Business case: a new project with new transactions has started. How would you set up monitoring for suspicious transactions if you know nothing about this project?

To set up monitoring of suspicious transactions without prior knowledge of the project, it is advisable to start with basic data analysis:

1. Data collection: gather all available transaction data — time, amount, type, source, recipient, etc.
2. Distribution analysis: study the distribution of key parameters (e.g., transaction amounts) — identify means, medians, outliers.
3. Anomaly detection: use simple statistical methods (e.g., z-score, interquartile range) to identify transactions that significantly deviate from the norm.
4. Domain-based rules: if any information about the business is available, establish basic rules (e.g., amount above a certain threshold, transactions during non-working hours).
5. Visualization: create time series plots, histograms, to visually detect unusual patterns.

An example of a simple rule in Python to identify large transactions:

import pandas as pd

data = pd.read_csv('transactions.csv')
threshold = data['amount'].mean() + 3 * data['amount'].std()
suspicious = data[data['amount'] > threshold]
print(suspicious)

Thus, step by step, you can build a basic monitoring system and refine it as you gain more knowledge about the project.