/opF93v24UH — The AI Book (@TheAIBook) May 28, 2020 The post The AI book appeared first on SmartLake ..read more
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Onboarding Virtual Assistant for Banking: Behind the Scene ( Part II )
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Building Blocks In the first part of the series we have shown how to define intents and capture dialog attributes using AWS Lex and AWS Lamda functions (https://smartlake.ch/onboarding-virtual-assistant-for-banking-behind-the-scene-part-i/). We will dive now a little bit deeper into the mechanism we have used to be able to recommend some products based on the user answers. There are multiple ways to implement product recommendations. I can refer to a previous post on that topic: https://smartlake.ch/personalizing-client-interaction-in-financial-services/. For this experiment we will use a simp ..read more
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Onboarding Virtual Assistant for Banking: Behind the Scene ( Part I )
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Building Blocks In a previous article,https://smartlake.ch/onboarding-virtual-assistant-for-banking-adding-product-recommendations/, we have shown the integration of product recommendation in a simple onboarding dialogue. In this article we are going to show how we built this simple experiment using various cloud based services. Amazon Lex Defining Intents and Utterances In order for the virtual assistant to interpret what a user wants to do, we must define user intents. One example of an intent is opening an account. Once we have created the intent, we need to define how the u ..read more
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Natural Language Processing: Smarter conversations using Spacy
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Introduction In the previous 6 articles we have illustrated the usage of Google and AWS NLP APIs. We have also experimented the spacy library to extract entities and nouns from different documents. We have shown how to improve the model using pattern matching function from spaCy ( https://spacy.io/ ) . Finally we have also trained the model with new entities. We have demonstrated how to match CV to job profiles. Let us now dig a bit deeper into some linguistic features of Spacy and how this can used in improving virtual conversations. The same can be used for mail processing, more advanc ..read more
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Natural Language Processing: Smarter conversations using Spacy
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Introduction In the previous 6 articles we have illustrated the usage of Google and AWS NLP APIs. We have also experimented the spacy library to extract entities and nouns from different documents. We have shown how to improve the model using pattern matching function from spaCy ( https://spacy.io/ ) . Finally we have also trained the model with new entities. We have demonstrated how to match CV to job profiles. Let us now dig a bit deeper into some linguistic features of Spacy and how this can used in improving virtual conversations. The same can be used for mail processing, more advanc ..read more
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Personalizing Client Interaction in Financial Services
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Background All financial services providers need more personalized offerings when interacting with clients and especially new clients. As a client I want to get some personalized recommendations on what to do next or which product to choose. Banks and other providers have had very limited personalization capabilities so far, this might be due to regulatory requirements or lack of historical data on past client behavior. This article reviews known recommendations techniques and their applicability for financial services Personalization approaches: Collaborative Filtering There ..read more
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Natural Language Processing: Resume Comparison Engine (Part 6)
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Introduction In the previous 5 articles we have illustrated the usage of Google and AWS NLP APIs. We have also experimented the spacy library to extract entities and nouns from different documents. We have shown how to improve the model using pattern matching function from spaCy. Finally we have also trained the model with new entities. Now we want to go into the full implementation of a comparison engine that goes beyond the simple keyword search and uses functionality of spacy. We have chosen to use personal profiles and job description, as this is a common use case and easily under ..read more
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Loan Scoring: Analyzing Data with R
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Objective Objective of the analysis is to find a potential way to detect or score loans pro actively using a machine learning model. The analysis done in R can be found here: https://rpubs.com/protzetter/557500 A small Shiny application can also be seen below where one can select different histograms of the data. Conclusion As we can see from the published analysis, we could not find any useful predictors which do not already indicate that the loan is deliquent. We need to find predictors that will allow us to predict before this occurs, The post Loan Scoring: Analyzing Da ..read more
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Personalization in Financial Services ( Part I)
SmartLake | The future of business with smart technologies
by smartlake
4y ago
Rationale We can see an increasing demand for personalized digital services in financial services. An article from BCG on the topic is helping in understanding the challenges and the opportunities of personalization in banking https://www.bcg.com/publications/2019/what-does-personalization-banking-really-mean.aspx. According to the study 37% of bank clients would like their bank to be more like Amazon. Clients are indeed open to get Amazon like recommendations from their bank. Additionally the pressure from big techs is putting financial services firms under pressure to deliver enh ..read more
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