Answers.Body: <p>Since handling post-trade compliance in financial applications involves monitoring and analyzing large volumes of transaction data to ensure adherence to regulatory requirements, this task often requires sophisticated and robust ML models capable of detecting rare and various complex anomaly patterns, like the ML use cases of credit card fraud detection. Bear in mind training data for these types of problems are often technically challenging due to several special issues such as class imbalanced data which often needs special calibration or loss reweighting schemes for real world production deployment as discussed in a recent <a href="https://ai.stackexchange.com/questions/46373/dealing-with-likely-class-imbalance-for-unlabeled-test-data/">post</a>.</p>
  <p>As a start you could first read the brief summary paper of Tiwari et al. (2021) <a href="https://arxiv.org/abs/2108.10005v1" rel="nofollow noreferrer"><em>&quot;Credit Card Fraud Detection using Machine Learning: A Study&quot;</em></a> for some general but important design ideas of your own ML pipelines and models since they're very similar use cases of ML.</p>
  <blockquote>
  <p>In this paper, we present a comprehensive review of various methods used to detect credit card frauds. These methodologies include HMM, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network... The study shows that the Fraud Bayesian Network Classifier followed by probability threshold is more beneficial than
  Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machines and Decision Trees on the PagSeguro dataset when precision, recall and economic efficiency are taken into account. Bayesian Learning when taken with Dempster-Shafer Theory resulted in 98% True Positives and less than 10% False Positives... When dataset is highly imbalanced and independent of the rate of frauds Bagging Ensemble Classifier is one of the most stable approaches and has a high fraud catching rate... A lot of financial resources were saved when Cost-Sensitive Decision Tree approach was used.</p>
  </blockquote>
  <p>In summary it's very hard to choose <em>the</em> best ML model for such use cases as each instance may have its own characteristic. For example, for a person with a long enough personal transaction history HMM is well suited for anomaly detection, but some people may just start their transactions without any history. You must consider your use case's own characteristic to choose your model in addition to some common knowledge similar to your problem.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-08-08T01:50:02.303Z
Answers.LastActivityDate: 2024-08-08T01:50:02.303Z
Answers.UserDisplayName: cinch
Body: <h5>My Background</h5>
  <p>Machine learning and software engineering</p>
  <h5>Got an Offer for</h5>
  <p>Data and AI engineer in a bitcoin market making firm/exchange.</p>
  <h5>Context</h5>
  <p>I already talked to one software engineer in the team, he said the team's major focus is on <em>post trade compliance</em>. I am trying to do more research on this topic, but some pointers on what exact problems constitutes the intersection  between big data, ML and Bitcoin market making will be sincerely appreciated.</p>
  <p>It is a new team, and I want to arrive with at least some vague ideas on what kind of problems will be feasible and beneficial to solve, using big data and ML in a bitcoin market making context, assuming we have past tick data and transaction data available. Some ideas I have are basically user clustering (risk hungry, or low risk), or somehow helping institutional traders (who trade using our exchange) on volatility or sentiment etc. But still too vague, so any pointer, blog, tutorial will be helpful.</p>
  <p>The answer, of course, need not be exclusively focussed post trade context, any problem ideas or typical data engineering challenges faced by bitcoin market makers will be helpful to understand.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-08-08T08:01:33.553Z
Comments.Text: ML models and pipeline designs are highly context specific, thus it's better for one question to focus on one use case. If you have other concerned cases in mind, you'd better ask a new question per SE site rules.
Comments.UserDisplayName: cinch
Tags: <machine-learning><data-preprocessing>
Title: What Are the Use Cases for Machine Learning or even NLP for Bitcoin Market Making?
UserDisplayName: Della
ThreadText: # What Are the Use Cases for Machine Learning or even NLP for Bitcoin Market Making?
  
  (Asked by: username_0 on 2024-08-07)
  
  ##### My Background
  
  Machine learning and software engineering
  
  ##### Got an Offer for
  
  Data and AI engineer in a bitcoin market making firm/exchange.
  
  ##### Context
  
  I already talked to one software engineer in the team, he said the team's major focus is on *post trade compliance*. I am trying to do more research on this topic, but some pointers on what exact problems constitutes the intersection between big data, ML and Bitcoin market making will be sincerely appreciated.
  
  It is a new team, and I want to arrive with at least some vague ideas on what kind of problems will be feasible and beneficial to solve, using big data and ML in a bitcoin market making context, assuming we have past tick data and transaction data available. Some ideas I have are basically user clustering (risk hungry, or low risk), or somehow helping institutional traders (who trade using our exchange) on volatility or sentiment etc. But still too vague, so any pointer, blog, tutorial will be helpful.
  
  The answer, of course, need not be exclusively focussed post trade context, any problem ideas or typical data engineering challenges faced by bitcoin market makers will be helpful to understand.
  
  **Tags:** machine-learning, data-preprocessing
  > **Comments:**
  > **username_1**: ML models and pipeline designs are highly context specific, thus it's better for one question to focus on one use case. If you have other concerned cases in mind, you'd better ask a new question per SE site rules.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-08-08. Score: 1
  
  Since handling post-trade compliance in financial applications involves monitoring and analyzing large volumes of transaction data to ensure adherence to regulatory requirements, this task often requires sophisticated and robust ML models capable of detecting rare and various complex anomaly patterns, like the ML use cases of credit card fraud detection. Bear in mind training data for these types of problems are often technically challenging due to several special issues such as class imbalanced data which often needs special calibration or loss reweighting schemes for real world production deployment as discussed in a recent [post](https://ai.stackexchange.com/questions/46373/dealing-with-likely-class-imbalance-for-unlabeled-test-data/).
  
  As a start you could first read the brief summary paper of Tiwari et al. (2021) [*"Credit Card Fraud Detection using Machine Learning: A Study"*](https://arxiv.org/abs/2108.10005v1) for some general but important design ideas of your own ML pipelines and models since they're very similar use cases of ML.
  
  > In this paper, we present a comprehensive review of various methods used to detect credit card frauds. These methodologies include HMM, Decision Trees, Logistic Regression, Support Vector Machines (SVM), Genetic algorithm, Neural Networks, Random Forests, Bayesian Belief Network... The study shows that the Fraud Bayesian Network Classifier followed by probability threshold is more beneficial than
  > Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machines and Decision Trees on the PagSeguro dataset when precision, recall and economic efficiency are taken into account. Bayesian Learning when taken with Dempster-Shafer Theory resulted in 98% True Positives and less than 10% False Positives... When dataset is highly imbalanced and independent of the rate of frauds Bagging Ensemble Classifier is one of the most stable approaches and has a high fraud catching rate... A lot of financial resources were saved when Cost-Sensitive Decision Tree approach was used.
  
  In summary it's very hard to choose *the* best ML model for such use cases as each instance may have its own characteristic. For example, for a person with a long enough personal transaction history HMM is well suited for anomaly detection, but some people may just start their transactions without any history. You must consider your use case's own characteristic to choose your model in addition to some common knowledge similar to your problem.
  
  
  ---
  
  