CyborgIntell’s Post

Automated machine learning is one of the new trends in data science. AutoML streamlines and automates the process of applying machine learning models. In this way, it becomes more available to non-experts and more efficient, leading to the democratization of data science. Essentially, AutoML is ML plus automation and application to real-life problems. With this data science trend, professionals whose primary expertise is not ML have access to ML. The development of ML-based apps heavily relies on automated machine learning.

Selling Models to Banks / NBFC , is one of the major responsibility of a data scientist in any credit bureau. Say, you are asked to sell an Application Scorecard model to a bank. In Most cases, bank is already using some other model . How do you convince that your model is better? The Answer is Swap In Swap Out Analysis. We perform this analysis to find out how many good customers our new model can swap in and how many bad customers it can swap out. This analysis will in turn generate the growth numbers, business can achieve by using the new model! Does your AI/ML course cover such real time topics? Learn industry practice of #datascience with us! Follow Scientist Express for daily content on Data Science / Credit Risk! #ai #ml #datascience #creditrisk

I am sure this is all too obvious to you all, data professionals, but for the sake of positive reinforecement. Dont let this opportunity get away. I just reposted an article that was shortly reasoning how difficult it is for us data professionals to be heard or funded for the "asset value" we can bring in. AI is everyones new pet, and we know that AI/ML is built on tech, good infra and architecture, genius mathematicians, coding AND DATA. So to all data professionals - now its time to seize the moment. 😁 We could even add a new word to our passion, data and "risk" management!

🌟 Project Completed: Bank Prediction Model 🌟 I am excited to share that I have successfully completed a project focused on building a bank prediction model! 🎉 This project involved developing a sophisticated machine learning model that predicts banking outcomes with high accuracy. Through this project, I gained valuable experience in data analysis, feature engineering, and model optimization. I would like to extend my gratitude to everyone who supported me throughout this journey. Your encouragement and guidance were invaluable. Looking forward to the next challenge! #MachineLearning #DataScience #BankPrediction #AI #ProjectCompletion

🚀 Excited to unveil my latest project: a loan approval prediction model leveraging machine learning! 💼💰 Using historical data from Kaggle, I meticulously preprocessed the dataset to handle missing values and outliers. Then, through extensive feature engineering, I crafted new indicators to enhance the model's predictive prowess. Next, I delved into model selection and tuning, experimenting with various algorithms and hyperparameters to pinpoint the most effective configuration. Performance evaluation was thorough, employing metrics like accuracy, precision, recall, and F1-score to ensure robustness. This project is poised to revolutionize the loan approval process, offering greater efficiency and reliability to both lenders and borrowers. By harnessing the power of machine learning, we aim to streamline decision-making and mitigate risks associated with lending. Excited to share this endeavor with you on GitHub: https://lnkd.in/gudiaZtS I'm eager to delve deeper into the project's intricacies and explore its real-world applications. Let's connect and collaborate to drive innovation in #MachineLearning, #DataScience, and #PredictiveAnalytics! 🤝💻 #AI #ML #DataDriven

Machine Learning Algorithms: Transforming Banking Operations 🏦 (Supervised) Supervised learning forms the backbone of many banking applications, leveraging labeled data to make accurate predictions and smarter decisions. Here are some of the most impactful algorithms used in the sector: 1️⃣ Logistic Regression 🔹 Use Case: Credit risk modeling, loan default prediction. 🔹 Why It’s Used: Simple, interpretable, and highly effective for binary classification tasks. 2️⃣ Decision Trees & Random Forests 🌳 🔹 Use Case: Fraud detection, credit scoring. 🔹 Why It’s Used: Handles non-linear relationships and provides feature importance insights. Random Forests enhance accuracy by averaging multiple decision trees. 3️⃣ Gradient Boosting Algorithms (XGBoost, LightGBM) ⚡ 🔹 Use Case: Risk scoring, customer retention modeling. 🔹 Why It’s Used: Known for exceptional performance with structured and imbalanced data. 4️⃣ Neural Networks 🧠 🔹 Use Case: Real-time fraud detection, advanced credit scoring. 🔹 Why It’s Used: Captures complex patterns in large, multi-dimensional datasets. 5️⃣ Support Vector Machines (SVM) 🔹 Use Case: Fraud detection, binary classifications. 🔹 Why It’s Used: Effective for high-dimensional datasets and noisy data environments. Impact of Supervised Learning in Banking ✔️ Smarter credit risk assessments. ✔️ Real-time fraud detection with greater accuracy. ✔️ Personalized financial solutions based on robust customer insights. 🔍 Which supervised algorithm has made the biggest impact in your banking projects? Let’s discuss! 📊 #MachineLearning | 🏦 #BankingAnalytics | 🌳 #RandomForest | 🚀 #XGBoost | 🔍 #FraudDetection | 💡 #DataScience | ⚡ #GradientBoosting | 🧠 #NeuralNetworks

In the ever-evolving financial landscape,   credit risk modeling is crucial for predicting   the likelihood of borrower defaults.      However, traditional methods face significant   challenges, including:    -Data quality issues   -Complex feature selection  -Non-linear relationships  -Model interpretability  -Imbalanced datasets  -Regulatory compliance    Machine Learning (ML) offers robust solutions to   these challenges:    Data Quality: ML algorithms handle large, noisy datasets   for accurate risk assessments.    Feature Selection: Automated feature selection in ML   identifies key variables, boosting model performance.    Non-Linearity & Interactions: Advanced ML models like neural   networks and random forests capture complex, non-linear   Relationships.    Regulatory Compliance: ML models can include fairness constraints   and regular audits to meet standards.    Why Choose ML for Credit Risk Modeling? Enhanced Predictive Power: ML models uncover subtle patterns in   vast datasets, leading to more accurate predictions.    Automation and Efficiency: ML automates feature selection and model   tuning, saving time and reducing human error.  Adaptability: ML models continuously update with new data, maintaining   their relevance and accuracy over time.    Continue reading: https://lnkd.in/gaBXggUA    Let’s build a more secure financial future together!  https://lnkd.in/gcHRD9H2    #MachineLearning #FinTech #FinancialServices #RiskManagement #innovation 

I’m excited to share a project I recently worked on where I built a credit risk model to predict loan defaults Here’s what I worked on: Balancing the dataset with SMOTE-Tomek, which helped me achieve 94% recall and a 0.71 F1-score for defaulters (class 1). Used VIF and IV analysis for feature engineering to uncover the most important predictors of credit risk. Built and optimized Logistic Regression models, applying WOE transformation to better handle categorical data. Evaluated the model with key metrics like AUC (0.98) and Gini coefficient (0.96), which showed a strong ability to separate high-risk and low-risk borrowers. Created a feature importance plot to visualize which factors had the most impact on predicting defaults. #MachineLearning #AI #DataScience #CreditRisk #FeatureEngineering #LogisticRegression #FinTech

Data leakage can sabotage even the most well-intentioned machine learning models, leading to inflated results and poor generalization. In my latest article, I cover seven common mistakes in data preprocessing, feature engineering, and train-test splitting that often lead to leakage—and how to avoid them. Check it out with the free link here if you are not a Medium member yet: https://lnkd.in/gUgwt3VQ. Thank Towards Data Science for posting another article from me!

Transformers are everywhere, but why do they require so much data to perform well? 🤖 It’s all about a crucial concept in data science: bias and variance. In Michael Zakhary's article, take a deep dive into how these two forces shape the effectiveness of transformer models like ChatGPT and BERT. #LLM #MachineLearning