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What are the most common data mining applications in finance?

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Data mining is the process of discovering patterns, trends, and insights from large and complex data sets. Data engineering is the discipline of building and maintaining the infrastructure, tools, and pipelines that enable data mining and analysis. In the finance sector, data mining and data engineering have many applications that can help improve decision making, risk management, fraud detection, customer segmentation, and more. In this article, we will explore some of the most common data mining applications in finance and how data engineering supports them.

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David Alexis García Espinosa

Sales | H2O.ai | Data Scientist | Biomedical Physicist

1 Credit scoring

Credit scoring is the method of evaluating the creditworthiness of a borrower based on their personal and financial data. Data mining techniques such as logistic regression, decision trees, neural networks, and ensemble methods can help create predictive models that assign scores to different borrowers based on their likelihood of defaulting or paying back their loans. Data engineering plays a vital role in collecting, cleaning, transforming, and integrating the data from various sources, such as credit bureaus, bank records, social media, and online transactions, that feed into the data mining models.

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2 Market analysis

Market analysis is the process of understanding the dynamics, trends, and opportunities in a specific market or industry. Data mining techniques such as clustering, association rules, time series analysis, and sentiment analysis can help identify patterns, correlations, and anomalies in the market data, such as prices, volumes, news, events, and customer feedback. Data engineering plays a crucial role in designing, building, and managing the data pipelines, databases, and dashboards that collect, store, process, and visualize the market data from various sources, such as stock exchanges, web scraping, APIs, and social media platforms.

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3 Portfolio optimization

Portfolio optimization is the process of selecting the best combination of assets that maximizes the expected return and minimizes the risk of a portfolio. Data mining techniques such as linear programming, quadratic programming, genetic algorithms, and Monte Carlo simulation can help solve the optimization problem and find the optimal weights for each asset in the portfolio. Data engineering plays a key role in providing, updating, and validating the data inputs, such as asset prices, returns, volatilities, correlations, and constraints, that are required for the data mining algorithms.

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4 Fraud detection

Fraud detection is the process of identifying and preventing fraudulent activities, such as identity theft, money laundering, credit card fraud, and insurance fraud. Data mining techniques such as anomaly detection, classification, rule-based systems, and machine learning can help detect and flag suspicious transactions, behaviors, and patterns that deviate from the normal or expected ones. Data engineering plays a significant role in ensuring the quality, security, and scalability of the data systems, platforms, and workflows that capture, store, analyze, and report the fraud data from various sources, such as transaction records, customer profiles, network logs, and external databases.

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David Alexis García Espinosa

Sales | H2O.ai | Data Scientist | Biomedical Physicist
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Data engineering is indispensable to solve fraud issues. Preprocessing your data involves identifying the specific type of fraud you aim to combat. Financial companies, ranging from fintechs to banks, process thousands to millions of transactions daily. Effective workflows demand high quality samples of data for training models (most of the time it isn’t easy to choose among millions of no fraud and few fraud cases). Additionally, there are numerous types of transactional fraud. Implementing an initial filter to analyze behaviors can help in determining which fraud type should be addressed first.

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5 Customer segmentation

Customer segmentation is the process of dividing customers into groups based on their characteristics, preferences, behaviors, and needs. Data mining techniques such as k-means clustering, hierarchical clustering, principal component analysis, and factor analysis can help create meaningful and actionable segments that can help tailor products, services, marketing campaigns, and pricing strategies to different customer groups. Data engineering plays an important role in enabling the access, integration, and transformation of the customer data from various sources, such as CRM systems, surveys, loyalty programs, and web analytics, that are used for the data mining models.

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MEE SUNTHORN

R at MOFRD Partner Ecosystem | Business Development | Emerging Technology | tiny.ee/View-And-Download-Now | Ready to Make an Impact in the Industry | t.ly/ZK0mO
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PyTorch is a Python-based library for deep learning that is commonly used in finance applications. It offers a number of advantages over other machine learning libraries, including its speed and flexibility. PyTorch also has a number of built-in features that are useful for data mining, such as data loaders and preprocessing tools. There are many examples and tutorials available for using PyTorch for data mining.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common data mining applications in finance?

1

2

3

4

5

6

1 Credit scoring

2 Market analysis

3 Portfolio optimization

4 Fraud detection

5 Customer segmentation

6 Here’s what else to consider

Data Engineering

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What are the most common data mining applications in finance?

1

2

3

4

5

6

1 Credit scoring

2 Market analysis

3 Portfolio optimization

4 Fraud detection

5 Customer segmentation

6 Here’s what else to consider

Data Engineering

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