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Python Regex: The 5 Exclusive Examples

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 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result = re.search(pattern, text) if result:     print("Pattern found:", result.group()) Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if re.search(pattern, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the
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5 Key Ideas on SAS Banking Analytics

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SAS is providing solutions for banking. Getting away with financial crime just got harder. The latest SAS Financial Crimes Suite arms institutions to detect potential suspicious activity more efficiently than ever.
A new customer due diligence solution within the suite more accurately detects changes in a customer’s risk profile. Enhanced anti-money laundering and case management capabilities also make it easier to have a complete view of threats across an institution’s financial crimes investigation unit.

“A comprehensive view of potential threats will help in efforts to thwart criminals from successful attempts of hiding illicit funds,” says James Wester, global payments research director at IDC Financial Insights.

 “A technology infrastructure with customer risk rating and high-performance analytics will help speed detection and investigation in all channels.”.

SAS Analytics Suite for Banking Crimes

  1. Today’s rigorous regulatory environment requires banks to move quickly with confidence. SAS Financial Crimes Suite uses a visual scenario designer to recommend optimal detection models. The designer instantly assesses the impact of potential scenarios and risk-rating changes.
  2. In-memory architecture speeds analysis of real-time testing environments, reducing guesswork through improved model efficiency. 
  3. To identify potential money launderers and people funneling money to terrorists, institutions must constantly assess customer activity. The SAS Customer Due Diligence does this by weighing all customer data to set baseline expectations. 
  4. Data management features easily integrate key customer attributes from external sources and detect incriminating relationships. 
  5. The regulatory reporting interface controls both workflow and investigations. Context-aware analytics intercept and assess events for possible risk. The resulting baseline customer score can be automatically updated with a new risk rating based on behavior changes
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