Banking Data Science — Skills & Portfolio Guide

Banking Data Science — Skills & Portfolio Guide

Banking Data Science — Skills & Portfolio Guide

Practical skills, application areas, and project ideas to build a strong banking-focused data science portfolio.

1. Core Application Areas

Where data science is most impactful in banking.

Domain Area Typical Use Case Example
Credit Risk Modeling Predict probability of default Loan delinquency forecasting
Fraud Detection Identify suspicious transactions Real-time card fraud alerts
Customer Segmentation & Personalization Tailor offers and experiences Next-best-action recommendations
Anti-Money Laundering (AML) Detect illicit fund flows Transaction network anomaly detection
Loan Pricing & Profitability Optimize interest rates Risk-based pricing engines
Market & Liquidity Risk Forecast balance sheet stress VaR and stress testing simulations
Chatbots & Process Automation (NLP/LLM) Automate customer service Intelligent query resolution and document extraction

2. Key Data Science Skills

Technical skills to develop and highlight in your portfolio.

Data & Modeling

  • Data Engineering: SQL, Spark, ETL pipelines, feature stores
  • Statistical Modeling: Logistic regression, survival analysis, GLMs
  • Machine Learning: XGBoost, CatBoost, ensemble methods
  • Deep Learning: RNNs/LSTMs (sequences), GNNs (transaction graphs)
  • Time Series: ARIMA, Prophet, Transformer-based forecasting

Advanced & Governance

  • NLP / LLMs: contract analysis, sentiment from call transcripts, summarization
  • Graph Analytics: AML network detection, recommendation graphs
  • Causal Inference: marketing campaign uplift, interest rate impact
  • Explainability & Fairness: SHAP, Partial Dependence, counterfactual fairness
  • Visualization: Tableau, Power BI, Streamlit dashboards

3. Important Domain Knowledge

Banking concepts to learn and reference in projects.

  • Credit lifecycle: origination → underwriting → servicing → collections
  • Basel III/IV: capital adequacy, risk-weighted assets (RWA)
  • Net Interest Margin (NIM) & ROE: core profitability KPIs
  • Stress testing & IFRS 9: expected credit loss (ECL) modeling
  • Regulatory compliance: KYC, PSD2, GDPR, CCAR
  • Transactional data streams: high-velocity card/SWIFTs data

4. Project Ideas for Your Portfolio

Concrete, portfolio-ready projects that demonstrate both technical skill and domain knowledge.

Project Description Techniques
Credit Default Prediction Forecast loan repayment risk Classification, survival analysis, SHAP
Real-Time Transaction Fraud Flag suspicious card activity Anomaly detection, streaming ML, GNNs
Customer Churn & LTV Predict attrition and lifetime value XGBoost, Buy-Til-You-Die models
Next-Best-Action Engine Recommend products per customer Reinforcement learning, multi-armed bandits
AML Transaction Monitoring Uncover money-laundering rings Graph analytics, community detection
Interest Rate Risk Simulation Model portfolio sensitivity Monte Carlo, Vasicek/Hull-White models

Comments

Post a Comment

Popular posts from this blog

Automate Blog Content Creation with n8n and Grok 3 API

Automate Blog Content Creation with n8n and Grok 3 API Introduction n8n is an open-source automation tool that connects apps to streamline tasks. This tutorial guides you through creating a workflow to automate blog post creation for Google Blogger using the Grok 3 API for content generation, Pexels for images, and Google Sheets for storage. The workflow ensures niche-relevant content and uses n8n’s free tier. Prerequisites n8n account (free at n8n.io ) Grok API key ( x.ai/api ) Pexels API key ( pexels.com/api ) Google account for Sheets and Blogger Google Sheet with columns: Title, Content, Meta Description, Image URL, Status Workflow Overview The workflow: Schedules automatic runs Sets niche keywords Generates a blog post topic Drafts a detailed post Fetches a relevant image Saves data to Google Sheets Handles e...
Read more

Neuro-Symbolic Integration: Enhancing LLMs with Knowledge Graphs

Image
Neuro-Symbolic Integration: Enhancing LLMs with Knowledge Graphs Abstract Large Language Models (LLMs) have revolutionized natural language processing, achieving remarkable success in tasks like text generation and question answering. However, their reasoning capabilities are constrained by hallucinations—generating plausible but factually incorrect outputs—and limited parametric memory, which hampers their ability to maintain context over long interactions or perform complex multi-step reasoning. This article synthesizes insights from 2024-2025 surveys on neuro-symbolic artificial intelligence, focusing on integrating LLMs with Knowledge Graphs (KGs) to enhance factual grounding, reasoning, and knowledge management in real-world applications. We explore methodologies for knowledge extraction, representation, reasoning, and dynamic updating, emphasizing bidirectional synergies where LLMs automate KG construction and KGs improve LLM reasoni...
Read more

Understanding and Using the Generalized Pareto Distribution (GPD)

Understanding and Using the Generalized Pareto Distribution (GPD) The Generalized Pareto Distribution (GPD) is a probability distribution used in Extreme Value Theory to model values that exceed a certain high threshold. It is widely used in finance, insurance, hydrology, and environmental science. 📘 What is the GPD? The GPD models the distribution of excess values over a threshold. That is, if we set a threshold u , the GPD models the distribution of X − u | X > u . 🔣 Probability Density Function (PDF) f(x) = (1 / σ) * (1 + ξ * x / σ)^(-1/ξ - 1) ξ : Shape parameter (controls the heaviness of the tail) σ : Scale parameter (spread) Support: x ≥ 0 if ξ ≥ 0 ; 0 ≤ x ≤ -σ/ξ if ξ < 0 🛠️ Fitting GPD to Synthetic Insurance Claims (Python Example) Let’s simulate a small dataset of insurance claims, set a threshold, and fit a Generalized Pareto Distribution using scipy . 🔢 Step 1: Simulate Data import numpy as np import matplotlib.py...
Read more