Python webscraping for lists of company names and details using BeautifulSoup
It seems like you are creating a kind of a crawler. To improve on performance, you can use a Session object. It will utilize the underlying TCP connection, making the script run faster.

You can also use BeautifulSoup's SoupStrainer to only parse the listing tag. This way, your crawler won't have to go through each and every line of the HTML document.

Additionally on perfomance, you can use the lxml parser which happens to be faster than html.parser.

You seem to be using the base link quite often, move it into a constant and add href tags to it as needed.

As an example, the way you are filling up your finlist can be written as such.

import requests
from bs4 import BeautifulSoup, SoupStrainer

URL = 'https://www.sccci.org.sg'
session = requests.Session()

response = session.get(URL + '/en/directory/corporate/members?ind=150')
strainer = SoupStrainer(class_='listing')
soup = BeautifulSoup(response.content, 'lxml', parse_only=strainer)
listing_links = [URL + link['href'] for link in soup.select('ol[start] a[href]')]


Note that I have used all of the above and CSS selectors resulting in reducing the amount of for loops to just one. Populating finlist takes about 2 seconds on my machine.

Staying on finlist, it's not a particularly descriptive variable name. Perhaps something like listing_links would be better?

I can't go through the rest of the crawler as I am short on time, but for file operations, use the with statement. It will ensure the file is closed, even if an error occurs.
Example usage:

foo = '1234'
with open('bar.txt', 'w') as f:
    f.write(foo)